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The Mathematics of (Hacking) Passwords

At one time or another, we have all been frustrated by trying to set a password, only to have it rejected as too weak. We are also told to change our choices regularly. Obviously such measures add safety, but how exactly?

I will explain the mathematical rationale for some standard advice, including clarifying why six characters are not enough for a good password and why you should never use only lowercase letters. I will also explain how hackers can uncover passwords even when stolen data sets lack them.

ChOose#W!sely@*

Here is the logic behind setting hack-resistant passwords. When you are asked to create a password of a certain length and combination of elements, your choice will fit into the realm of all unique options that conform to that rule—into the “space” of possibilities. For example, if you were told to use six lowercase letters—such as, afzjxd, auntie, secret, wwwwww—the space would contain 266, or 308,915,776, possibilities. In other words, there are 26 possible choices for the first letter, 26 possible choices for the second, and so forth. These choices are independent: you do not have to use different letters, so the size of the password space is the product of the possibilities, or 26 x 26 x 26 x 26 x 26 x 26 = 266.

If you are told to select a 12-character password that can include uppercase and lowercase letters, the 10 digits and 10 symbols (say, !, @, #, $, %, ^, &, ?, / and +), you would have 72 possibilities for each of the 12 characters of the password. The size of the possibility space would then be 7212 (19,408,409,961,765,342,806,016, or close to 19 x 1021).

That is more than 62 trillion times the size of the first space. A computer running through all the possibilities for your 12-character password one by one would take 62 trillion times longer. If your computer spent a second visiting the six-character space, it would have to devote two million years to examining each of the passwords in the 12-character space. The multitude of possibilities makes it impractical for a hacker to carry out a plan of attack that might have been feasible for the six-character space.

Calculating the size of these spaces by computer usually involves counting the number of binary digits in the number of possibilities. That number, N, is derived from this formula: 1 + integer(log2(N)). In the formula, the value of log2(N) is a real number with many decimal places, such as log2(266) = 28.202638…. The “integer” in the formula indicates that the decimal portion of that log value is omitted, rounding down to a whole number—as in integer(28.202638… 28). For the example of six lowercase letters above, the computation results in 29 bits; for the more complex, 12-character example, it is 75 bits. (Mathematicians refer to the possibility spaces as having entropy of 29 and 75 bits, respectively.) The French National Cybersecurity Agency (ANSSI) recommends spaces having a minimum of 100 bits when it comes to passwords or secret keys for encryption systems that absolutely must be secure. Encryption involves representing data in a way that ensures it cannot be retrieved unless a recipient has a secret code-breaking key. In fact, the agency recommends a possibility space of 128 bits to guarantee security for several years. It considers 64 bits to be very small (very weak); 64 to 80 bits to be small; and 80 to 100 bits to be medium (moderately strong).

Moore’s law (which says that the computer-processing power available at a certain price doubles roughly every two years) explains why a relatively weak password will not suffice for long-term use: over time computers using brute force can find passwords faster. Although the pace of Moore’s law appears to be decreasing, it is wise to take it into account for passwords that you hope will remain secure for a long time.

For a truly strong password as defined by ANSSI, you would need, say, a sequence of 16 characters, each taken from a set of 200 characters. This would make a 123-bit space, which would render the password close to impossible to memorize. Therefore, system designers are generally less demanding and accept low- or medium-strength passwords. They insist on long ones only when the passwords are automatically generated by the system, and users do not have to remember them.

There are other ways to guard against password cracking. The simplest is well known and used by credit cards: after three unsuccessful attempts, access is blocked. Alternative ideas have also been suggested, such as doubling the waiting time after each successive failed attempt but allowing the system to reset after a long period, such as 24 hours. These methods, however, are ineffective when an attacker is able to access the system without being detected or if the system cannot be configured to interrupt and disable failed attempts.

How Long Does It Take to Search All Possible Passwords?

For a password to be difficult to crack, it should be chosen randomly from a large set, or “space,” of possibilities. The size, T, of the possibility space is based on the length, A, of the list of valid characters in the password and the number of characters, N, in the password. The size of this space (T= AN) may vary considerably.

Each of the following examples specifies values of A, N, T and the number of hours, D, that hackers would have to spend to try every permutation of characters one by one.X is the number of years that will have to pass before the space can be checked in less than one hour, assuming that Moore’s law (the doubling of computing capacity every two years) remains valid. I also assume that in 2019, a computer can explore a billion possibilities per second. I represent this set of assumptions with the following three relationships and consider five possibilities based on values of A and N:

Relationships

T = AN
D = T/(109 × 3,600)
X = 2 log2[T/(109 × 3,600)]

Results

_________________________________
If A = 26 and N = 6, then T = 308,915,776
D = 0.0000858 computing hour
X = 0; it is already possible to crack all passwords in the space in under an hour
_________________________________
If A = 26 and N = 12, then T = 9.5 × 1016
D = 26,508 computing hours
X = 29 years before passwords can be cracked in under an hour
_________________________________

If A = 100 and N = 10, then T = 1020
D = 27,777,777 computing hours
X = 49 years before passwords can be cracked in under an hour
_________________________________
If A = 100 and N = 15, then T = 1030
D= 2.7 × 1017 computing hours
X= 115 years before passwords can be cracked in under an hour
________________________________
If A = 200 and N = 20, then T = 1.05 × 1046
D = 2.7 × 1033 computing hours
X = 222 years before passwords can be cracked in under an hour

Weaponizing Dictionaries and Other Hacker Tricks

Quite often an attacker succeeds in obtaining encrypted passwords or password “fingerprints” (which I will discuss more fully later) from a system. If the hack has not been detected, the interloper may have days or even weeks to attempt to derive the actual passwords.

To understand the subtle processes exploited in such cases, take another look at the possibility space. When I spoke earlier of bit size and password space (or entropy), I implicitly assumed that the user consistently chooses passwords at random. But typically the choice is not random: people tend to select a password they can remember (locomotive) rather than an arbitrary string of characters (xdichqewax).

This practice poses a serious problem for security because it makes passwords vulnerable to so-called dictionary attacks. Lists of commonly used passwords have been collected and classified according to how frequently they are used. Attackers attempt to crack passwords by going through these lists systematically. This method works remarkably well because, in the absence of specific constraints, people naturally choose simple words, surnames, first names and short sentences, which considerably limits the possibilities. In other words, the nonrandom selection of passwords essentially reduces possibility space, which decreases the average number of attempts needed to uncover a password.

Below are the first 25 entries in one of these password dictionaries, listed in order, starting with the most common one. (I took the examples from a database of five million passwords that was leaked in 2017 and analyzed by SplashData.)

1. 123456
2. password
3. 12345678
4. qwerty
5. 12345
6. 123456789
7. letmein
8. 1234567
9. football
10. iloveyou
11. admin
12. welcome
13. monkey
14. login
15. abc123
16. starwars
17. 123123
18. dragon
19. passw0rd
20. master
21. hello
22. freedom
23. whatever
24. qazwsx
25. trustno1

If you use password or iloveyou, you are not as clever as you thought! Of course, lists differ according to the country where they are collected and the Web sites involved; they also vary over time.

For four-digit passwords (for example, the PIN code of SIM cards on smartphones), the results are even less imaginative. In 2013, based on a collection of 3.4 million passwords each containing four digits, the DataGenetics Web site reported that the most commonly used four-digit sequence (representing 11 percent of choices) was 1234, followed by 1111 (6 percent) and 0000 (2 percent). The least-used four-digit password was 8068. Careful, though, this ranking may no longer be true now that the result has been published. The 8068 choice appeared only 25 times among the 3.4-million four-digit sequences in the database, which is much less than the 340 uses that would have occurred if each four-digit combination had been used with the same frequency. The first 20 series of four digits are: 1234; 1111; 0000; 1212; 7777; 1004; 2000; 4444; 2222; 6969; 9999; 3333; 5555; 6666; 1122; 1313; 8888; 4321; 2001; 1010.

Even without a password dictionary, using differences in frequency of letter use (or double letters) in a language makes it possible to plan an effective attack. Some attack methods also take into account that, to facilitate memorization, people may choose passwords that have a certain structure—such as A1=B2=C3, AwX2AwX2 or O0o.lli. (which I used for a long time)—or that are derived by combining several simple strings, such as password123 or johnABC0000. Exploiting such regularities makes it possible to for hackers to speed up detection.

Making Hash of Hackers

As the main text explains, instead of storing clients’ passwords, Internet servers store the “fingerprints” of these passwords: sequences of characters that are derived from the passwords. In the event of an attack, the use of fingerprints can make it is very difficult, if not impossible, for hackers to use what they find.

The transformation is achieved by using algorithms known as cryptographic hash functions. These are meticulously developed processes that transform a data file, F, however long it may be, into a sequence, h(F), called a fingerprint of F. For example, the hash function SHA256 transforms the phrase “Nice weather” into:

DB0436DB78280F3B45C2E09654522197D59EC98E7E64AEB967A2A19EF7C394A3
(64 hexadecimal, or base 16, characters, which is equivalent to 256 bits)

Changing a single character in the file completely alters its fingerprint. For example, if the first character of Nice weather is changed to lowercase (nice weather), the hash SHA256 will generate another fingerprint:

02C532E7418CD1B57961A1B090DB6EC37B3C58380AC0E6877F3B6155C974647E

You can do these calculations yourself and check them at https://passwordsgenerator.net/sha256-hash-generator or www.xorbin.com/tools/sha256-hash-calculator

Good hash functions produce fingerprints that are similar to those that would be obtained if the fingerprint sequence was uniformly chosen at random. In particular, for any possible random result (a sequence of 64 hexadecimal characters), it is impossible to find a data file F with this fingerprint in a reasonable amount of time.

There have been several generations of hash functions. The SHA0 and SHA1 generations are obsolete and are not recommended. The SHA2 generation, including SHA256, is considered secure. 

The Take-Home for Consumers

Taking all this into account, properly designed Web sites analyze the passwords proposed at the time of their creation and reject those that would be too easy to recover. It is irritating, but it’s for your own good.

The obvious conclusion for users is that they must choose their passwords randomly. Some software does provide a random password. Be aware, however, that such password-generating software may, deliberately or not, use a poor pseudo-random generator, in which case what it provides may be imperfect.

You can check whether any of your passwords has already been hacked by using a Web tool called Pwned Passwords (https://haveibeenpwned.com/Passwords). Its database includes more than 500 million passwords obtained after various attacks.

I tried e=mc2e=mc2, which I liked and believed to be secure, and received an unsettling response: “This password has been seen 114 times before.” Additional attempts show that it is difficult to come up with easy-to-memorize passwords that the database does not know. For example, aaaaaa appeared 395,299 times; a1b2c3d4, 113,550 times; abcdcba, 378 times; abczyx, 186 times; acegi, 117 times; clinton, 18,869 times; bush, 3,291 times; obama, 2,391 times; trump, 859 times.

It is still possible to be original. The Web site did not recognize the following six passwords, for example: eyahaled (my name spelled backward); bizzzzard; meaudepace and modeuxpass (two puns on the French for “password”); abcdef2019; passwaurde. Now that I’ve tried them, I wonder if the database will add them when it next updates. In that case, I won’t use them.

Advice for Web Sites

Web sites, too, follow various rules of thumb. The National Institute of Standards and Technology recently published a notice recommending the use of dictionaries to filter users’ password choices.

Among the rules that a good Web server designer absolutely must adhere to is, do not store plaintext lists of usernames and passwords on the computer used to operate the Web site.

The reason is obvious: hackers could access the computer containing this list, either because the site is poorly protected or because the system or processor contains a serious flaw unknown to anyone except the attackers (a so-called zero-day flaw), who can exploit it.

One alternative is to encrypt the passwords on the server: use a secret code that transforms them via an encryption key into what will appear to be random character sequences to anyone who does not possess the decryption key. This method works, but it has two disadvantages. First, it requires decrypting the stored password every time to compare it with the user’s entry, which is inconvenient. Second, and more seriously, the decryption necessary for this comparison requires storing the decryption key in the Web site computer’s memory. This key may therefore be detected by an attacker, which brings us back to the original problem.

A better way to store passwords is through what are called hash functions that produce “fingerprints.” For any data in a file—symbolized as F—a hash function generates a fingerprint. (The process is also called condensing or hashing.) The fingerprint—h(F)—is a fairly short word associated with F but produced in such a way that, in practice, it is impossible to deduce F from h(F). Hash functions are said to be one-way: getting from F to h(F) is easy; getting from h(F) to F is practically impossible. In addition, the hash functions used have the characteristic that even if it is possible for two data inputs, F and F', to have the same fingerprint (known as a collision), in practice for a given F, it is almost impossible to find an F' with a fingerprint identical to F.

Using such hash functions allows passwords to be securely stored on a computer. Instead of storing the list of paired usernames and passwords, the server stores only the list of username/fingerprint pairs.

When a user wishes to connect, the server will read the individual’s password, compute the fingerprint and determine whether it corresponds to the list of stored username/fingerprint pairs associated with that username. That maneuver frustrates hackers because even if they have managed to access the list, they will be unable to derive the users’ passwords, inasmuch as it is practically impossible to go from fingerprint to password. Nor can they generate another password with an identical fingerprint to fool the server because it is practically impossible to create collisions.

Still, no approach is foolproof, as is highlighted by frequent reports of the hacking of major sites. In 2016, for example, data from a billion accounts were stolen from Yahoo!

For added safety, a method known as salting is sometimes used to further impede hackers from exploiting stolen lists of username/fingerprint pairs. Salting is the addition of a unique random string of characters to each password. It ensures that even if two users employ the same password, the stored fingerprints will differ. The list on the server will contain three components for each user: username, fingerprint derived after salt was added to the password, and the salt itself. When the server checks the password entered by a user, it adds the salt, computes the fingerprint and compares the result with its database.

Even when user passwords are weak, this method considerably complicates the hacker’s work. Without salting, a hacker can compute all the fingerprints in a dictionary and see those in the stolen data; all the passwords in the hacker’s dictionary can be identified. With salting, for every salt used, the hacker must compute the salted fingerprints of all the passwords in the hacker’s dictionary. For a set of 1,000 users, this multiplies by 1,000 the computations required to use the hacker’s dictionary.

Survival of the Fittest

It goes without saying that hackers have their own ways of fighting back. They face a dilemma, though: their simplest options either take a lot of computing power or a lot of memory. Often neither option is viable. There is, however, a compromise approach known as the rainbow table method (see “Rainbow Tables Help Hackers”).

In the age of the Internet, supercomputers and computer networks, the science of password setting and cracking continues to evolve—as does the relentless struggle between those who strive to protect passwords and those who are determined to steal, and potentially abuse, them.

Rainbow Tables Help Hackers

Say you are a hacker looking to exploit data that you have acquired. These data consist of username/fingerprint pairs, and you know the hash function (see “Making Hash of Hackers”). The password is contained in the possibility space of strings of 12 lowercase letters, which corresponds to 56 bits of information and 2612 (9.54 x 1016) possible passwords.

At least two strong approaches are open to you:

Method 1. You scroll through the entire space of passwords. You calculate the fingerprint, h(P), for each password, checking to see whether it appears in the stolen data. You do not need a lot of memory, because prior results are deleted with each new attempt, although you do, of course, have to keep track of the possibilities that have been tested.

Scrolling through all the possible passwords in this way takes a long time. If your computer runs a billion tests per second, you will need 2612/(109 x 3,600 x 24) days (1,104 days), or about three years to complete the task. The feat is not impossible; if you happen to have a computer network of 1,000 machines, one day will suffice. It is not feasible, however, to repeat such a calculation every time you wish to test additional data, such as if you obtain a new set of username/fingerprint pairs. (Because you have not saved the results of your computations, you would need an additional 1,104 days to process the new information.)

Method 2. You say to yourself, “I’ll compute the fingerprints of all possible passwords, which will take time, and I’ll store the resulting fingerprints in a big table. Then I’ll have to find only a password fingerprint in the table to identify the corresponding password in the stolen data.”

You will need (9.54 x 1016) x (12 + 32) bytes of memory because the task requires 12 bytes for the password and 32 bytes for the fingerprint if the fingerprint contains 256 bits (assuming an SHA256 function). That’s 4.2 x 1018 bytes, or 4.2 million hard disks with a capacity of one terabyte.

This memory requirement is too large. Method 2 is no more feasible than method 1. Method 1 requires too many computations, and method 2 requires too much memory. Both cases are problematic: either each new password takes too long to compute, or precomputing all possibilities and storing all the results is too large a task.

Is there some compromise that requires less computing power than method 1 and less memory than required for method 2? Indeed, there is. In 1980 Martin Hellman of Stanford University suggested an approach that was improved in 2003 by Philippe Oechslin of the Swiss Federal Institute of Technology in Lausanne and further refined more recently by Gildas Avoine of the National Institute of Applied Sciences of Rennes (INSA Rennes) in France. It demands less computing power than method 1 in exchange for using a little more memory.

The Beauty of the Rainbow

Here is how it works: First, we need a function R that transforms a fingerprint h(P) into a new password R(h(P)). One might, for instance, consider fingerprints as numbers written in the binary numeral system and consider passwords as numbers written in the K numeral system, where K is the number of allowable symbols for passwords. Then the function R converts data from the binary numeral system to the K numeral system. For every fingerprint h(P), it computes a new password R(h(P)).

Now, with this function R, we can precompute data tables called rainbow tables (so named perhaps because of the multicolored way these tables are depicted).

To generate a data point in this table, we start from a possible password P0, compute its fingerprint, h(P0) and then compute a new possible password R(h(P0)), which becomes P1. Next, we continue this process from P1. Without storing anything other than P0, we compute the sequence P1, P2,... until the fingerprint starts with 20 zeros; that fingerprint is designated h(Pn). Such a fingerprint occurs only once in about 1,000,000 fingerprints because the result of a hash function is similar to result of a uniform random draw, and 220 is roughly equal to 1,000,000. The password/fingerprint pair [P0, h(Pn)], containing the fingerprint that starts with 20 zeros is then stored in the table.

A very large number of pairs of this type are computed. Each password/fingerprint pair [P0, h(Pn)] represents the sequence of passwords P0, P1,… Pn and their fingerprints, but the table does not store those intermediate calculations. The table thus lists many password/fingerprint pairs and represents many more (the intermediates, such as P1 and P2, that can be derived from the listed pairs). But, of course, there may be gaps: some passwords may be absent from all the chains of calculations.

For a good database with almost no gaps, the memory needed to store the calculated pairs is a million times smaller than that needed for method 2, as described earlier. That is less than four one-terabyte hard disks. Easy. Also, as will be seen, using the table to derive passwords from stolen fingerprints is quite doable.

Let us see how the data stored on the hard disks makes it possible to determine a password in a given space in just a few seconds. Assume that there are no gaps; precomputation of the table takes into account all the passwords of a designated type—for example, 12-character passwords taken from the 26 letters of the alphabet.

A fingerprint f0 in a stolen data set can be used to reveal the associated password in the following way. Calculate h(R(f0)) to arrive at a new fingerprint, f1, then calculate h(R(f1)) to get f2, and so on, until you get to a fingerprint that begins with 20 zeros: fm. Then check the table to see which original password, P0, the fingerprint fm is associated with. Based on P0, calculate the passwords and fingerprints h1, h2,... that follow until you inevitably generate the original fingerprint f0, designated hk. The password you are looking for is the one that gave rise to hk—in other words, R(hk – 1), which is one step earlier in the chain of calculations.

The computation time required is what it takes to look for fm in the table plus the time needed to compute the sequence of fingerprints from the associated password (h1, h2,…, hk)—which is about a million times shorter than the time needed to compute the table itself. In other words, the time needed is quite reasonable.

Thus, doing a (very long) precomputation and storing only part of the results makes it possible to retrieve any password with a known fingerprint in a reasonable amount of time.

The sequences below represent separate chains of calculations leading from passwords (Mo, No,..., Qo) to fingerprints and other passwords, until the desired fingerprint (and thus the password that precedes it) pops out.(The long dotted line represents may other lines similar to the top two.)

To summarize, by knowing the beginning and end of each chain of computations (the only things that are stored during precomputation), a hacker can retrieve any password from a fingerprint. In somewhat simplistic terms, starting from a stolen fingerprint—call it fingerprint X—a hacker would apply the R and h functions repeatedly, calculating a series of passwords and fingerprints until reaching a fingerprint with 20 zeros in front of it. The hacker would then look up that final fingerprint in the table (Fingerprint C in the example below) and identify its corresponding password (Password C).

Sample Table Excerpt
Password A—Fingerprint A
Password B—Fingerprint B
Password CFingerprint C
Password D—fingerprint D

Next, the hacker would apply the h and R functions again, beginning with the identified password, continuing on until one of the resulting fingerprints in the chain matches the stolen fingerprint:

Sample Calculation

Password C → fingerprint 1 → password 2-- → fingerprint 2 → password 3…. → password 22-- →fingerprint 23 [a match to fingerprint X!]

The match (fingerprint 23) would indicate that the previous password (password 22), from which the fingerprint was derived, is the one linked to the stolen fingerprint.

Many computations must be done to establish the first and last column of the rainbow table. By storing only the data in these two columns and by recomputing the chain, hackers can identify any password from its fingerprint.

Источник: https://www.scientificamerican.com/article/the-mathematics-of-hacking-passwords/

Sketch 70.3 Crack + License Key Full Free Download 2021{Latest}

 

Sketch 56.3 Crack

Sketch 70.3 Crack is one of the most popular applications for designers and helps them out to turn their raw ideas into real designs. It comes with the support for many useful plugins and comes with powerful features to present your best work. This program has the ability to work with other tools like blurring, gradients, shadows, and blending. It offers a strong and powerful text rendering engine which allows you to make complex shapes by grouping various objects. You can export and import in different documents formats including .png, .jpg, .gif, .eps, .pdf, and .svg.

The interface is easy to use, and the application even has a dark mode option which takes the stress off of your eyes and lets you direct your full focus to your design. Sketch Torrent can work with complex documents and can run on Metal to provide you with improved performance. Working with this software, you get pixel precision which can let you control every aspect of your design. You can also export presets which can save you a lot of time and gives you a good start.

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Sketch 70.3 Crack Mac is a revolutionary and contemporary have a look at vector drawing. Its deliberately minimalist design relies upon a drawing house of limitless measurement and layers, free from palettes, panels, menus, home windows, and controls. Sketch Activation Key Although easy to make use of, it gives highly effective vector drawing and textual content instruments like excellent Boolean operations, symbols, and highly effective rulers, guides, and grids.

Additionally, you can use it to create fluid layouts to make the margins and paddings of your elements more manageable. You can also group the layers in your project to make resizing more convenient. Sketch Crack 2021 contains professional-grade templates for different projects such as iOS UI Design, iOS App Icon, and some others. In Short, this is a handy graphical software that helps you to work with many projects by utilizing efficient tools.

Sketch 56.3 Crack

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  • You can group your content with a few clicks and create lists, mockup messages and series of rows. To accomplish this task, you will have to add on Craft plugin.
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  • With Sketch License Key, you can create beautiful responsive websites, and you can even add form elements to your web page. To avail this feature, you will have to integrate the Launchpad plugin by Anima.
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  • When you are designing with it, you can determine for each layer to either have a fixed size or change with the layout. You can pick the corner where you will like to pinch the respective segment too. This feature lets you change the dimensions of the screen more easily.

System Requirements:

  • Intel, 64-bit processor.
  • macOS 10.13.four or later.

 

What’s News?

  • Included a new Artboard preset size for Galaxy S10 as well as updated iPad models
  • Toggle the visibility for multiple overrides on a specific symbol master
  • Improved the alignment of icons ToolBars button for better results
  • Now, you have the ability to show the system Color Panel
  • The Smart Guides will appear when you insert new symbol instances
  • Also, Snap lines display more consistently compare to the previous version when moving layers with a keyboard.
  • Fix crash which occurs when localized font names did not find or missing from an installed font.
  • Also, resolved the problem when some documents would not be able to open or render as the user expected
  • Snapping has many enhancements in working as well as add a brand new attractive look for customizable guide color.
  • This version helps you to easily toggle in between Ligh Mode and Dark Mode as you wish without the worrying about your system setting
  • Improved the working of snapping when dragging a layer in or out of an artboard
Sketch Crack Serial Key: DVEFHS-RUFYGB-RFGCVR-RUYGUW WIUWR-FBVRVR-RUVBNC-EUHFRBR ESFGCV-EADGSXC-SFHC-ASFHXB SFHX-WRYSFG-WRYFGVB-RETDHG Sketch Crack License Key: DSBSDR-YRGBC-RUYGFNE-RYFUNC DBBBDR-RUHBET-UGYHNC-RFYRHU QEWRF-ESFG-QETRSG-RWYSHFXGBV WRYSFG-RWYSFH-WRSHFD-5WUTEDGH Sketch Crack 2021 Key: HBEJGR-RYGFN-TYUVBE-YRGFHJ BNEYE-YGNUTT-HGJRIV-RGHIRR WERYF-RSYFH-SRYHFV-SRHDVB ARSGFV-SRYFHV-SRYHF-SRYHFD

How to Crack?

  • First of all, Sketch Crack plus setup from below button

  • You need to extract download files and then run the setup

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Sketch 70.4 Crack + Serial Key (100% Working) Free Download 2021

Sketch 70.4 Crack is a software program devolved for Mac programs. Particularly, Sketches will allow you to plan and edit all forms of ideas. The primary actions encompass controls, dishes, home windows, and menus. Sketch 52 Crack is especially used for the manufacturing of graphical sketches and textual content instruments. On the alternative hand, it performs a number of Boolean capabilities, symbols, and insurance policies. The vary of designers and professional designs is broadly distributed by way of this software program program. Lastly, many instruments are constructed right here to create new symbols and elements.

It minimal onboarding inside the platform. It is fantastic for the community of users who have made lots of tutorials for getting started and what to anticipate. In general, it truly is a far better tool for program layout than Photoshop or Photoshop for MAC users. Additionally, It has good promise in the realm of design. Our line of work was too bothersome for as long, and we have lost a lot of time with Photoshop, and so on, so it is excellent to find innovation so genuinely revolutionary. Should you take some opportunity to check and find out with these programs, you are going to appreciate the benefits in the long term.

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Sketch 70.4 Crack is one of the most popular applications for designers and helps them out to turn their raw ideas into real designs. It comes with the support for many useful plugins and comes with powerful features to present your best work. This program has the ability to work with other tools like blurring, gradients, shadows, and blending. It offers a strong and powerful text rendering engine which allows you to make complex shapes by grouping various objects. You can export and import in different documents formats including .png, .jpg, .gif, .eps, .pdf, and .svg.

The interface is easy to use, and the application even has a dark mode option which takes the stress off of your eyes and lets you direct your full focus to your design. Sketch Torrent can work with complex documents and can run on Metal to provide you with improved performance. Working with this software, you get pixel precision which can let you control every aspect of your design. You can also export presets which can save you a lot of time and gives you a good start.

Additionally, you can use it to create fluid layouts to make the margins and paddings of your elements more manageable. You can also group the layers in your project to make resizing more convenient. Sketch Crack 2019 contains professional grade templates for different projects such as iOS UI Design, iOS App Icon, and some others. In Short, this is a handy graphical software that helps you to work with many projects by utilizing efficient tools.

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Vast Amount of Resources for Design:

  • With the InVision Craft plugin, you will never have to rely on dummy text or avatars and can choose from an extensive library of photos to add more life to your design. You also get to insert dummy text on any subject or names residing in your workflow.

Multiply Your Content:

  • You can group your content with a few clicks and create lists, mockup messages and series of rows. To accomplish this task, you will have to add on Craft plugin.

Create Prototypes:

  • You can export your design to a plugin called Marvel to quickly create prototypes that are responsive. You can also share the updated version of your project with anyone you want via an SMS. The SMS will contain the link that will enable the download of the prototype on any device.

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  • With Sketch 55.1 License Key, you can create beautiful responsive websites, and you can even add form elements to your web page. To avail this feature, you will have to integrate the Launchpad plugin by Anima.

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  • It lets you add mathematical quotations into each input and perform calculations in the input field. You can use this feature to make more progressive calculations to make your design look more professional.

Make Your Design More Responsive:

  • When you are designing with it, you can determine for each layer to either have a fixed size or change with the layout. You can pick the corner where you will like to pinch the respective segment too. This feature lets you change the dimensions of the screen more easily.

Sketch Advantages:

  • Provide a complete set of tools for graphic design.
  • Edit Each detail in the design.
  • Every pixel is accurate.
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  • For new users, presets are available.
  • Export code.
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What’s new in Sketch 70.4 Crack?

  • The Smart Guides will appear when you insert new symbol instances
  • Also, Snap lines display more consistently compare to the previous version when moving layers with a keyboard.
  • Fix crash which occurs when localized font names did not find or missing from an installed font.
  • Also, resolved the problem when some documents would not be able to open or render as the user expected
  • Snapping has many enhancements in working as well as add a brand new attractive look for customizable guide color.
  • This version helps you to easily toggle in between Ligh Mode and dark Mode as you wish without the worrying about your system setting
  • Improved the working of snapping when dragging a layer in or out of an artboard
  • Included a new Artboard preset size for Galaxy S10 as well as updated iPad models
  • Toggle the visibility for multiple overrides on a specific symbol master
  • Improved the alignment of icons ToolBars button for better results
  • Now, you have the ability to show the system Color Panel
  • Fix the issue which occurs when you try to rotate multiple layers with the help of Touch Bar
  • Also, fix the size of Pixel 3 Artboard preset
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Previous Updates:

  • The new version has a bug fixed that a Library Styles may have been displayed as the Default Style in spite of being named correctly to show the relation to their concerned Libraries.
  • The latest version comes with many improvements and also fixed many issues
  • Fixed an issue where documents contain specific path not able to open
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  • Sketch 55.1 wants Mac OS X El Capitan (10.11.2) or newer.
  • A minimum of 1 GB of the free house. A robust Mac with extra RAM will certainly assist.
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Sketch Serial Key: DVEFHS-RUFYGB-RFGCVR-RUYGUW WIUWR-FBVRVR-RUVBNC-EUHFRBR ESFGCV-EADGSXC-SFHC-ASFHXB SFHX-WRYSFG-WRYFGVB-RETDHG Sketch License Key: DSBSDR-YRGBC-RUYGFNE-RYFUNC DBBBDR-RUHBET-UGYHNC-RFYRHU QEWRF-ESFG-QETRSG-RWYSHFXGBV WRYSFG-RWYSFH-WRSHFD-5WUTEDGH Sketch 2021 Key: HBEJGR-RYGFN-TYUVBE-YRGFHJ VBNEYE-YGNUTT-HGJRIV-RGHIRR WERYF-RSYFH-SRYHFV-SRHDVB ARSGFV-SRYFHV-SRYHF-SRYHFD

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  • First of all, Sketch Crack plus setup from below button
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The Mathematics of (Hacking) Passwords

At one time or another, we have all been frustrated by trying to set a password, only to have it rejected as too weak. We are also told to change our choices regularly. Obviously such measures add safety, but how exactly?

I will explain the mathematical rationale for some standard advice, including clarifying why six characters are not enough for a good password and why you should never use only lowercase letters. I will also explain how hackers can uncover passwords even when stolen data sets lack them.

ChOose#W!sely@*

Here is the logic behind setting hack-resistant passwords. When you are asked to create a password of a certain sketch 49 crack - Crack Key For U and combination of elements, your choice will fit into the realm of all unique options that conform to that rule—into the “space” of possibilities. For example, if you were told to use six lowercase letters—such as, afzjxd, auntie, secret, wwwwww—the space would contain 266, or 308,915,776, possibilities. In other words, there are 26 possible choices for the first letter, 26 possible choices for the second, and so forth. These choices are independent: you do not have to use different letters, so the size of the password space is the product of the possibilities, or 26 x 26 x 26 x 26 x 26 x 26 = 266.

If you are told to select a 12-character password that can include uppercase and lowercase letters, the 10 digits and 10 symbols (say, !, @, #, sketch 49 crack - Crack Key For U, %, ^, &, ?, / and +), you would have 72 possibilities for each of the 12 characters of the password. The size of the possibility space would then be 7212 (19,408,409,961,765,342,806,016, or close to 19 x 1021).

That is more than 62 trillion times the size of the first space. A computer running through all the possibilities for your 12-character password one by one would take 62 trillion times longer. If your computer spent a second visiting the six-character space, it would have to devote two million years to examining each of the passwords in the 12-character space. The multitude of possibilities makes it impractical for a hacker to carry out a plan of attack that might have been feasible for the six-character space.

Calculating the size of these spaces by computer usually involves counting the number of binary digits in the number of possibilities. That number, N, is derived from this formula: 1 + integer(log2(N)). In the formula, the value of log2(N) is a real number with many decimal places, such as log2(266) = 28.202638&hellip. The “integer” in the formula indicates that the decimal portion of that log value is omitted, rounding down to a whole number—as in integer(28.202638… 28). For the example of six lowercase letters above, the computation results in 29 bits; for the more complex, 12-character example, it is 75 bits. (Mathematicians refer to the possibility spaces as having entropy of 29 and 75 bits, respectively.) The French National Cybersecurity Agency (ANSSI) recommends spaces having a minimum of 100 bits when it comes to passwords or secret keys for encryption systems that absolutely must be secure. Encryption involves representing data in a way that ensures it cannot be retrieved unless a recipient has a secret code-breaking key. In fact, the agency recommends a possibility space of 128 bits to guarantee security for several years. It considers 64 bits to be very small (very weak); 64 to 80 bits to be small; and 80 to 100 bits to be medium (moderately strong).

Moore’s law (which says that the computer-processing power available at a certain price doubles roughly every two years) explains why a relatively weak password will not suffice for long-term use: over time computers using brute force can find passwords faster. Although the pace of Moore’s law appears to be decreasing, it is wise to take it into account for passwords that you hope will remain secure for a long time.

For a truly strong password as defined by ANSSI, you would need, say, a sequence of 16 characters, each taken from a set of 200 characters. This would make a 123-bit space, which would render the password close to impossible to memorize. Therefore, system designers are generally less demanding and accept low- or medium-strength passwords. They insist on long ones only when the passwords are automatically generated by the system, and users do not have to remember them.

There are other ways to guard against password cracking. The simplest is well known and used by credit cards: after three unsuccessful attempts, access is blocked. Alternative ideas have also been suggested, such as doubling the waiting time after each successive failed attempt but allowing the system to reset after a long period, such as 24 hours. These methods, however, are ineffective when an attacker is able to access the system without being detected or if the system cannot be configured to interrupt and disable failed attempts.

How Long Does It Take to Search All Possible Passwords?

For a password to be difficult to crack, it should be chosen randomly from a large set, or “space,” of possibilities. The size, T, of the possibility space is based on the length, A, of the list of valid characters in the password and the number of characters, N, in the password. The size of this space (T= AN) may vary considerably.

Each of the following examples specifies values of A, N, T and the number of hours, D, that hackers would have to spend to try every permutation of characters one by one.X is the number of years that will have to pass before the space can be checked in less than one hour, assuming that Moore’s law (the doubling of computing capacity every two years) remains valid. I also assume that in 2019, a computer can explore a billion possibilities per second. I represent this set of assumptions with the following three relationships and consider five possibilities based on values of A and N:

Relationships

T = AN
D = T/(109 × 3,600)
X = 2 log2[T/(109 × 3,600)]

Results

_________________________________
If A = 26 and N = 6, then T = 308,915,776
D = 0.0000858 computing hour
X = 0; it is already possible to crack all passwords in the space in under an hour
_________________________________
If A = 26 and N = 12, then T = 9.5 × 1016
D = 26,508 computing hours
X = 29 years before passwords can be cracked in under an hour
_________________________________

If A = 100 and N = 10, then T = 1020
D = 27,777,777 computing hours
X = 49 years before passwords can be cracked in under an hour
_________________________________
If A = 100 and N = 15, then T = 1030
D= 2.7 × 1017 computing hours
X= 115 years before passwords can be cracked in under an hour
________________________________
If A = 200 and N = 20, then T = 1.05 × 1046
D = 2.7 × 1033 computing hours
X = 222 years before passwords can avast secureline vpn apk cracked in under an hour

Weaponizing Dictionaries and Other Hacker Tricks

Quite often an attacker succeeds in obtaining encrypted passwords or password “fingerprints” (which I will discuss more fully later) from a system. If the hack has not been detected, the interloper may have days or even weeks to attempt to derive the actual passwords.

To understand the subtle processes exploited in such cases, take another look at the possibility space. When I spoke earlier of bit size and password space (or entropy), I implicitly assumed that the user consistently chooses passwords at random. But typically the choice is not random: people tend to select a password they can remember (locomotive) rather than an arbitrary string of characters (xdichqewax).

This practice poses a serious problem for security because it makes passwords vulnerable to so-called dictionary attacks. Lists of commonly used passwords have been collected and classified according to how frequently they are used. Attackers attempt to crack passwords by going through these lists systematically. This method works remarkably well because, in the absence of specific constraints, people naturally choose simple words, surnames, first names and short sentences, which considerably limits the possibilities. In other words, the nonrandom selection of passwords essentially reduces possibility space, which decreases the average number of attempts needed to uncover a password.

Below are the first 25 entries in one of these password dictionaries, listed in order, starting with the most common one. (I took the examples from a database of five million passwords that was leaked in 2017 and analyzed by SplashData.)

1. 123456
2. password
3. 12345678
4. qwerty
5. 12345
6. 123456789
7. letmein
8. 1234567
9. football
10. iloveyou
11. admin
12. welcome
13. monkey
14. login
15. abc123
16. starwars
17. 123123
18. dragon
19. passw0rd
20. master
21. hello
22. freedom
23. whatever
24. qazwsx
25. trustno1

If you use password or iloveyou, you are not as clever as you thought! Of course, lists differ according to the country where they are collected and the Web sites involved; they also vary over time.

For four-digit passwords (for example, the PIN code of SIM cards on smartphones), the results are even less imaginative. In 2013, based on a collection of 3.4 million passwords each containing four digits, the DataGenetics Web site reported that the most commonly used four-digit sequence (representing 11 percent of choices) was 1234, followed by 1111 (6 percent) and 0000 (2 percent). The least-used four-digit password was 8068. Careful, though, this ranking may no longer be true now that the result has been published. The 8068 choice appeared only 25 times among the 3.4-million four-digit sequences in the database, which is much less than the 340 uses that would have occurred if each four-digit combination had been used with the same frequency. The first 20 series of four digits are: 1234; 1111; 0000; 1212; 7777; 1004; 2000; 4444; 2222; 6969; 9999; 3333; 5555; 6666; 1122; 1313; 8888; 4321; 2001; 1010.

Even microsoft office word - Activators Patch a password dictionary, using differences in frequency of letter use (or double letters) in a language makes it possible to plan an effective attack. Some attack methods also take into account that, to facilitate memorization, people may choose passwords that have a certain structure—such as A1=B2=C3, AwX2AwX2 or O0o.lli. (which I used for a long time)—or that are derived by combining several simple strings, such as password123 or johnABC0000. Exploiting such regularities makes it possible to for hackers to speed up detection.

Making Hash of Hackers

As the main text explains, instead of storing clients’ passwords, Internet servers store the “fingerprints” of these passwords: sequences of characters that are derived from the passwords. In the event of an attack, the use of fingerprints can make it is very difficult, if not impossible, for hackers to use what they find.

The transformation is achieved by using algorithms known as cryptographic hash functions. These are meticulously developed processes that transform a data file, F, however long it may be, into a sequence, h(F), called a fingerprint of F. For example, the hash function SHA256 transforms the phrase “Nice weather” into:

DB0436DB78280F3B45C2E09654522197D59EC98E7E64AEB967A2A19EF7C394A3
(64 hexadecimal, or base 16, characters, which is equivalent to 256 bits)

Changing a single character in the file completely alters its fingerprint. For example, if sketch 49 crack - Crack Key For U first character of Nice weather is changed to lowercase (nice weather), the hash SHA256 will generate another fingerprint:

02C532E7418CD1B57961A1B090DB6EC37B3C58380AC0E6877F3B6155C974647E

You can do these calculations yourself and check them at https://passwordsgenerator.net/sha256-hash-generator or www.xorbin.com/tools/sha256-hash-calculator

Good hash functions produce fingerprints that are similar to those that would be obtained if the fingerprint sequence was uniformly chosen at random. In particular, for any possible random result (a sequence of 64 hexadecimal characters), it is impossible to find a data file F with this fingerprint in a turbo application - Free Activators amount of time.

There have been several generations of hash functions. The SHA0 and SHA1 generations are obsolete and are not recommended. The SHA2 generation, including SHA256, is considered secure. 

The Take-Home for Consumers

Taking all this into account, properly designed Web sites analyze the passwords proposed at the time of their creation and reject those that would be too easy to recover. It is irritating, but it’s for your own good.

The obvious conclusion for users is that they must choose their passwords randomly. Some software does provide a random password. Be aware, however, that such password-generating software may, deliberately or not, use a poor pseudo-random generator, in which case what it provides may be imperfect.

You can check whether any of your passwords has already been hacked by using a Web tool called Pwned Passwords (https://haveibeenpwned.com/Passwords). Its database includes more than 500 million passwords obtained after various attacks.

I tried e=mc2e=mc2, which I liked and believed to be secure, and received an unsettling response: “This password has been seen 114 times before.” Additional attempts show that it is difficult to come up with easy-to-memorize passwords that the database does not know. For example, aaaaaa appeared 395,299 times; a1b2c3d4, 113,550 times; abcdcba, 378 times; abczyx, 186 times; acegi, 117 times; clinton, 18,869 times; bush, 3,291 times; obama, 2,391 times; trump, 859 times.

It is still possible to be original. The Web site did not recognize the following six passwords, for example: eyahaled (my name spelled backward); bizzzzard; meaudepace and modeuxpass (two puns on the French for “password”); abcdef2019; passwaurde. Now that I’ve tried them, I wonder if the database will add them when it next updates. In that case, I won’t use them.

Advice for Web Sites

Web sites, too, follow various rules of thumb. The National Institute of Standards and Technology recently published a notice recommending the use of dictionaries to filter users’ password choices.

Among the rules that a good Web server designer absolutely must adhere to is, do not store plaintext lists of usernames and passwords on the computer used to operate the Web site.

The reason is obvious: hackers sketch 49 crack - Crack Key For U access the computer containing this list, either because the site is poorly protected or because the system or processor contains a serious flaw unknown to anyone except the attackers (a so-called zero-day flaw), who can exploit it.

One alternative is to encrypt the passwords on the server: use a secret code that transforms them via an encryption key into what will appear to be random character sequences to anyone who does not possess the decryption key. This method works, but it has two disadvantages. First, it requires decrypting the stored password every time to compare it with the user’s entry, which is inconvenient. Second, and more seriously, the decryption necessary for this comparison requires storing the decryption key in the Web site computer’s memory. This key may therefore be detected by an attacker, which brings us back to the original problem.

A better way to store passwords is through what are called hash functions that produce “fingerprints.” For any data in a file—symbolized as F—a hash function generates a fingerprint. (The process is also called condensing or hashing.) The fingerprint—h(F)—is a fairly short word associated with F but produced in such a way that, in practice, it is impossible to deduce F from h(F). Hash functions are said to be one-way: getting from F to h(F) is easy; getting from h(F) to F is practically impossible. In addition, the hash functions used have the characteristic that even if it is possible for two data inputs, F and F', to have the same fingerprint (known as a collision), in practice for a given F, it is almost impossible to find an F' with a fingerprint identical to F.

Using such hash functions allows passwords to be securely stored on a computer. Instead of storing the list of paired usernames and passwords, the server stores only the list of username/fingerprint pairs.

When a user wishes to connect, the server will read the individual’s password, compute the fingerprint and determine whether it corresponds to the list of stored username/fingerprint pairs associated with that username. That maneuver frustrates hackers because even if they have managed to access the list, they will be unable to derive the users’ passwords, inasmuch as it is practically impossible to go from fingerprint to password. Nor can they generate another password with an identical fingerprint to fool the server because it is practically impossible to create collisions.

Still, no approach is foolproof, as is highlighted by frequent reports of the hacking of major sites. In 2016, for example, data from a billion accounts were stolen from Yahoo!

For added safety, a method known as salting is sometimes used to further impede hackers from exploiting stolen lists of username/fingerprint pairs. Salting is the addition of a unique random string of characters to each password. It ensures that even if two users employ the same password, the stored fingerprints will differ. The list on the server will contain three components for each user: username, fingerprint derived after salt was added to the password, and the salt itself. When the server checks the password entered by a user, it adds the salt, computes the fingerprint and compares the result with its database.

Even when user passwords are weak, this method considerably complicates the hacker’s work. Without salting, a hacker can compute all the fingerprints in a dictionary and see those in the stolen data; all the passwords in the hacker’s dictionary can be identified. With salting, for every salt used, the hacker must compute the salted fingerprints of all the passwords in the hacker’s dictionary. For a set of 1,000 users, this multiplies by 1,000 the computations required to use the hacker’s dictionary.

Survival of the Fittest

It goes without saying that hackers have their own ways of fighting back. They face a dilemma, though: their simplest options either take a lot of computing power or a lot of memory. Often neither option is viable. There is, however, a compromise approach known as the rainbow table method (see “Rainbow Tables Help Hackers”).

In the age of the Internet, supercomputers and computer networks, the science of password setting and cracking continues to evolve—as does the relentless struggle between those who strive to protect passwords and those who are determined to steal, and potentially abuse, them.

Rainbow Tables Help Hackers

Say you are a hacker looking to exploit data that you have acquired. These data consist of username/fingerprint pairs, and you know the hash function (see “Making Hash of Hackers”). The password is contained in the possibility space of strings of 12 lowercase letters, which corresponds to 56 bits of information and 2612 (9.54 x 1016) possible passwords.

At least two strong approaches are open to you:

Method 1. You scroll through the entire space of passwords. You calculate the fingerprint, h(P), for each password, checking to see whether it appears in the stolen data. You do not need a lot of memory, because prior results are deleted with each new attempt, although you do, of course, have to keep track of the possibilities that have been tested.

Scrolling through all the possible passwords in this way takes a long time. If your computer runs a billion tests per second, you will need 2612/(109 x 3,600 x 24) days (1,104 days), or about three years to complete the task. The feat is not impossible; if you happen to have a computer network of 1,000 machines, one day will suffice. It is not feasible, however, to repeat such a calculation every time you wish to test additional data, such as if you obtain a new set of username/fingerprint pairs. (Because you have not saved the results of your computations, you would need an additional 1,104 days to process the new information.)

Method 2. You say to yourself, “I’ll compute the fingerprints of all possible passwords, which will take time, and I’ll store the resulting fingerprints in a big table. Then I’ll have to find only a password fingerprint in the table to identify the corresponding password in the stolen data.”

You will need (9.54 x 1016) x (12 + 32) bytes of memory because the task requires 12 bytes for the password and 32 bytes for the fingerprint if the fingerprint contains 256 bits (assuming an SHA256 function). That’s 4.2 x 1018 bytes, or 4.2 million hard disks with a capacity of one terabyte.

This memory requirement is too large. Method 2 is no more feasible than method 1. Method 1 requires too many computations, and method 2 requires too much memory. Both cases are problematic: either each new password takes too long to compute, or precomputing all possibilities and storing all the results is too large a task.

Is there some compromise that requires less computing power than method 1 and less memory than required for method 2? Indeed, there is. In 1980 Martin Hellman of Stanford University suggested an approach that was improved in 2003 by Philippe Oechslin of the Swiss Federal Institute of Technology in Lausanne and further refined more recently by Gildas Avoine of the National Institute of Applied Sciences of Rennes (INSA Rennes) in France. It demands less computing power than method 1 in exchange for using a little more memory.

The Beauty of the Rainbow

Here is how it works: First, we need a function R that transforms a fingerprint h(P) into a new password R(h(P)). One might, for instance, consider fingerprints as numbers written in the binary numeral system and consider passwords as numbers written in the K numeral system, where K is the number of allowable symbols for passwords. Then the function R converts data from the binary numeral system to the K numeral system. For every fingerprint h(P), it computes a new password R(h(P)).

Now, with this function R, we can precompute data tables called rainbow tables (so named perhaps because of the multicolored way these tables are depicted).

To generate a data point in this table, we start from a possible password P0, compute its fingerprint, h(P0) and then compute a new possible password R(h(P0)), which becomes P1. Next, we continue this process from P1. Without storing anything other than P0, we compute the sequence P1, P2. until the fingerprint starts with 20 zeros; that fingerprint is designated h(Pn). Such a fingerprint occurs only once in about 1,000,000 fingerprints because the result of a hash function is similar to result of a uniform random draw, and 220 is roughly equal to 1,000,000. The password/fingerprint pair [P0, h(Pn)], containing the fingerprint that starts with 20 zeros is then stored in the table.

A very large number of pairs of this type are computed. Each password/fingerprint pair [P0, h(Pn)] represents the sequence of passwords P0, P1,… Pn and their fingerprints, but the table does not store those intermediate calculations. The table thus lists many password/fingerprint pairs and represents many more (the intermediates, such as P1 and P2, that can be derived from the listed pairs). But, of course, there may be gaps: some passwords may be absent from all the chains of calculations.

For a good database with almost no gaps, the memory needed to store the calculated pairs is a million times smaller than that needed for method 2, as described earlier. That is less than four one-terabyte hard disks. Easy. Also, as will be seen, using the table to derive passwords from stolen fingerprints is quite doable.

Let us see how the data stored on the hard disks makes it possible to determine a password in a given space in just a few seconds. Assume that there are no gaps; precomputation of the table takes into account all the passwords of a designated type—for example, 12-character passwords taken from the 26 letters of the alphabet.

A fingerprint f0 in a stolen data set can be used to reveal the associated password in the following way. Calculate h(R(f0)) to arrive at a new fingerprint, f1, then calculate h(R(f1)) to get f2, and so on, until you get to a fingerprint that begins with 20 zeros: fm. Then check the table to see which original password, P0, the fingerprint fm is associated with. Based on P0, calculate the passwords and fingerprints h1, h2. that follow until you inevitably generate the original fingerprint f0, designated hk. The password you are looking for is the one that gave rise to hk—in other words, R(hk – 1), which is one step earlier in the chain of calculations.

The computation time required is what it takes to look for fm in the table plus the time needed to compute the sequence of fingerprints from the associated password (h1, h2,…, hk)—which is about a million times shorter than the time needed to compute the table itself. In other words, the time needed is quite reasonable.

Thus, doing a (very long) precomputation and storing only part of the results makes it possible to retrieve any password with a known fingerprint in a reasonable amount of time.

The sequences below represent separate chains of calculations leading from passwords (Mo, No., Qo) to fingerprints and other passwords, until the desired fingerprint (and thus the password that precedes it) pops out.(The long dotted line represents may other lines similar to the top two.)

To summarize, by knowing the beginning and end of each chain of computations (the only things that are stored during precomputation), a hacker can retrieve any password from a fingerprint. In somewhat simplistic terms, starting from a stolen fingerprint—call it fingerprint X—a hacker would apply the R and h functions repeatedly, calculating a series of passwords and fingerprints until reaching a fingerprint with 20 zeros in front of it. The hacker would then look up that final fingerprint in the table (Fingerprint C in the example below) and identify its corresponding password (Password C).

Sample Table Excerpt
Password A—Fingerprint A
Password B—Fingerprint B
Password CFingerprint C
Password D—fingerprint D

Next, the hacker would apply the h and R functions again, beginning with the identified password, continuing on until one of the resulting fingerprints in the chain matches the stolen fingerprint:

Sample Calculation

Password C → fingerprint 1 → password 2-- → fingerprint 2 → password 3&hellip. → password 22-- →fingerprint 23 [a match to fingerprint X!]

The match (fingerprint 23) would indicate that the previous password (password 22), from which the fingerprint was derived, is the one linked to the stolen fingerprint.

Many computations must be done to establish the first and last column of the rainbow table. By storing only the data in these two columns and by recomputing the chain, hackers can identify any password from its fingerprint.

Источник: https://www.scientificamerican.com/article/the-mathematics-of-hacking-passwords/

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Environmental stress cracking

Crazes (surface cracks) produced by ESC in PMMAdrinking beaker

Environmental Stress Cracking (ESC) is one of the most common causes of unexpected brittle failure of thermoplastic (especially amorphous) polymers known at present. According to ASTM D883, stress cracking is defined as "an external or internal crack in a plastic caused by tensile stresses less than its short-term mechanical strength". This type of cracking typically involves brittle cracking, with little or no ductile drawing of the material from its adjacent failure surfaces.[1] Environmental stress cracking may account for around 15-30% of all plastic component failures in service.[2] This behavior is especially prevalent in glassy, amorphous thermoplastics.[3] Amorphous polymers exhibit ESC because of their loose structure which makes it easier for the fluid to permeate into the polymer. Amorphous polymers are more prone to ESC at temperature higher than their glass transition temperature (Tg) due to the increased free volume. When Tg is approached, more fluid can permeate into the polymer chains.[4]

ESC and polymer resistance to ESC (ESCR) have been studied for several decades.[5] Research shows that the exposure of polymers to liquid chemicals tends to accelerate the crazing process, initiating crazes at stresses that are much lower than the stress causing crazing in air.[5][6] The action of either a tensile stress or a corrosive liquid alone would not be enough to cause failure, but in ESC the initiation and Total PDF Converter Free Activate of a crack is caused by the combined action of the stress and a corrosive environmental liquid. These corrosive environmental liquids are called 'secondary chemical agents', are often organic, and are defined as solvents not anticipated to come into contact with the plastic during its lifetime of use. Failure is rarely associated with primary chemical agents, as these materials are anticipated to come into contact with the polymer during its lifetime, and thus compatibility is ensured prior to use. In air, failure due to creep is known as creep rupture, as the air acts as a plasticizer, and this acts in parallel to environmental stress cracking.[7]

It is somewhat different from polymer degradation in that stress cracking does not break polymer bonds. Instead, it breaks the secondary linkages between polymers. These are broken when the mechanical stresses cause minute cracks in the polymer and they propagate rapidly under the harsh environmental conditions.[8] It has also been seen that catastrophic failure under stress can occur due to the attack of a reagent that would not attack the polymer in an unstressed state. Environmental stress cracking is accelerated due to higher temperatures, cyclic loading, increased stress concentrations, and fatigue.[7]

Metallurgists typically use the term Stress corrosion cracking or Environmental stress fracture to describe this type of failure in metals.

Predicting ESC[edit]

Although the phenomenon of ESC has been known for a number of decades, research has not yet enabled prediction of this type of failure for all environments and for every type of polymer. Some scenarios are well known, documented or are able to be predicted, but there is no complete reference for all combinations of stress, polymer and environment. The rate of ESC is dependent on many factors including the polymer's chemical makeup, bonding, crystallinity, surface roughness, molecular weight and residual stress. It also depends on the liquid reagent's chemical nature and concentration, the temperature of the system and the strain rate.

Mechanisms of ESC[edit]

There are a number of opinions on how certain reagents act on polymers under stress. Because ESC is often seen in amorphous polymers rather than in semicrystalline polymers, theories regarding the mechanism of ESC often revolve around liquid interactions with the amorphous regions of polymers. One such theory is that the liquid can diffuse into the polymer, causing swelling which increases the polymer's chain mobility. The result is a decrease in the yield stress and glass transition temperature (Tg), as well as a plasticisation of the material which leads to crazing at lower stresses and strains.[2][6] A second view is that the liquid can reduce the energy required to create new surfaces in the polymer by wetting the polymer's surface and hence aid the formation of voids, which is thought to be very important in the early stages of craze formation.[2] ESC may occur continuously, or a piece-wise start and stop mechanism

There is an array of experimentally derived evidence to support the above theories:

  • Once a craze is formed in a polymer this creates an easy diffusion path so that the environmental attack can continue and the crazing process can accelerate.
  • Chemical compatibility between the environment and the polymer govern the amount in which the environment can swell and plasticise the polymer.[2]
  • The effects of ESC are reduced when crack growth rate is high. This is primarily due to the inability of the liquid to keep up with the growth of the crack.[2]
  • Once separated from the other chains, the polymers align, thus allowing embrittlement.

ESC generally occurs at the surface of a plastic and doesn't require the secondary chemical agent to penetrate the material significantly, which leaves the bulk properties unmodified.[7]

Another theory for the mechanism of craze propagation in amorphous polymers is proposed by Kramer. According to his theory, the formation of internal surfaces in polymers is facilitated by polymeric surface tension that is determined by both secondary interactions and the contribution of load-bearing chains that must undergo fracture or slippage to form a surface. This theory provides and explanation for the decrease in the stress needed to propagate the craze in the presence of surface-active reagents such as detergents and high temperature.[9]

ESC mechanism in polyethylene[edit]

Semi-crystalline polymers such as polyethylene show brittle fracture under stress if exposed to stress cracking agents. In such polymers, the crystallites are connected by the tie molecules through the amorphous phase. The tie molecules play an important role in the mechanical properties of the polymer through the transferring of load. Stress cracking agents, such as detergents, act to lower the cohesive forces which maintain the tie molecules in the crystallites, thus facilitating their “pull-out” and disentanglement from the lamellae.[10] As a result, cracking is initiated at stress values lower than the critical stress level of the material.

In general, the mechanism of environmental stress cracking in polyethylene involves the disentanglement of the tie molecules from the crystals. The number of tie molecules and the strength of the crystals that anchor them are considered the controlling factors in determining the polymer resistance to ESC.[11]

Characterizing ESC[edit]

A number of different methods are used to evaluate a polymer's resistance to environmental stress cracking. A common method in the polymer industry is use of the Bergen jig, which subjects the sample to variable strain during a single test. The results of this test indicate the critical strain to cracking, using only one sample.[5] Another widely used test is the Bell Telephone test where bent strips are exposed to fluids of interest under controlled conditions. Further, new tests have been developed where the time for crack initiation under transverse loading and an aggressive solvent (10% Igepal CO-630 solution) is evaluated. These methods rely on an indentor to stress the material biaxially, while preventing a radial stress concentration. The stressed polymer sits in the aggressive sketch 49 crack - Crack Key For U and the stressed plastic around the indentor is watched to evaluate the time to crack formation, which is the way that ESC resistance is quantified. A testing apparatus for this method is known as the Telecom and is commercially available; initial experiments have shown that this testing gives equivalent results to ASTM D1693, but at a much shorter time scale.[12] Current research deals with the application of fracture mechanics to the study of ESC phenomena.[13][14] In summary, though, there is not a singular descriptor that is applicable to ESC—rather, the specific fracture is dependent on the material, conditions, and secondary chemical agents present.

Scanning electron microscopy and fractographic methods have historically been used to analyze the failure mechanism, particularly in high density polyethylene (HDPE). Freeze fracture has proved particularly useful for examining the kinetics of ESC, as they provide a snapshot in time of the crack propagation process.[1]

Strain hardening as a measure of environmental stress cracking resistance (ESCR)[edit]

Many different methods exist for sound forge audio studio 12 keygen - Crack Key For U ESCR. However, the long testing time and high costs associated with these methods slow sketch 49 crack - Crack Key For U the R&D activities for designing materials with higher resistance to stress cracking. To overcome these challenges, a new simpler and faster method was developed by SABIC to assess ESCR for high density polyethylene (HDPE) materials. In this method, the resistance of slow crack growth or environmental stress cracking is predicted from simple tensile measurement at a temperature of 80℃.[9] When polyethylene is deformed under a uniaxiial tension, before yield, the stiff crystalline phase of the polymer undergoes small deformation while the amorphous domains deforms significantly. After the yield point but before the material undergoes strain hardening, the crystalline lamellae slips where both the crystalline phase and the amorphous domains contribute to load bearing and straining. At some point, the amorphous domains will stretch fully at which the strain hardening begin. In the strain hardening region, the elongated amorphous domains become the loading bearing phase whereas the crystalline lamellae undergoes fracture and unfold to adjust for the change in strain. The load-bearing chains in the amorphous domains in polyethylene are made of tie-molecules and entangles chains. Because of the key role of tie-molecules and entanglements in resisting environmental stress cracking in polyethylene, it follows that ESCR and strain hardening behaviors can very well be correlated.[15]

In the strain hardening method, the slope of strain hardening region (above the natural draw ratio) in the true stress-strain curves is calculated and used as a measure of ESCR. This slope is called the strain hardening modulus (Gp). The strain hardening modulus is calculated over the entire strain hardening region in the true stress strain curve. The strain hardening region of the stress-strain curve is considered to be the homogeneously deforming part well above the natural draw ratio, which is determined by presence of the neck propagation, and below the maximum elongation.[9] The strain hardening modulus when measured at 80℃ is sensitive to the same molecular factors that govern slow crack resistance in HDPE as measured by an accelerated ESCR test where a surface active agent is used.[9] The strain hardening modulus and ESCR values for polyethylene have been found to be strongly correlated with each others.

Examples[edit]

An obvious example of the need to resist ESC in everyday life is the automotive industry, in which a number of different polymers are subjected to a number of fluids. Some of the chemicals involved in these interactions include petrol, brake fluid and windscreen cleaning solution.[6]Plasticisers leaching from PVC can also cause ESC over an extended period of time, for example. One of the first examples of the problem concerned ESC of LDPE. The material was initially used in insulating electric cables, and cracking occurred due to the interaction of the insulation with oils. The solution to the problem lay in increasing the molecular weight of the polymer. A test of exposure to a strong detergent such as Igepal was developed to give a warning of ESC.

SAN piano key[edit]

A more specific example comes in the form of a piano key made from injection moulded styrene acrylonitrile (SAN). The key has a hook end which connects it to a metal spring, which causes the key to spring back into position after being struck. During assembly of the piano an adhesive was used, and excess adhesive which had spilled onto areas where it was not required was removed using a ketone solvent. Some vapour from this solvent condensed on the internal surface of the piano keys. Some time after this cleaning, fracture occurred at the junction where the hook end meets the spring.[16]

To determine the cause of the fracture, the SAN piano key was heated above its glass transition temperature for a short time. If there is residual stress within the polymer, the piece will shrink when held at such a temperature. Results showed that there was significant shrinkage, particularly at the hook end-spring junction. This indicates stress concentration, possibly the combination of residual stress from forming and the action of the spring. It was concluded that although there was residual stress, the fracture was due to a combination of the tensile stress from the spring action and the presence of the ketone solvent.[16]

See also[edit]

References[edit]

  1. ^ abChoi, Byoung-Ho; Weinhold, Jeffrey; Reuschle, David; Kapur, Mridula (2009). "Modeling of the fracture mechanism of HDPE subjected to environmental stress crack resistance test". Polymer Engineering & Science. 49 (11): 2085–2091. doi:10.1002/pen.21458. ISSN 1548-2634.
  2. ^ abcdeH. F. Mark. Encyclopedia of Polymers Science and Technology – 3rd Ed. Vol 12. John Miley & Sons Inc. 2004
  3. ^Henry, L. F. (1974). "Prediction and evaluation of the susceptibilities of glassy thermoplastics to environmental stress cracking". Polymer Engineering & Science. 14 (3): 167–176. doi:10.1002/pen.760140304. ISSN 1548-2634.
  4. ^J. Scheirs (2000). Compositional and Failure Analysis of Polymers. J. Wiley & Sons.
  5. ^ abcXiangyang Li. Environmental Stress Cracking Resistance of a New Copolymer of Bisphenol-A. Polymer Degradation and Stability. Volume 90, Issue 1, October 2005, Pages 44-52
  6. ^ abcJ. C. Arnold. The Effect of Diffusion on Environmental Stress Crack Adobe After Effects CC Free Activate in PMMA. Journal of Materials Science 33 (1998) p 5193 – 5204
  7. ^ abc"Plastics Engineering - November/December 2015 - Plastic Failure through Environmental Stress Cracking". read.nxtbook.com. Retrieved 23 May 2019.
  8. ^Michigan University – College of Engineering, Properties of PlasticsArchived 6 May 2008 at the Wayback Machine. Accessed 22 April 2008.
  9. ^ abcdKureleca, L.; Teeuwenb, M.; Schoffeleersb, H.; Deblieckb, R. (2005). "Strain hardening modulus as a measure of environmental stress crack resistance of high density polyethylene". Polymer. 46 (17): 6369–6379. doi:10.1016/j.polymer.2005.05.061.
  10. ^Chen, Yang (2014). "Investigations of environmental stress cracking resistance of HDPE/EVA and LDPE/EVA blends". Journal of Applied Polymer Science. 131 (4): n/a. doi:10.1002/app.39880. ISSN 1097-4628.
  11. ^Ward, A. L.; Lu, X.; Huang, Y.; Brown, N. (1 January 1991). "The mechanism of slow crack growth in polyethylene by an environmental stress cracking agent". Polymer. 32 (12): 2172–2178. doi:10.1016/0032-3861(91)90043-I. ISSN 0032-3861.
  12. ^Jar, Ben (2017). "A NEW METHOD TO CHARACTERIZE ENVIRONMENTAL STRESS CRACKING RESISTANCE (ESCR) OF POLYETHYLENE PIPES". SPE ANTEC Anaheim 2017: 1994–1998. S2CID 13879793.
  13. ^Andena, Luca; Castellani, Leonardo; Castiglioni, Andrea; Mendogni, Andrea; Rink, Marta; Sacchetti, Francisco (1 March 2013). "Determination of environmental stress cracking resistance of polymers: Effects of loading history and testing configuration". Engineering Fracture Mechanics. Fracture of Polymers, Composites and Adhesives. 101: 33–46. doi:10.1016/j.engfracmech.2012.09.004.
  14. ^Kamaludin, M.A.; Patel, Y.; Williams, J.G.; Blackman, B.R.K. (2017). "A fracture mechanics approach to characterising the environmental stress cracking behaviour of thermoplastics". Theoretical and Applied Fracture Mechanics. aster v7 2.22 keygen - Free Activators 373–380. doi:10.1016/j.tafmec.2017.06.005. hdl:10044/1/49864.
  15. ^Cheng, Joy J.; Polak, Maria A.; Penlidis, Alexander (1 June 2008). "A Tensile Strain Hardening Test Indicator of Environmental Stress Cracking Resistance". Journal of Macromolecular Science, Part A. 45 (8): 599–611. doi:10.1080/10601320802168728. ISSN 1060-1325. S2CID 137204431.
  16. ^ abEzrin, M & Lavigne, G. Unexpected and Unusual Failures of Polymeric Materials. Engineering Failure Analysis, Volume 14, Pages 1153-1165, January 2007

Further reading[edit]

  • Ezrin, Meyer, Plastics Failure Guide: Cause and Prevention, Hanser-SPE (1996).
  • Wright, David C., Environmental Stress Cracking of Plastics RAPRA (2001).
  • Lewis, Peter Rhys, Reynolds, K and Gagg, C, Forensic Materials Engineering: Case studies, CRC Press (2004)
Источник: https://en.wikipedia.org/wiki/Environmental_stress_cracking
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