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Worst passwords of 2014 are just as awful as you can imagine - CNET. Few smartwatches have so far resonated with consumers. Apple is trying to drag the entire category into the mainstream with what it calls "the most advanced timepiece ever created. " The consumer technology industry has spent the last 18 months hailing wearable devices as the next big thing. But who will want a smartwatch? And, more important, why do you need one? Apple on Monday set out to answer those questions with the Apple Watch, its entry into the burgeoning area of wearable technology.

Though the watch made its debut in September 2014, customers had few details about the gadget until today. Customers in those nine countries can start going to Apple's retail stores and select department stores such as Selfridges in London and Galeries Lafayettes in Paris beginning April 10 to try them on and determine the right size. "Apple Watch brings a whole new personal dimension to timekeeping that's never been done before," Apple CEO Tim Cook said at an event in San Francisco on Monday.

Today I Am Releasing Ten Million Passwords. Frequently I get requests from students and security researchers to get a copy of my password research data. I typically decline to share the passwords but for quite some time I have wanted to provide a clean set of data to share with the world. A carefully-selected set of data provides great insight into user behavior and is valuable for furthering password security. So I built a data set of ten million usernames and passwords that I am releasing to the public domain. But recent events have made me question the prudence of releasing this information, even for research purposes.

The arrest and aggressive prosecution of Barrett Brown had a marked chilling effect on both journalists and security researchers. Suddenly even linking to data was an excuse to get raided by the FBI and potentially face serious charges. “This is completely absurd that I have to write an entire article justifying the release of this data out of fear of prosecution” Why the FBI Shouldn’t Arrest Me Slippery Slopes. The Most Common and Least Used 4-Digit PIN Numbers [Security Analysis Report] How ‘secure’ is your 4-digit PIN number? Is your PIN number a far too common one or is it a bit more unique in comparison to others? The folks over at the Data Genetics blog have put together an interesting analysis report that looks at the most common and least used 4-digit PIN numbers chosen by people. Numerically based (0-9) 4-digit PIN numbers only allow for a total of 10,000 possible combinations, so it stands to reason that some combinations are going to be far more common than others.

The question is whether or not your personal PIN number choices are among the commonly used ones or ‘stand out’ as being more unique. Note 1: Data Genetics used data condensed from released, exposed, & discovered password tables and security breaches to generate the analysis report. Note 2: The updates section at the bottom has some interesting tidbits concerning peoples’ use of dates and certain words for PIN number generation. PIN Number Analysis [via Apartment Therapy] Dictionary attack. In cryptanalysis and computer security, a dictionary attack is a technique for defeating a cipher or authentication mechanism by trying to determine its decryption key or passphrase by trying hundreds or sometimes millions of likely possibilities, such as words in a dictionary. Technique[edit] Pre-computed dictionary attack/Rainbow table attack[edit] It is possible to achieve a time-space tradeoff by pre-computing a list of hashes of dictionary words, and storing these in a database using the hash as the key.

This requires a considerable amount of preparation time, but allows the actual attack to be executed faster. The storage requirements for the pre-computed tables were once a major cost, but are less of an issue today because of the low cost of disk storage. Pre-computed dictionary attacks are particularly effective when a large number of passwords are to be cracked.

Dictionary attack software[edit] See also[edit] External links[edit] Password Reuse. Salt & Pepper, please: a note on password storage | PyTux. Password Strength. GRC's | Password Haystacks: How Well Hidden is Your Needle?   ... and how well hidden is YOUR needle? Every password you use can be thought of as a needle hiding in a haystack. After all searches of common passwords and dictionaries have failed, an attacker must resort to a “brute force” search – ultimately trying every possible combination of letters, numbers and then symbols until the combination you chose, is discovered. If every possible password is tried, sooner or later yours will be found. The question is: Will that be too soon . . . or enough later? This interactive brute force search space calculator allows you to experiment with password length and composition to develop an accurate and quantified sense for the safety of using passwords that can only be found through exhaustive search.

<! (The Haystack Calculator has been viewed 8,151,017 times since its publication.) IMPORTANT!!! It is NOT a “Password Strength Meter.” Since it could be easily confused for one, it is very important for you to understand what it is, and what it isn't: Okay. Brute-force attack. The EFF's US$250,000 DEScracking machine contained over 1,800 custom chips and could brute-force a DES key in a matter of days. The photograph shows a DES Cracker circuit board fitted on both sides with 64 Deep Crack chips. When password guessing, this method is very fast when used to check all short passwords, but for longer passwords other methods such as the dictionary attack are used because of the time a brute-force search takes. When key guessing, the key length used in the cipher determines the practical feasibility of performing a brute-force attack, with longer keys exponentially more difficult to crack than shorter ones.

A cipher with a key length of N bits can be broken in a worst-case time proportional to 2N and an average time of half that. Brute-force attacks can be made less effective by obfuscating the data to be encoded, something that makes it more difficult for an attacker to recognize when he/she has cracked the code. Theoretical limits[edit] Credential recycling[edit]

25-GPU cluster cracks every standard Windows password in <6 hours. A password-cracking expert has unveiled a computer cluster that can cycle through as many as 350 billion guesses per second. It's an almost unprecedented speed that can try every possible Windows passcode in the typical enterprise in less than six hours. The five-server system uses a relatively new package of virtualization software that harnesses the power of 25 AMD Radeon graphics cards. It achieves the 350 billion-guess-per-second speed when cracking password hashes generated by the NTLM cryptographic algorithm that Microsoft has included in every version of Windows since Server 2003. As a result, it can try an astounding 958 combinations in just 5.5 hours, enough to brute force every possible eight-character password containing upper- and lower-case letters, digits, and symbols. Such password policies are common in many enterprise settings.

Gosney unveiled the machine last week at the Passwords^12 conference in Oslo, Norway. Slides of Gosney's Passwords^12 presentation are here. Prevention. Rainbow table. Rainbow tables are an application of an earlier, simpler algorithm by Martin Hellman.[1] Simplified rainbow table with 3 reduction functions Background[edit] Any computer system that requires password authentication must contain a database of passwords, either hashed or in plaintext, and various methods of password storage exist.

Because the tables are vulnerable to theft, storing the plaintext password is dangerous. Someone who gains access to the (hashed) password table cannot merely enter the user's (hashed) database entry to gain access (using the hash as a password would of course fail since the authentication system would hash that a second time, producing a result which does not match the stored value, which was hashed only once). Rainbow tables are one tool that has been developed in an effort to derive a password by looking only at a hashed value. Rainbow tables are not always needed, for there are simpler methods of hash reversal available.

Precomputed hash chains[edit] Or. Birthday attack. Understanding the problem[edit] , about 7.9%. However, the probability that at least one student has the same birthday as any other student is around 70% for n = 30, from the formula Mathematics[edit] Given a function , the goal of the attack is to find two different inputs such that . Yields any of different outputs with equal probability and is sufficiently large, then we expect to obtain a pair of different arguments and with after evaluating the function for about different arguments on average. We consider the following experiment. Let n(p; H) be the smallest number of values we have to choose, such that the probability for finding a collision is at least p. And assigning a 0.5 probability of collision we arrive at Let Q(H) be the expected number of values we have to choose before finding the first collision. As an example, if a 64-bit hash is used, there are approximately 1.8 × 1019 different outputs.

The subexpression in the equation for is not computed accurately for small birthday.cc , where . Meet-in-the-middle attack. The Meet-in-the-Middle attack (MITM) is a generic space–time tradeoff cryptographic attack. Description[edit] MITM is a generic attack, applicable on several cryptographic systems. The internal structure of a specific system is therefore unimportant to this attack. An attacker requires the ability to encrypt and decrypt, and the possession of pairs of plaintexts and corresponding ciphertexts. When trying to improve the security of a block cipher, a tempting idea is to simply use several independent keys to encrypt the data several times using a sequence of functions (encryptions). The Meet-in-the-Middle attack attempts to find a value using both of the range (ciphertext) and domain (plaintext) of the composition of several functions (or block ciphers) such that the forward mapping through the first functions is the same as the backward mapping (inverse image) through the last functions, quite literally meeting in the middle of the composed function.

History[edit] MITM (1D-MITM)[edit] in a set.