In our method, wordlists are separated based on their source or characteristics. In many previous wordlist approaches, a single ordered wordlist is created. Importantly, it requires no a-priori training. After each guess, it uses the relative success of all previous guesses to identify how well each wordlist matches the passwords (see Sect. Our algorithm suggests guesses to be made against a population of users in an online or offline attack. In this paper, our contributions are as follows: In this paper, we investigate whether an automated learning algorithm can identify these idiosyncrasies within a password set and if it can leverage this knowledge in order to improve the success of password guessing rates. In addition, users have been observed choosing passwords that reflect the nature of the website they are choosing the password for . For example, users from similar demographics will often choose similar passwords .
Users choosing a password are known to be influenced by common factors. In this paper, we will show the speed of our learning strategy and compare it to the optimal rate of password compromise, a term we will discuss in more detail later. Therefore, a quick learning strategy to maximise rewards is valuable. Often they may only be able to make a small number of guesses before they will become locked out. The order in which guesses are made can be important for a password guesser.
To our knowledge, this learning problem has not been studied before. In this paper we are interested in investigating whether we can automate this learning and use it to inform wordlist choice. A human attacker who is guessing password will look for clues such as language, nationality and composition policies that might indicate a good wordlist to use in order to guess a password set, i.e. Guessing passwords either involves formulating new words to try as guesses or using existing wordlists that include common password choices, words based on language dictionaries and datasets of previous password leaks. This way, we can create countermeasures to protect the security of users. It is important for security advocates and researchers to understand the capabilities of attackers given they have access to this data. In fact, with the regular occurrence of leaks of password datasets , attackers are provided with an increasing amount of data to inform password guesses. However, one major weakness is that human chosen passwords can often be guessed by attackers. Passwords are a widely used form of authentication online.
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