Password Cracking and Countermeasures in Computer Security: A Survey

With the rapid development of internet technologies, social networks, and other related areas, user authentication becomes more and more important to protect the data of the users. Password authentication is one of the widely used methods to achieve authentication for legal users and defense against intruders. There have been many password cracking methods developed during the past years, and people have been designing the countermeasures against password cracking all the time. However, we find that the survey work on the password cracking research has not been done very much. This paper is mainly to give a brief review of the password cracking methods, import technologies of password cracking, and the countermeasures against password cracking that are usually designed at two stages including the password design stage (e.g. user education, dynamic password, use of tokens, computer generations) and after the design (e.g. reactive password checking, proactive password checking, password encryption, access control). The main objective of this work is offering the abecedarian IT security professionals and the common audiences with some knowledge about the computer security and password cracking, and promoting the development of this area.Comment: add copyright to the tables to the original authors, add acknowledgement to helpe

Interpretable Probabilistic Password Strength Meters via Deep Learning

Probabilistic password strength meters have been proved to be the most accurate tools to measure password strength. Unfortunately, by construction, they are limited to solely produce an opaque security estimation that fails to fully support the user during the password composition. In the present work, we move the first steps towards cracking the intelligibility barrier of this compelling class of meters. We show that probabilistic password meters inherently own the capability of describing the latent relation occurring between password strength and password structure. In our approach, the security contribution of each character composing a password is disentangled and used to provide explicit fine-grained feedback for the user. Furthermore, unlike existing heuristic constructions, our method is free from any human bias, and, more importantly, its feedback has a clear probabilistic interpretation. In our contribution: (1) we formulate the theoretical foundations of interpretable probabilistic password strength meters; (2) we describe how they can be implemented via an efficient and lightweight deep learning framework suitable for client-side operability.Comment: An abridged version of this paper appears in the proceedings of the 25th European Symposium on Research in Computer Security (ESORICS) 202

MPI Enhancements in John the Ripper

John the Ripper (JtR) is an open source software package commonly used by system administrators to enforce password policy. JtR is designed to attack (i.e., crack) passwords encrypted in a wide variety of commonly used formats. While parallel implementations of JtR exist, there are several limitations to them. This research reports on two distinct algorithms that enhance this password cracking tool using the Message Passing Interface. The first algorithm is a novel approach that uses numerous processors to crack one password by using an innovative approach to workload distribution. In this algorithm the candidate password is distributed to all participating processors and the word list is divided based on probability so that each processor has the same likelihood of cracking the password while eliminating overlapping operations. The second algorithm developed in this research involves dividing the passwords within a password file equally amongst available processors while ensuring load-balanced and fault tolerant behavior. This paper describes John the Ripper, the design of these two algorithms and preliminary results. Given the same amount of time, the original JtR can crack 29 passwords, whereas our algorithms 1 and 2 can crack an additional 35 and 45 passwords respectively

Investigating the Distribution of Password Choices

In this paper we will look at the distribution with which passwords are chosen. Zipf's Law is commonly observed in lists of chosen words. Using password lists from four different on-line sources, we will investigate if Zipf's law is a good candidate for describing the frequency with which passwords are chosen. We look at a number of standard statistics, used to measure the security of password distributions, and see if modelling the data using Zipf's Law produces good estimates of these statistics. We then look at the the similarity of the password distributions from each of our sources, using guessing as a metric. This shows that these distributions provide effective tools for cracking passwords. Finally, we will show how to shape the distribution of passwords in use, by occasionally asking users to choose a different password