Password & Auth 2

05 April 2017, 11:00, A6-004

Session chair: Gene Tsudik, University of California, Irvine, USA

Understanding Human-Chosen PINs: Characteristics, Distribution and Security

Ding Wang, Qianchen Gu, Xinyi Huang, Ping Wang

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Personal Identification Numbers (PINs) are ubiquitously used in embedded computing systems where user input interfaces are constrained. Yet, little attention has been paid to this important kind of authentication credentials, especially for 6-digit PINs which dominate in Asian countries and are gaining popularity worldwide. Unsurprisingly, many fundamental questions (e.g., what’s the distribution that human-chosen PINs follow?) remain as intact as about fifty years ago when they first arose. In this work, we conduct a systematic investigation into the characteristics, distribution and security of both 4-digit PINs and 6-digit PINs that are chosen by English users and Chinese users. Particularly, we, for the first time, perform a comprehensive comparison of the PIN characteristics and security between these two distinct user groups. Our results show that there are great differences in PIN choices between these two groups of users, a small number of popular patterns prevail in both groups, and surprisingly, over 50% of every PIN datasets can be accounted for by just the top 5%~8% most popular PINs. What’s disturbing is the observation that, as online guessing is a much more serious threat than offline guessing in the current PIN-based systems, longer PINs only attain marginally improved security: human-chosen 4-digit PINs can offer about 6.6 bits of security against online guessing and 8.4 bits of security against offline guessing, and this figure for 6-digit PINs is 7.2 bits and 13.2 bits, respectively. We, for the first time, reveal that Zipf’s law is likely to exist in PINs. Despite distinct language/cultural backgrounds, both user groups choose PINs with almost the same Zipf distribution function, and such Zipf PIN-distribution from one source (about which we may know little information) can be well predicted by real-world attackers by running Markov-Chains with PINs from another known source. Our Zipf theory would have foundational implications for analyzing PIN-based protocols and for designing PIN creation policies, while our security measurements provide guidance for bank agencies and financial authorities that are planning to conduct PIN migration from 4-digits to 6-digits.

Evaluating Behavioral Biometrics for Continuous Authentication: Challenges and Metrics

Simon Eberz, Kasper B. Rasmussen, Vincent Lenders, Ivan Martinovic

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In recent years, behavioral biometrics have become a popular approach to support continuous authentication systems. Most generally, a continuous authentication system can make two types of errors: false rejects and false accepts. Based on this, the most commonly reported metrics to evaluate systems are the False Reject Rate (FRR) and False Accept Rate (FAR). However, most papers only report the mean of these measures with little attention paid to their distribution. This is problematic as systematic errors allow attackers to perpetually escape detection while random errors are less severe. Using 16 biometric datasets we show that these systematic errors are very common in the wild. We show that some biometrics (such as eye movements) are particularly prone to systematic errors, while others (such as touchscreen inputs) show more even error distributions. Our results also show that the inclusion of some distinctive features lowers average error rates but significantly increases the prevalence of systematic errors. As such, blind optimization of the mean EER (through feature engineering or selection) can some- times lead to lower security. Following this result we propose the Gini Coefficient (GC) as an additional metric to accurately capture different error distributions. We demonstrate the usefulness of this measure both to compare different systems and to guide researchers during feature selection. In addition to the selection of features and classifiers, some non- functional machine learning methodologies also affect error rates. The most notable examples of this are the selection of training data and the attacker model used to develop the negative class. 13 out of the 25 papers we analyzed either include imposter data in the negative class or randomly sample training data from the entire dataset, with a further 6 not giving any information on the methodology used. Using real-world data we show that both of these decisions lead to significant underestimation of error rates by 63% and 81%, respectively. This is an alarming result, as it suggests that researchers are either unaware of the magnitude of these effects or might even be purposefully attempting to over-optimize their EER without actually improving the system.

Pass-O: A Proposal to Improve the Security of Pattern Unlock Scheme

Harshal Tupsamudre, Vijayanand Banahatti, Sachin Lodha, Ketan Vyas

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The graphical pattern unlock scheme which requires users to connect a minimum of 4 nodes on 3X3 grid is one of the most popular authentication mechanism on mobile devices. However prior research suggests that users’ pattern choices are highly biased and hence vulnerable to guessing attacks. Moreover, 3X3 pattern choices are devoid of features such as longer stroke lengths, direction changes and intersections that are considered to be important in preventing shoulder-surfing attacks. We attribute these insecure practices to the geometry of the grid and its complicated drawing rules which prevent users from realising the full potential of graphical passwords. In this paper, we propose and explore an alternate circular layout referred to as Pass-O which unlike grid layout allows connection between any two nodes, thus simplifying the pattern drawing rules. Consequently, Pass-O produces a theoretical search space of 9,85,824, almost 2.5 times greater than 3X3 grid layout. We compare the security of 3X3 and Pass-O patterns theoretically as well as empirically. Theoretically, Pass-O patterns are uniform and have greater visual complexity due to large number of intersections. To perform empirical analysis, we conduct a large-scale web-based user study and collect more than 1,23,000 patterns from 21,053 users. After examining user-chosen 3X3 and Pass-O patterns across different metrics such as pattern length, stroke length, start point, end point, repetitions, number of direction changes and intersections, we find that Pass-O patterns are much more secure than 3X3 patterns.