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Register a new userComparing Video Based Shoulder Surfing with Live Simulation
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Added by
Adam Aviv
Publication date
2018
fields
Informatics Engineering
and research's language is
English
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We analyze the claims that video recreations of shoulder surfing attacks offer a suitable alternative and a baseline, as compared to evaluation in a live setting. We recreated a subset of the factors of a prior video-simulation experiment conducted by Aviv et al. (ACSAC 2017), and model the same scenario using live participants ($n=36$) instead (i.e., the victim and attacker were both present). The live experiment confirmed that for Androids graphical patterns video simulation is consistent with the live setting for attacker success rates. However, both 4- and 6-digit PINs demonstrate statistically significant differences in attacker performance, with live attackers performing as much 1.9x better than in the video simulation. The security benefits gained from removing feedback lines in Androids graphical patterns are also greatly diminished in the live setting, particularly under multiple attacker observations, but overall, the data suggests that video recreations can provide a suitable baseline measure for attacker success rate. However, we caution that researchers should consider that these baselines may greatly underestimate the threat of an attacker in live settings.
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Given the nature of mobile devices and unlock procedures, unlock authentication is a prime target for credential leaking via shoulder surfing, a form of an observation attack. While the research community has investigated solutions to minimize or prevent the threat of shoulder surfing, our understanding of how the attack performs on current systems is less well studied. In this paper, we describe a large online experiment (n=1173) that works towards establishing a baseline of shoulder surfing vulnerability for current unlock authentication systems. Using controlled video recordings of a victim entering in a set of 4- and 6-length PINs and Android unlock patterns on different phones from different angles, we asked participants to act as attackers, trying to determine the authentication input based on the observation. We find that 6-digit PINs are the most elusive attacking surface where a single observation leads to just 10.8% successful attacks, improving to 26.5% with multiple observations. As a comparison, 6-length Android patterns, with one observation, suffered 64.2% attack rate and 79.9% with multiple observations. Removing feedback lines for patterns improves security from 35.3% and 52.1% for single and multiple observations, respectively. This evidence, as well as other results related to hand position, phone size, and observation angle, suggests the best and worst case scenarios related to shoulder surfing vulnerability which can both help inform users to improve their security choices, as well as establish baselines for researchers.
Shoulder-surfing is a known risk where an attacker can capture a password by direct observation or by recording the authentication session. Due to the visual interface, this problem has become exacerbated in graphical passwords. There have been some graphical schemes resistant or immune to shoulder-surfing, but they have significant usability drawbacks, usually in the time and effort to log in. In this paper, we propose and evaluate a new shoulder-surfing resistant scheme which has a desirable usability for PDAs. Our inspiration comes from the drawing input method in DAS and the association mnemonics in Story for sequence retrieval. The new scheme requires users to draw a curve across their password images orderly rather than click directly on them. The drawing input trick along with the complementary measures, such as erasing the drawing trace, displaying degraded images, and starting and ending with randomly designated images provide a good resistance to shouldersurfing. A preliminary user study showed that users were able to enter their passwords accurately and to remember them over time.
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