Deauthentication is an important component of any authentication system. The widespread use of computing devices in daily life has underscored the need for zero-effort deauthentication schemes. However, the quest for eliminating user effort may lead to hidden security flaws in the authentication schemes.As a case in point, we investigate a prominent zero-effort deauthentication scheme, called ZEBRA, which provides an interesting and a useful solution to a difficult problem as demonstrated in the original paper. We identify a subtle incorrect assumption in its adversary model that leads to a fundamental design flaw. We exploit this to break the scheme with a class of attacks that are much easier for a human to perform in a realistic adversary model, compared to the naïve attacks studied in the ZEBRA paper. For example, one of our main attacks, where the human attacker has to opportunistically mimic only the victim's keyboard typing activity at a nearby terminal, is significantly more successful compared to the naïve attack that requires mimicking keyboard and mouse activities as well as keyboardmouse movements. Further, by understanding the design flaws in ZEBRA as cases of tainted input, we show that we can draw on well-understood design principles to improve ZEBRA's security.
Abstract-Unsafe websites consist of malicious as well as inappropriate sites, such as those hosting questionable or offensive content. Website reputation systems are intended to help ordinary users steer away from these unsafe sites. However, the process of assigning safety ratings for websites typically involves humans. Consequently it is time consuming, costly and not scalable. This has resulted in two major problems: (i) a significant proportion of the web space remains unrated and (ii) there is an unacceptable time lag before new websites are rated.In this paper, we show that by leveraging structural and content-based properties of websites, it is possible to reliably and efficiently predict their safety ratings, thereby mitigating both problems. We demonstrate the effectiveness of our approach using four datasets of up to 90,000 websites. We use ratings from Web of Trust (WOT), a popular crowdsourced web reputation system, as ground truth. We propose a novel ensemble classification technique that makes opportunistic use of available structural and content properties of webpages to predict their eventual ratings in two dimensions used by WOT: trustworthiness and child safety. Ours is the first classification system to predict such subjective ratings and the same approach works equally well in identifying malicious websites. Across all datasets, our classification performs well with average F1-score in the 74-90% range.
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