Adaptive authentication enables smartphones and enterprise apps to decide when and how to authenticate users based on contextual and behavioral factors. In practice, a system may employ multiple policies to adapt its authentication mechanisms and access controls to various scenarios. However, existing approaches suffer from contradictory or insecure adaptations, which may enable attackers to bypass the authentication system. Besides, most existing approaches are inflexible and do not provide desirable access controls. We design and build a multi-stage risk-aware adaptive authentication and access control framework (MRAAC), which provides the following novel contributions: Multi-stage: MRAAC organizes adaptation policies in multiple stages to handle different risk types and progressively adapts authentication mechanisms based on context, resource sensitivity, and user authenticity. Appropriate access control: MRAAC provides libraries to enable sensitive apps to manage the availability of their in-app resources based on MRAAC’s risk awareness. Extensible: While existing proposals are tailored to cater to a single use case, MRAAC supports a variety of use cases with custom risk models. We exemplify these advantages of MRAAC by deploying it for three use cases: an enhanced version of Android Smart Lock, guest-aware continuous authentication, and corporate app for BYOD. We conduct experiments to quantify the CPU, memory, latency, and battery performance of MRAAC. Our evaluation shows that MRAAC enables various stakeholders (device manufacturers, enterprise and secure app developers) to provide complex adaptive authentication workflows on COTS Android with low processing and battery overhead.

Electronics repair and service providers offer a range of services to computing device owners across North America—from software installation to hardware repair. Device owners obtain these services and leave their device along with their access credentials at the mercy of technicians, which leads to privacy concerns for owners’ personal data. We conduct a comprehensive four-part study to measure the state of privacy in the electronics repair industry. First, through a field study with 18 service providers, we uncover that most service providers do not have any privacy policy or controls to safeguard device owners’ personal data from snooping by technicians. Second, we drop rigged devices for repair at 16 service providers and collect data on widespread privacy violations by technicians, including snooping on personal data, copying data off the device, and removing tracks of snooping activities. Third, we conduct an online survey (n=112) to collect data on customers’ experiences when getting devices repaired. Fourth, we invite a subset of survey respondents (n=30) for semi-structured interviews to establish a deeper understanding of their experiences and identify potential solutions to curtail privacy violations by technicians. We apply our findings to discuss possible controls and actions different stakeholders and regulatory agencies should take to improve the state of privacy in the repair industry.

The uniqueness of behavioral biometrics (e.g., voice or keystroke patterns) has been challenged by recent works. Statistical attacks have been proposed that infer general population statistics and target behavioral biometrics against a particular victim. We show that despite their success, these approaches require several attempts for successful attacks against different biometrics due to the different nature of overlap in users’ behavior for these biometrics. Furthermore, no mechanism has been proposed to date that detects statistical attacks. In this work, we propose a new hypervolumes-based statistical attack and show that unlike existing methods, it (1) is successful against a variety of biometrics, (2) is successful against more users, and (3) requires fewest attempts for successful attacks. More specifically, across five diverse biometrics, for the first attempt, on average our attack is 18 percentage points more successful than the second best (37% vs. 19%). Similarly, for the fifth attack attempt, on average our attack is 18 percentage points more successful than the second best (67% vs. 49%). We propose and evaluate a mechanism that can detect the more devastating statistical attacks. False rejects in biometric systems are common, and by distinguishing statistical attacks from false rejects, our defense improves usability and security. The evaluation of the proposed detection mechanism shows its ability to detect on average 94% of the tested statistical attacks with an average probability of 3% to detect false rejects as a statistical attack. Given the serious threat posed by statistical attacks to biometrics that are used today (e.g., voice), our work highlights the need for defending against these attacks.

Personal Identification Numbers (PINs) are widely used as an access control mechanism for digital assets (e.g., smartphones), financial assets (e.g., ATM cards), and physical assets (e.g., locks for garage doors or homes). Using semi-structured interviews (n=35), participants reported on PIN usage for different types of assets, including how users choose, share, inherit, and reuse PINs, as well as behaviour following the compromise of a PIN. We find that memorability is the most important criterion when choosing a PIN, more so than security or concerns of reuse. Updating or changing a PIN is very uncommon, even when a PIN is compromised. Participants reported sharing PINs for one type of asset with acquaintances but inadvertently reused them for other assets, thereby subjecting themselves to potential risks. Participants also reported using PINs originally set by previous homeowners for physical devices (e.g., alarm or keypad door entry systems). While aware of the risks of not updating PINs, this did not always deter participants from using inherited PINs, as they were often missing instructions on how to update them. Given the expected increase in PIN-protected assets (e.g., loyalty cards, smart locks, and web apps), we provide suggestions and future research directions to better support users with multiple digital and non-digital assets and more secure human-device interaction when utilizing PINs.

Keystroke behaviour-based authentication employs the unique typing behaviour of users to authenticate them. Recent such proposals for virtual keyboards on smartphones employ diverse temporal, contact, and spatial features to achieve over 95% accuracy. Consequently, they have been suggested as a second line of defense with text-based password authentication. We show that a state-of-the-art keystroke behaviour-based authentication scheme is highly vulnerable against mimicry attacks. While previous research used training interfaces to attack physical keyboards, we show that this approach has limited effectiveness against virtual keyboards. This is mainly due to the large number of diverse features that the attacker needs to mimic for virtual keyboards. We address this challenge by developing an augmented reality-based app that resides on the attacker’s smartphone and leverages computer vision and keystroke data to provide real-time guidance during password entry on the victim’s phone. In addition, we propose an audiovisual attack in which the attacker overlays transparent film printed with spatial pointers on the victim’s device and uses audio cues to match the temporal behaviour of the victim. Both attacks require neither tampering or installing software on the victim’s device nor specialized hardware. We conduct experiments with 30 users to mount over 400 mimicry attacks. We show that our methods enable an attacker to mimic keystroke behaviour on virtual keyboards with little effort. We also demonstrate the extensibility of our augmented reality-based technique by successfully mounting mimicry attacks on a swiping behaviour-based continuous authentication system.

We evaluate the efficacy of shoulder surfing defenses for PIN-based authentication systems. We find tilting the device away from the observer, a widely adopted defense strategy, provides limited protection. We also evaluate a recently proposed defense incorporating an "invisible pressure component" into PIN entry. Contrary to earlier claims, our results show this provides little defense against malicious insider attacks. Observations during the study uncover successful attacker strategies for reconstructing a victim's PIN when faced with a tilt defense. Our evaluations identify common misconceptions regarding shoulder surfing defenses, and highlight the need to educate users on how to safeguard their credentials from these attacks.

We develop an augmented reality-based app that resides on the attacker's smartphone and leverages computer vision and raw input data to provide real-time mimicry attack guidance on the victim's phone. Our approach does not require tampering or installing software on the victim's device, or specialized hardware. The app is demonstrated by attacking keystroke dynamics, a method leveraging the unique typing behaviour of users to authenticate them on a smartphone, which was previously thought to be hard to mimic. In addition, we propose a low-tech AR-like audiovisual method based on spatial pointers on a transparent film and audio cues. We conduct experiments with 31 participants and mount over 400 attacks to show that our methods enable attackers to successfully bypass keystroke dynamics for 87% of the attacks after an average mimicry training of four minutes. Our AR-based method can be extended to attack other input behaviour-based biometrics. While the particular attack we describe is relatively narrow, it is a good example of using AR guidance to enable successful mimicry of user behaviour---an approach of increasing concern as AR functionality becomes more commonplace.

Touch input implicit authentication (``touch IA'') employs behavioural biometrics like touch location and pressure to continuously and transparently authenticate smartphone users. We provide the first ever evaluation of targeted mimicry attacks on touch IA and show that it fails against shoulder surfing and offline training attacks. Based on experiments with three diverse touch IA schemes and 256 unique attacker-victim pairs, we show that shoulder surfing attacks have a bypass success rate of 84% with the majority of successful attackers observing the victim's behaviour for less than two minutes. Therefore, the accepted assumption that shoulder surfing attacks on touch IA are infeasible due to the hidden nature of some features is incorrect. For offline training attacks, we created an open-source training app for attackers to train on their victims' touch data. With this training, attackers achieved bypass success rates of 86%, even with only partial knowledge of the underlying features used by the IA scheme. Previous work failed to find these severe vulnerabilities due to its focus on random, non-targeted attacks. Our work demonstrates the importance of considering targeted mimicry attacks to evaluate the security of an implicit authentication scheme. Based on our results, we conclude that touch IA is unsuitable from a security standpoint.

Possession of modern smartphones is becoming increasingly ubiquitous, and with this rise in usage comes a rise in the amount of sensitive data being stored on them. Despite this, the high-frequency, low-duration nature of the average smartphone session makes passwords or PIN-locks even less usable than in the desktop context. To combat these issues, implicit authentication (IA) schemes can be developed and deployed to smartphones. IA schemes continuously authenticate users by profiling their behaviour using the variety of sensors prevalent on the phones, such as touchscreens and accelerometers. When a non-owner acquires the device and attempts to access sensitive data on it, the IA scheme recognizes the difference in behaviour and automatically ejects the attacker from the system. In particularly sensitive contexts, IA schemes can also be deployed as a secondary defence mechanism on top of explicit authentication, providing layered security in the event of, for example, a shoulder-surfing attack compromising the device's PIN or an operating system vulnerability allowing its bypass. In this work, we evaluate existing proposals for IA schemes using different behavioural feature sets, and evaluate them against real-world data to show when they are (and are not) useful. We have implemented them in an easily extensible open source framework for the Android operating system called Itus, which allows other researchers to iteratively improve on the existing mechanisms for performing IA. Itus performs IA at the app level, which we have shown allows app developers to selectively protect sensitive data while decreasing the impact on battery life and device performance, and at the same time obtaining better detection accuracy for the IA scheme being invoked.

Security and usability issues with pass-locks on mobile devices have prompted researchers to develop implicit authentication (IA) schemes, which continuously and transparently authenticate users using behavioural biometrics. Contemporary IA schemes proposed by the research community are challenging to deploy, and there is a need for a framework that supports: different behavioural classifiers, given that different apps have different requirements; app developers using IA without becoming domain experts; and real-time classification on resource-constrained mobile devices. We present Itus, an IA framework for Android that allows the research community to improve IA schemes incrementally, while allowing app developers to adopt these improvements at their own pace.