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Shrestha, Babins, Mohamed, Manar, Saxena, Nitesh.  2019.  ZEMFA: Zero-Effort Multi-Factor Authentication based on Multi-Modal Gait Biometrics. 2019 17th International Conference on Privacy, Security and Trust (PST). :1–10.
In this paper, we consider the problem of transparently authenticating a user to a local terminal (e.g., a desktop computer) as she approaches towards the terminal. Given its appealing usability, such zero-effort authentication has already been deployed in the real-world where a computer terminal or a vehicle can be unlocked by the mere proximity of an authentication token (e.g., a smartphone). However, existing systems based on a single authentication factor contains one major security weakness - unauthorized physical access to the token, e.g., during lunch-time or upon theft, allows the attacker to have unfettered access to the terminal. We introduce ZEMFA, a zero-effort multi-factor authentication system based on multiple authentication tokens and multi-modal behavioral biometrics. Specifically, ZEMFA utilizes two types of authentication tokens, a smartphone and a smartwatch (or a bracelet) and two types of gait patterns captured by these tokens, mid/lower body movements measured by the phone and wrist/arm movements captured by the watch. Since a user's walking or gait pattern is believed to be unique, only that user (no impostor) would be able to gain access to the terminal even when the impostor is given access to both of the authentication tokens. We present the design and implementation of ZEMFA. We demonstrate that ZEMFA offers a high degree of detection accuracy, based on multi-sensor and multi-device fusion. We also show that ZEMFA can resist active attacks that attempt to mimic a user's walking pattern, especially when multiple devices are used.
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Mohamed, Manar, Shrestha, Babins, Saxena, Nitesh.  2016.  SMASheD: Sniffing and Manipulating Android Sensor Data. Proceedings of the Sixth ACM Conference on Data and Application Security and Privacy. :152–159.

The current Android sensor security model either allows only restrictive read access to sensitive sensors (e.g., an app can only read its own touch data) or requires special install-time permissions (e.g., to read microphone, camera or GPS). Moreover, Android does not allow write access to any of the sensors. Sensing-based security applications therefore crucially rely upon the sanity of the Android sensor security model. In this paper, we show that such a model can be effectively circumvented. Specifically, we build SMASheD, a legitimate framework under the current Android ecosystem that can be used to stealthily sniff as well as manipulate many of the Android's restricted sensors (even touch input). SMASheD exploits the Android Debug Bridge (ADB) functionality and enables a malicious app with only the INTERNET permission to read, and write to, multiple different sensor data files at will. SMASheD is the first framework, to our knowledge, that can sniff and manipulate protected sensors on unrooted Android devices, without user awareness, without constant device-PC connection and without the need to infect the PC. The primary contributions of this work are two-fold. First, we design and develop the SMASheD framework. Second, as an offensive implication of the SMASheD framework, we introduce a wide array of potentially devastating attacks. Our attacks against the touchsensor range from accurately logging the touchscreen input (TouchLogger) to injecting touch events for accessing restricted sensors and resources, installing and granting special permissions to other malicious apps, accessing user accounts, and authenticating on behalf of the user –- essentially almost doing whatever the device user can do (secretively). Our attacks against various physical sensors (motion, position and environmental) can subvert the functionality provided by numerous existing sensing-based security applications, including those used for(continuous) authentication, and authorization.

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Mohamed, Manar, Saxena, Nitesh.  2016.  Gametrics: Towards Attack-resilient Behavioral Authentication with Simple Cognitive Games. Proceedings of the 32Nd Annual Conference on Computer Security Applications. :277–288.

Authenticating a user based on her unique behavioral bio-metric traits has been extensively researched over the past few years. The most researched behavioral biometrics techniques are based on keystroke and mouse dynamics. These schemes, however, have been shown to be vulnerable to human-based and robotic attacks that attempt to mimic the user's behavioral pattern to impersonate the user. In this paper, we aim to verify the user's identity through the use of active, cognition-based user interaction in the authentication process. Such interaction boasts to provide two key advantages. First, it may enhance the security of the authentication process as multiple rounds of active interaction would serve as a mechanism to prevent against several types of attacks, including zero-effort attack, expert trained attackers, and automated attacks. Second, it may enhance the usability of the authentication process by actively engaging the user in the process. We explore the cognitive authentication paradigm through very simplistic interactive challenges, called Dynamic Cognitive Games, which involve objects floating around within the images, where the user's task is to match the objects with their respective target(s) and drag/drop them to the target location(s). Specifically, we introduce, build and study Gametrics ("Game-based biometrics"), an authentication mechanism based on the unique way the user solves such simple challenges captured by multiple features related to her cognitive abilities and mouse dynamics. Based on a comprehensive data set collected in both online and lab settings, we show that Gametrics can identify the users with a high accuracy (false negative rates, FNR, as low as 0.02) while rejecting zero-effort attackers (false positive rates, FPR, as low as 0.02). Moreover, Gametrics shows promising results in defending against expert attackers that try to learn and later mimic the user's pattern of solving the challenges (FPR for expert human attacker as low as 0.03). Furthermore, we argue that the proposed biometrics is hard to be replayed or spoofed by automated means, such as robots or malware attacks.