Recent hardware advances have led to the development and consumerization of mobile devices, which mainly include smartphones and various wearable devices. To protect the privacy of users, various user authentication mechanisms have been proposed. In particular, biometrics has been widely used for multi-factor authentication. However, biometrics-based authentication mechanisms usually require costly sensors deployed on devices, and rely on explicit user input and Internet connection for performing user authentication. In this article, we propose a system, called RISKCOG, which can authenticate the ownership of mobile devices unobtrusively and in a real-time manner by adopting a learning-based approach. Unlike previous studies on user authentication, for cross-platform deployment, maximum user privacy protection, and unobtrusive authentication, RISKCOG only relies on those widely available and privacy-insensitive motion sensors to capture the data related to the users' daily device usage. It requires no users' explicit input and has no requirement on the users' motion state or the device placement. RISKCOG is also usable in the environment without Internet access by performing offline user identity verification. We conduct comprehensive experiments on smartphones and smartwatches, which show that RISKCOG can authenticate device users rapidly and with high accuracy.

Android has provided dynamic code loading (DCL) since API level one. DCL allows an app developer to load additional code at runtime. DCL raises numerous challenges with regards to security and accountability analysis of apps. While previous studies have investigated DCL on Android, in this paper we formulate and answer three critical questions that are missing from previous studies: (1) Where does the loaded code come from (remotely fetched or locally packaged), and who is the responsible entity to invoke its functionality? (2) In what ways is DCL utilized to harden mobile apps, specifically, application obfuscation? (3) What are the security risks and implications that can be found from DCL in off-the-shelf apps? We design and implement DyDroid, a system which uses both dynamic and static analysis to analyze dynamically loaded code. Dynamic analysis is used to automatically exercise apps, capture DCL behavior, and intercept the loaded code. Static analysis is used to investigate malicious behavior and privacy leakage in that dynamically loaded code. We have used DyDroid to analyze over 46K apps with little manual intervention, allowing us to conduct a large-scale measurement to investigate five aspects of DCL, such as source identification, malware detection, vulnerability analysis, obfuscation analysis, and privacy tracking analysis. We have several interesting findings. (1) 27 apps are found to violate the content policy of Google Play by executing code downloaded from remote servers. (2) We determine the distribution, pros/cons, and implications of several common obfuscation methods, including DEX encryption/loading. (3) DCL's stealthiness enables it to be a channel to deploy malware, and we find 87 apps loading malicious binaries which are not detected by existing antivirus tools. (4) We found 14 apps that are vulnerable to code injection attacks due to dynamically loading code which is writable by other apps. (5) DCL is mainly used by third-party SDKs, meaning that app developers may not know what sort of sensitive functionality is injected into their apps.

The booming popularity of smartphones is partly a result of application markets where users can easily download wide range of third-party applications. However, due to the open nature of markets, especially on Android, there have been several privacy and security concerns with these applications. On Google Play, as with most other markets, users have direct access to natural-language descriptions of those applications, which give an intuitive idea of the functionality including the security-related information of those applications. Google Play also provides the permissions requested by applications to access security and privacy-sensitive APIs on the devices. Users may use such a list to evaluate the risks of using these applications. To best assist the end users, the descriptions should reflect the need for permissions, which we term description-to-permission fidelity. In this paper, we present a system AutoCog to automatically assess description-to-permission fidelity of applications. AutoCog employs state-of-the-art techniques in natural language processing and our own learning-based algorithm to relate description with permissions. In our evaluation, AutoCog outperforms other related work on both performance of detection and ability of generalization over various permissions by a large extent. On an evaluation of eleven permissions, we achieve an average precision of 92.6% and an average recall of 92.0%. Our large-scale measurements over 45,811 applications demonstrate the severity of the problem of low description-to-permission fidelity. AutoCog helps bridge the long-lasting usability gap between security techniques and average users.

Energy efficiency is the core issue in wireless sensor network's scheduling algorithm design, which calls up wide attention from researchers. However, few of the previous works notice the constraints on sensors caused by limited buffer size. It challenges the system's meeting deadlines performance. To alleviate this concern, we propose a scheduling policy: the least energy and free storage remaining first, which extends wireless sensor network's lifetime and provides a high real-time service's quality. To make our strategy more practical, we consider the user's non-cooperative behavior out of self-interests. It increases the system's packet lost in buffer overflow and reduces the energy efficiency. Based on a mixed strategy game model, we show that the non-cooperative game converges to an inefficient Nash Equilibrium, where the spectrum resource is significantly wasted. In order to eliminate user's selfish manner, we design a punishment scheme via a repeated game, which considers the time-variant character of network and can detect non-cooperative action precisely and rapidly.

Game theory has made great contributions to Cognitive Radio (CR) algorithm design and it calls up wide interest of CR researchers, while the considerable iterations and the operation complexity would hinder its implementation. And the evaluation under practical environment is an indispensable step before its application. To alleviate the concerns, we propose a distributed relay selection algorithm based on Stackelberg Game and overcome the challenges caused by the high calculation complexity to make our strategy applicable. Moreover, we utilize a real time testbed to evaluate the CR MAC algorithm, which contains the necessary components: programmable RF layer, software-defined MAC layer and adaptive network layer to fulfill the requirements of CR research. It is based on system-on-chip processors with strong configure ability which can develop large scale network. We make experiments on our platform to reveal the game equilibrium's properties and verify our scheme's performance in improving the capacity at the destination and secondary user's spectrum utilization efficiency.