Biblio

Detecting fake accounts (sybils) in online social networks (OSNs) is vital to protect OSN operators and their users from various malicious activities. Typical graph-based sybil detection (a mainstream methodology) assumes that sybils can make friends with only a limited (or small) number of honest users. However, recent evidences showed that this assumption does not hold in real-world OSNs, leading to low detection accuracy. To address this challenge, we explore users' activities to assist sybil detection. The intuition is that honest users are much more selective in choosing who to interact with than to befriend with. We first develop the social and activity network (SAN), a two-layer hyper-graph that unifies users' friendships and their activities, to fully utilize users' activities. We also propose a more practical sybil attack model, where sybils can launch both friendship attacks and activity attacks. We then design Sybil SAN to detect sybils via coupling three random walk-based algorithms on the SAN, and prove the convergence of Sybil SAN. We develop an efficient iterative algorithm to compute the detection metric for Sybil SAN, and derive the number of rounds needed to guarantee the convergence. We use "matrix perturbation theory" to bound the detection error when sybils launch many friendship attacks and activity attacks. Extensive experiments on both synthetic and real-world datasets show that Sybil SAN is highly robust against sybil attacks, and can detect sybils accurately under practical scenarios, where current state-of-art sybil defenses have low accuracy.

Deception has been widely considered in literature as an effective means of enhancing security protection when the defender holds some private information about the ongoing rivalry unknown to the attacker. However, most of the existing works on deception assume static environments and thus consider only myopic deception, while practical security games between the defender and the attacker may happen in dynamic scenarios. To better exploit the defender's private information in dynamic environments and improve security performance, a stochastic deception game (SDG) framework is developed in this work to enable the defender to conduct foresighted deception. To solve the proposed SDG, a new iterative algorithm that is provably convergent is developed. A corresponding learning algorithm is developed as well to facilitate the defender in conducting foresighted deception in unknown dynamic environments. Numerical results show that the proposed foresighted deception can offer a substantial performance improvement as compared to the conventional myopic deception.

We present a new method for mitigating wall return and a new greedy algorithm for detecting stationary targets after wall clutter has been cancelled. Given limited measurements of a stepped-frequency radar signal consisting of both wall and target return, our objective is to detect and localize the potential targets. Modulated Discrete Prolate Spheroidal Sequences (DPSS's) form an efficient basis for sampled bandpass signals. We mitigate the wall clutter efficiently within the compressive measurements through the use of a bandpass modulated DPSS basis. Then, in each step of an iterative algorithm for detecting the target positions, we use a modulated DPSS basis to cancel nearly all of the target return corresponding to previously selected targets. With this basis, we improve upon the target detection sensitivity of a Fourier-based technique.