Visible to the public Biblio

Filters: Author is Xiao, B.  [Clear All Filters]
2017
Yao, Y., Xiao, B., Wu, G., Liu, X., Yu, Z., Zhang, K., Zhou, X..  2017.  Voiceprint: A Novel Sybil Attack Detection Method Based on RSSI for VANETs. 2017 47th Annual IEEE/IFIP International Conference on Dependable Systems and Networks (DSN). :591–602.

Vehicular Ad Hoc Networks (VANETs) enable vehicle-to-vehicle (V2V) and vehicle-to-infrastructure (V2I) communications that bring many benefits and conveniences to improve the road safety and drive comfort in future transportation systems. Sybil attack is considered one of the most risky threats in VANETs since a Sybil attacker can generate multiple fake identities with false messages to severely impair the normal functions of safety-related applications. In this paper, we propose a novel Sybil attack detection method based on Received Signal Strength Indicator (RSSI), Voiceprint, to conduct a widely applicable, lightweight and full-distributed detection for VANETs. To avoid the inaccurate position estimation according to predefined radio propagation models in previous RSSI-based detection methods, Voiceprint adopts the RSSI time series as the vehicular speech and compares the similarity among all received time series. Voiceprint does not rely on any predefined radio propagation model, and conducts independent detection without the support of the centralized infrastructure. It has more accurate detection rate in different dynamic environments. Extensive simulations and real-world experiments demonstrate that the proposed Voiceprint is an effective method considering the cost, complexity and performance.

2018
Sahabandu, D., Xiao, B., Clark, A., Lee, S., Lee, W., Poovendran, R..  2018.  DIFT Games: Dynamic Information Flow Tracking Games for Advanced Persistent Threats. 2018 IEEE Conference on Decision and Control (CDC). :1136-1143.
Dynamic Information Flow Tracking (DIFT) has been proposed to detect stealthy and persistent cyber attacks that evade existing defenses such as firewalls and signature-based antivirus systems. A DIFT defense taints and tracks suspicious information flows across the network in order to identify possible attacks, at the cost of additional memory overhead for tracking non-adversarial information flows. In this paper, we present the first analytical model that describes the interaction between DIFT and adversarial information flows, including the probability that the adversary evades detection and the performance overhead of the defense. Our analytical model consists of a multi-stage game, in which each stage represents a system process through which the information flow passes. We characterize the optimal strategies for both the defense and adversary, and derive efficient algorithms for computing the strategies. Our results are evaluated on a realworld attack dataset obtained using the Refinable Attack Investigation (RAIN) framework, enabling us to draw conclusions on the optimal adversary and defense strategies, as well as the effect of valid information flows on the interaction between adversary and defense.