Taming Cross-Layer Attacks in Wireless Networks: A Bayesian Learning Approach

Abstract

Wireless networks are extremely vulnerable to a plethora of security threats, including eavesdropping, jamming, and spoofing, to name a few. Recently, a number of next-generation cross-layer attacks have been unveiled, which leverage small changes on one network layer to stealthily and significantly compromise another target layer. Since cross-layer attacks are stealthy, dynamic, and unpredictable in nature, novel security techniques are needed. Since models of the environment and attacker's behavior may be hard to obtain in practical scenarios, machine learning techniques become the ideal choice to tackle cross-layer attacks. In this paper, we propose FORMAT, a novel framework to tackle cross-layer security attacks in wireless networks. FORMAT is based on Bayesian learning and made up by a detection and a mitigation component. On one hand, the attack detection component constructs a model of observed evidence to identify stealthy attack activities. On the other hand, the mitigation component uses optimization theory to achieve the desired trade-off between security and performance. The proposed FORMAT framework has been extensively evaluated and compared with existing work by simulations and experiments obtained with a real-world testbed made up by Ettus Universal Software Radio Peripheral (USRP) radios. Results demonstrate the effectiveness of the proposed methodology as FORMAT is able to effectively detect and mitigate the considered cross-layer attacks.