Title: Learning to Detect and Mitigate Cross-layer Attacks in Wireless Networks: Framework and Applications,
Abstract: Security threats such as jamming and route manipulation can have significant
consequences on the performance of modern wireless networks. To increase the
efficacy and stealthiness of such threats, a number of extremely challenging,
cross-layer attacks have been recently unveiled. Although existing research has
thoroughly addressed many single-layer attacks, the problem of detecting and
mitigating cross-layer attacks still remains unsolved. For this reason, in this
paper we propose a novel framework to analyze and address cross-layer attacks
in wireless networks. Specifically, our framework consists of a detection and a
mitigation component. The attack detection component is based on a Bayesian
learning detection scheme that constructs a model of observed evidence to
identify stealthy attack activities. The mitigation component comprises a
scheme that achieves the desired trade-off between security and performance. We
specialize and evaluate the proposed framework by considering a specific
cross-layer attack that uses jamming as an auxiliary tool to achieve route
manipulation. Simulations and experimental results obtained with a test-bed
made up by USRP software-defined radios demonstrate the effectiveness of the
proposed methodology.