Learning to Detect and Mitigate Cross-layer Attacks in Wireless Networks: Framework and Applications
  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.
