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

TL;DR

This paper introduces a novel framework for detecting and mitigating complex cross-layer attacks in wireless networks, utilizing Bayesian learning for detection and a performance-security trade-off scheme, validated through simulations and real-world tests.

Contribution

The paper presents a new comprehensive framework specifically designed to detect and mitigate cross-layer attacks in wireless networks, addressing a gap in existing research.

Findings

Bayesian detection effectively identifies stealthy cross-layer attacks.

The mitigation scheme balances security and network performance.

Experimental results confirm the framework's effectiveness in real-world scenarios.

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.