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Cross-Layer Attack and Defense in Cognitive Radio Networks

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The Problem

The existing research on security issues in wireless networks mainly focuses on attack and defense in individual network layers. However, the attackers do not necessarily restrict themselves within the boundaries of network layers. In this project, we raise the concern that smart attacker can launch several attacks in different layers coordinately, which is referred as the cross-layer attack in our project. Can attackers significantly increase the damage or reduce the risk of being detected by launching the cross-layer attacker? What are the effective defense strategies? We aim to answer these questions.


We gain insights of cross-layer attacks by investigating cognitive radio networks as an example. We choose the reporting false sensing data attack in PHY layer and the small-back-off-window attack in MAC layer. The goal of the attack is to reduce channel utilization. Particularly, we propose a cross-layer attack in which attackers can launch several attacks in different layers coordinately, propose a cross-layer defense architecture, which relies on trust evaluation in individual layers and trust fusion across multiple-layers, modify/develop anomaly detection in individual layers, and design trust fusion algorithm that considers the diverse performance of anomaly detection in different layers.


We demonstrated the significant increase of the attackers’ power due to cross-layer attack strategies, as well as the effectiveness of cross-layer defense in terms of reducing maximum damage caused by attackers.


This research is partially supported by NSF Award # 0831315.