A Machine Learning Approach for Backoff Manipulation Attack Detection in Cognitive Radio

Abstract

Cognitive radio (CR) is a promising paradigm that comes to address the scarcity of the radio spectrum by providing opportunistic utilization of the underutilized licensed channels to attain higher spectrum efficiency. The efficient use of the vacant portion of the spectrum depends directly on the medium access control (MAC) layer that coordinates fairly the access of the CR nodes to the idle spectrum. However, the MAC layer is vulnerable to several attacks driven by malicious nodes. One of those attacks is the backoff manipulation attack (BMA), where the selfish attacker deviates from the defined contention mechanism to gain inequitable access to the available channels. This unfair access presents some specific characteristics of an attacker, which can be considered as an input to the supervised machine learning algorithm for classification. In this paper, we propose a support vector machine (SVM) based model in order to distinctively identify the attacker depending on the throughput and the average packet delay to classify/predict an eventual attack. Finally, theoretical predictions and simulation results are presented to validate the proposed framework while giving useful insights into CR systems' performance, vulnerable to BMA attacks.