An Adaptive Primary User Emulation Attack Detection Mechanism for Cognitive Radio Networks

TL;DR

This paper proposes an adaptive detection mechanism for primary user emulation attacks in cognitive radio networks, utilizing physical transmission features like power levels to distinguish malicious signals from legitimate primary users.

Contribution

It introduces a novel detection technique that leverages intrinsic transmission features, such as power, which attackers cannot easily mimic, enhancing security in CRNs.

Findings

Effective detection of PUE attacks demonstrated through experiments

Utilizes physical layer features for robust attack identification

Improves security without significant impact on spectrum utilization

Abstract

The proliferation of advanced information technologies (IT), especially the wide spread of Internet of Things (IoTs) makes wireless spectrum a precious resource. Cognitive radio network (CRN) has been recognized as the key to achieve efficient utility of communication bands. Because of the great difficulty, high complexity and regulations in dynamic spectrum access (DSA), it is very challenging to protect CRNs from malicious attackers or selfish abusers. Primary user emulation (PUE) attacks is one type of easy-to-launch but hard-to-detect attacks in CRNs that malicious entities mimic PU signals in order to either occupy spectrum resource selfishly or conduct Denial of Service (DoS) attacks. Inspired by the physical features widely used as the fingerprint of variant electronic devices, an adaptive and realistic PUE attack detection technique is proposed in this paper. It leverages the PU transmission features that attackers are not able to mimic. In this work, the transmission power is selected as one of the hard-to-mimic features due to the intrinsic discrepancy between PUs and attackers, while considering constraints in real implementations. Our experimental results verified the effectiveness and correctness of the proposed mechanism.