Detection of primary user emulation attack in cognitive radio environment

Cognitive Radio Network (CRN) is an emerging technology used to solve spectrum shortage problems in wireless communications. In CRN, unlicensed secondary users (SUs) and licensed primary users (PUs) use spectrum resources at the same time by avoiding any interference from SUs. However, the spectrum sensing process in CRN is often disturbed by a security issue known as the Primary User Emulation Attack (PUEA). PUEA is one of the main security issues that disrupt the whole activity of CRN. The attacker transmits false information to interrupt the spectrum sensing process of CRN, which leads to poor usage of the spectrum. The proposed study uses a proficient Time Difference of Arrival (TDOA) based localization method using the Differential Evolution (DE) algorithm to identify the PUEA in CRNs. The DE algorithm is used to solve the objective function of TDOA values. The proposed methodology constructs a CRN and identifies PUEA. The proposed method aims to sense and localize PUEA efficiently. Mean Square Error (MSE) is the performance evaluation parameter that is used to measure the accuracy of the proposed technique. The results are compared with the previously proposed Firefly optimization algorithm (FA). It is clear from the results that DE converges faster than FA.

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An Emergent Self-Attention LSTM Module-Based Malicious User Detection in Cognitive Radio Networks

Jothiraj,

Brindha,

Persis Jeffrey

2024

Advances in Wireless Technologies and Telecommunication

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Cognitive radio networks (CRNs) represent a dynamic and intelligent paradigm for efficient spectrum utilization by allowing unlicensed users, known as secondary users, to opportunistically access underutilized spectrum bands. However, the open and shared nature of the spectrum in CRNs exposes them to various security threats and attacks. In this context, the need for robust intrusion detection mechanisms becomes paramount to safeguard the integrity of the network. This work proposes a novel approach for detecting attackers in cognitive radio networks using a combination of self-attention mechanism and long short-term memory (LSTM) networks. The self-attention LSTM model is designed to capture complex temporal dependencies and spatial correlations in the dynamic spectrum access patterns, making it well-suited for the inherent variability and uncertainty in CRNs. The proposed approach is evaluated using real-world CRN datasets, considering various attack scenarios such as spectrum sensing data falsification and malicious spectrum access.

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Detection of primary user emulation attack in cognitive radio environment

Cited by 3 publications

References 5 publications

Accurate Primary User Emulation Attack (PUEA) Detection in Cognitive Radio Network using KNN and ANN Classifier

Accurate Primary User Emulation Attack (PUEA) Detection in Cognitive Radio Network using KNN and ANN Classifier

Detection of Primary User Emulation Attack Using the Differential Evolution Algorithm in Cognitive Radio Networks

An Emergent Self-Attention LSTM Module-Based Malicious User Detection in Cognitive Radio Networks

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