A Modified Active Ensemble Neural Network Using Primary User Emulation Attack in Cognitive Radio Network

The growing demand for wireless applications has led to an increasing need for efficient spectrum utilization. Cognitive radio networks (CRNs) emerge as a promising solution to address this demand. CRNs enable unlicensed users to access the available spectrum without causing interference to primary users. However, ensuring the accurate identification of primary user emulation (PUE) attackers is critical due to the sensitivity of these networks. In this paper, an active ensemble neural network-based approach with FlightSpa optimization is employed to enhance the detection of aggressive users. Leveraging neural network (NN) classifiers, the proposed method implicitly captures complex nonlinear relationships between dependent and independent variables, allowing for a comprehensive analysis of network nodes and precise identification of malicious users. Given the network's inherent complexity, accurately identifying malevolent users is a challenging task, which the active neural network effectively addresses. The classifier's parameters are tuned optimally by applying a hybrid FlightSpa optimization strategy, leading to successful optimization and reliable output generation. The performance reveals that the active NN-based FlightSpa optimization achieves impressive results, with detection rates for malicious users in CRNs reaching the highest values of 84.085% for accuracy, 82.351% for sensitivity and 85.820% for specificity with 50 nodes. These outcomes surpass those obtained using earlier techniques, marking a significant advancement in the field of cognitive radio network security.