Hierarchical Cat and Mouse based ensemble extreme learning machine for spectrum sensing data falsification attack detection in cognitive radio network

Abstract

While cognitive radio networks (CRNs) provide a promising solution to mitigate the scarcity of the radio spectrum, they are still susceptible to several security threats. Until now, only a few researchers considered the usage of intrusion detection systems (IDSs) to combat the threads against CRNs. In CRN, spectrum sensing is considered as a significant function and collaborative spectrum sensing (CSS) has known to result in better sensing accuracy. However, CSS is vulnerable to Spectrum Sensing Data Falsification (SSDF) attack wherein a node maliciously falsifies the sensing report prior to sending it to the Fusion Centre (FC), with the aim of disrupting the spectrum sensing process. Thus, a new novel machine learning model is significant for detecting of attacks in a CRN. In this paper, a Hierarchical Cat and Mouse Based Ensemble Extreme Learning Machine (HCM-EELM) model has proposed to analyse the security threats in CRN for enhancing the network performance by minimizing the attacks. Also, it is attempted at investigating the viability of machine learning classification for detecting SSDF attack in a binary reporting CRN. History of the sensing reports accumulated at the FC are applied to acquire the temporal characteristics of SU, and thereby establishing the training and testing data-sets. Here, three attacks namely Random Yes (RY) attack, Random No (RN) attack, and Random False (RF) attack have considered. Moreover, the proposed model is implemented in MATLAB software and the stimulation results are compared with existing models such as Extreme Learning Machine (ELM), Deep Neural Network (DNN), Recurrent Neural Network (RNN), and Support Vector Machine (SVM). The result has determined in terms of performance metrics such as accuracy, precision, recall, F1-score and FAR. As a result, better accuracy for 99.7% has achieved than the existing models by detecting the attacks efficiently.