Mitigation of spectrum sensing data falsification attack using multilayer perception in cognitive radio networks

Abstract

Cognitive radio network (CRN) is used to solve spectrum scarcity and low spectrum utilization problems in wireless communication systems. Spectrum sensing is a vital process in CRNs, which needs continuous measurement of energy. It enables the sensors to sense the primary signal. Cooperative Spectrum Sensing (CSS) has recommended to sense spectrum accurately and to enhance detection performance. However, Spectrum Sensing Data Falsification (SSDF) attack being launched by malicious users can lead to wrong global decision on the availability of spectrum. It is an extremely challenging task to alleviate impact of SSDF attack. Over the years, numerous strategies have been proposed to mitigate SSDF attack ranging from statistical to machine learning models. Energy measurement through statistical models is based on some predefined criteria. On the other hand, machine learning models have low sensing performance. Therefore, it is necessary to develop an efficient method to mitigate the negative impact of SSDF attack. This paper intends to propose a Multilayer Perceptron (MLP) classifier to identify falsified data in CSS to prevent SSDF attack. The statistical features of the received signals are measured and taken as feature vectors to be trained by MLP. In this manner, measurement of these statistical features using MLP becomes a key task in cognitive radio networks. Trained network is employed to differentiate malicious users signal from honest users’ signal. The network is trained with the Levenberg-Marquart algorithm and then employed for eliminating the effect of attacks due to the SSDF process. Once the simulated results are observed, it can be revealed that the proposed model could efficiently reduce the impact of malicious users in CRN.