Comprehensive Performance Analysis of Soft Data Fusion Schemes Under SSDF Attacks in Cognitive Radio Networks

Abstract

Trust and security are the biggest challenges facing the Cooperative Spectrum Sensing (CSS) process in Cognitive Radio Networks (CRNs). The Spectrum Sensing Data Falsification (SSDF) attack is viewed as the biggest threat menacing CSS. This paper investigates the performance of different soft data combining rules such as Maximal Ratio Combining (MRC), Square Law Selection (SLS), Square Law Combining (SLC), and Selection Combining (SC), in the presence of Always Yes and Always No Malicious User (AYMU and ANMU). More precisely, a comparative study is conducted to assess the impact of such malicious users on the reliability of various soft data fusion schemes in terms of miss detection and false alarm probabilities. Furthermore, computer simulations are carried out to show that the soft data fusion scheme using MRC is the best among soft data combining. On the other hand, ANMU has a slight impact on CSS, however, AYMU degrades severely the cooperative detection performance.