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

Abstract

Background: 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 considered the biggest threat menacing CSS. Methods: 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). Results: This comparative study aims 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 performed to show that the soft data fusion scheme using MRC is the best in the field of soft data computing. Conclusion: ANMU has a slight impact on CSS. Yet, AYMU affects the cooperative detection performance.