A Comparative Study of Machine Learning Techniques for Detecting Spectrum Sensing Attacks

Abstract

Cooperative Spectrum Sensing (CSS) facilitates effective spectrum sharing in CRNs through the collaborative detection of PU activity by SUs[1]. Even though cooperation enhances detection robustness against fading and interference, it simultaneously poses threats from SSDF attacks in which colluding SUs manipulate sensing data to affect the global decision made at the fusion center. Many existing approaches to detecting SSDF have claimed high accuracy, but their assumptions about wireless channels may not reflect reality. The paper describes a framework for SSDF detection through machine learning on datasets generated by simulations. The sensing behaviors are modeled according to different SNR levels, and adversarial manipulation is simulated using probabilistic rules to generate a heterogeneous dataset. Statistical features based on energy detection are extracted and fed into three classifiers: Support Vector Machine, Logistic Regression, and Random Forest. The results indicate that Random Forest consistently outperforms other analysed classifiers for different SNR and attack conditions Thus, tree-based algorithms can efficiently model complex non-linear relationships in feature space. The good performance of Random Forest indicates that the tree-based ensemble approaches are well suited to detect the non-linear relations that are typical in the feature space