Low-cost Influence-Limiting Defense against Adversarial Machine Learning Attacks in Cooperative Spectrum Sensing

Abstract

Cooperative spectrum sensing aims to improve the reliability of spectrum sensing by individual sensors for better utilization of the scarce spectrum bands, which gives the feasibility for secondary spectrum users to transmit their signals when primary users remain idle. However, there are various vulnerabilities experienced in cooperative spectrum sensing, especially when machine learning techniques are applied. The influence-limiting defense is proposed as a method to defend the data fusion center when a small number of spectrum sensing devices is controlled by an intelligent attacker to send erroneous sensing results. Nonetheless, this defense suffers from a computational complexity problem. In this paper, we propose a low-cost version of the influence-limiting defense and demonstrate that it can decrease the computation cost significantly (the time cost is reduced to less than 20% of the original defense) while still maintaining the same level of defense performance.