A Density-based Clustering Approach to detect Colluding SSDF Attackers in Cognitive Radio Networks
Amar Taggu, Ningrinla Marchang · 2022
Among the many attacks that Cognitive Radio Networks (CRN) are vulnerable to, Spectrum Sensing Data Falsification (SSDF) attack is one of the most common attacks. The current work focusses on an advanced form of SSDF attacks, called Colluding SSDF attack. The colluding attack is a collaborative well-planned and well-timed attack where a leader instructs the other attackers when and how to attack a CRN during sensing period. Needless to say, Colluding SSDF attacks are harder to detect and thus, they are more harmful. To detect this attack, we have proposed a technique based on Density-Based Spatial Clustering of Applications with Noise (DBSCAN) [1]. The density-based clustering technique separates the high-density clusters (normal SUs) from low-density clusters (attackers). Simulation results demonstrate the effectiveness of the proposed when the percentage of attackers is less than 50%. Additionally, a performance comparison of the proposed method has been done with an existing work, agglomerative clustering detection algorithm (ACD)[2].