Sybil attack detection using received signal strength in MIMO systems

Abstract

Identity theft in distributed networks, such as the internet, is becoming a significant problem due to Sybil attacks. In these attacks, perpetrators create multiple fake identities to manipulate network decisions. Traditional cryptographic methods for detecting these attacks can be resource-intensive and sometimes ineffective. This study suggests using Received Signal Strength R_S_S based techniques in massive MIMO systems, along with diversity combining methods like Selection Combining (SLC), Equal Gain Combining (EGC), and Maximal Ratio Combining (MRC). The research also involves using machine learning algorithms such as K-means and K-medoids for clustering to distinguish between legitimate nodes and attackers. Machine learning techniques like KNN and Naive Bayes are used to conclude results. The results indicate that configurations with 30-40 antennas and low noise levels can achieve high detection accuracy. This method combines physical-layer security with machine learning, making future wireless networks more resilient against Sybil attacks.