Research on SDN Fingerprint Attack Defense Mechanism Based on Dynamic Disturbance and Information Entropy Detection

Abstract

As an emerging type of network architecture, SDN is widespread used and security issues have also received more and more attention. Fingerprint attacks represent one of the most significant threats to network security. Attackers obtain key fingerprint information of the target network, which lays the foundation for subsequent more threatening attacks. Currently, research on domestic and international SDN fingerprint attacks focuses on how to attack, and less research is being done on how to defend against fingerprinting attacks. This paper proposes a mechanism for defending fingerprint attacks that combines dynamic disturbance and information entropy detection. This mechanism adopts the principle of fingerprint attack, combined with a moving average algorithm, Bloom Filter, and packet delay tool, to confuse opponents by disturbing a small number of packets, simultaneously, combined with the information entropy detection to make real-time processing feedback to the network. The experimental results show that this mechanism works effectively to defend SDNs against fingerprint attacks without affecting the normal network communication.