Research on Malicious Attack Detection and Evaluation Methods based on Entropy Increase Analysis

Xin Cao, Yu Han, Liumei Zhang, Yuanjiao Hu

Abstract: In view of the increasingly prominent problem of malicious attacks in the current network security field, this study proposes a malicious attack detection and evaluation method based on entropy increase analysis. In the GNS3 virtual network experimental environment, malicious attacks are simulated and system performance is monitored. By applying the entropy increase calculation method, the attacked system is successfully identified and its stability is evaluated. The experimental results show that this entropy increase-based method is feasible and effective, and can detect malicious attacks more accurately and take timely countermeasures to protect the security and stability of the network system and improve the network security defense capability.

External IDs:dblp:conf/nana/CaoHZH24