Automatic Detection Method of Encrypted Malicious Traffic Based on the Gradient Boosting Decision Tree
To protect network resources,maintain network space security,and identify encrypted malicious traffic,an automatic detection method for encrypted malicious traffic based on Gradient Boosting Decision Tree(GBDT)is proposed.Extract encrypted malicious traffic features by analyzing the characteristics of encrypted data in network traffic.Based on the extracted features using the GBDT algorithm,the malicious traffic source Internet Protocol(IP)address is identified.Adjust the accumulated misclassification cost for identified malicious IP addresses and identify and detect encrypted malicious traffic.The experimental results show that this method has high efficiency in detecting malicious traffic and low false alarm rate,providing strong technical support for network security protection.
NETL
NSTL
万方数据
维普
