Malicious network traffic classification method based on sample enhancement and comparative learning

• Main Authors: LYU MINGQI, CHEN TIEMING, ZHU TIANTIAN, XIE JINGXI
• Format: Patent
• Language: Chinese; English
• Published: 10.11.2023
• Subjects: CALCULATING; COMPUTER SYSTEMS BASED ON SPECIFIC COMPUTATIONAL MODELS; COMPUTING; COUNTING; ELECTRIC DIGITAL DATA PROCESSING; PHYSICS

The invention belongs to the technical field of network security and deep learning, and particularly relates to a malicious network traffic classification method based on sample enhancement and comparative learning. The method comprises the following steps: acquiring network traffic, extracting network traffic characteristics, constructing a network traffic sample set, preprocessing the network traffic characteristics, and constructing a pre-training set; training the basic model, calculating a loss value, calculating parameters of the basic model according to the loss value, and updating the parameters to obtain a comparative learning malicious traffic classification model; and carrying out malicious traffic classification in the new task by adopting a comparative learning malicious traffic classification model. According to the method, the basic model of the shallow neural network architecture with light resource occupation is adopted, so that the occupied resources are few, and the operation efficiency is