Mo Chen1, Xiaojuan Wang1, *, Mingshu He1, Lei Jin1, Khalid Javeed2, Xiaojun Wang3
CMC-Computers, Materials & Continua, Vol.64, No.2, pp. 941-959, 2020, DOI:10.32604/cmc.2020.09802
Abstract Attacks on websites and network servers are among the most critical threats in
network security. Network behavior identification is one of the most effective ways to
identify malicious network intrusions. Analyzing abnormal network traffic patterns and
traffic classification based on labeled network traffic data are among the most effective
approaches for network behavior identification. Traditional methods for network traffic
classification utilize algorithms such as Naive Bayes, Decision Tree and XGBoost.
However, network traffic classification, which is required for network behavior
identification, generally suffers from the problem of low accuracy even with the recently
proposed deep learning models. To improve network… More >
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