Effect Analysis of Malicious Flow Classification Model Based on Representation Learning on Network Flow Anomaly Detection

Abstract

Network traffic anomaly detection, as a key link of network security, has been paid more and more attention in recent years. Aiming at abnormal flow caused by improper network usage, this paper proposes a network flow anomaly detection model using representation learning. In this model, the study treats raw flow data as images directly through representation learning, and then classifies malicious flow by performing image classification tasks. The study is tested using the USTC-TFC2016 dataset. The experimental results show that the model exhibits excellent classification accuracy of 0.9990 both in the characterization of flow sessions and total flow, and PR and F1 values are all above 0.9907. In addition, the classification accuracy of the three classifiers for flow data is more than 98%, and the classification accuracy of normal flow and malicious flow is 100%. The experimental results show that the proposed method meets the needs of practical applications and has excellent classification performance. This provides a new research angle and direction for network flow anomaly detection.