Accurate Encrypted Malicious Traffic Identification via Traffic Interaction Pattern Using Graph Convolutional Network

Abstract

Telecommuting and telelearning have gradually become mainstream lifestyles in the post-epidemic era. The extensive interconnection of massive terminals gives attackers more opportunities, which brings more significant challenges to network traffic security analysis. The existing attacks, often using encryption technology and distributed attack methods, increase the number and complexity of attacks. However, the traditional methods need more analysis of encrypted malicious traffic interaction patterns and cannot explore the potential correlations of interaction patterns in a macroscopic and comprehensive manner. Anyway, the changes in interaction patterns caused by attacks also need further study. Therefore, to achieve accurate and effective identification of attacks, it is essential to comprehensively describe the interaction patterns of malicious traffic and portray the relations of interaction patterns with the appearance of attacks. We propose a method for classifying attacks based on the traffic interaction attribute graph, named G-TIAG. At first, the G-TIAG studies interaction patterns of traffic describes the construction rule of the graphs and selects the attributive features of nodes in each graph. Then, it uses a convolutional graph network with a GRU and self-attention to classify benign data and different attacks. Our approach achieved the best classification results, with 89% accuracy and F1-Score, 88% recall, respectively, on publicly available datasets. The improvement is about 7% compared to traditional machine learning classification results and about 6% compared to deep learning classification results, which finally successfully achieved the classification of attacks.

A Mobile Application-Classifying Method Based on a Graph Attention Network from Encrypted Network Traffic

“…Simultaneously, with the improvement of hardware technology, the performance of deep learning-based classifiers has begun to show its superiority in many fields, dwarfing traditional machine learning-based classifiers, and this is also true for the field of traffic classification [18][19][20][21]. However, since pre-processing traffic is important in the classification process, it is crucial to effectively represent different traffic during feature engineering.…”