TY - GEN
T1 - A Multi-Modal Approach For Context-Aware Network Traffic Classification
AU - Pang, Bo
AU - Fu, Yongquan
AU - Ren, Siyuan
AU - Shen, Siqi
AU - Wang, Ye
AU - Liao, Qing
AU - Jia, Yan
N1 - Publisher Copyright:
© 2023 IEEE.
PY - 2023
Y1 - 2023
N2 - Network traffic classification is important for network security and management. State-of-the-art classifiers use deep learning techniques to automatically extract feature vectors from the traffic, which however lose important context of the communication sessions and encapsulated text semantics. In this paper, we present a Multi-Modal Classification method named MTCM to systematically exploit the context for the classification task. We build an adaptive context-aware feature extraction framework over varying-length and dynamic packet sequences, based on the attention-aware graph neural networks and BERT. We next automatically fusion multimodal features with the Multi-Layer Perception (MLP) that unifies the graph and semantic features for the packet stream. Extensive evaluation with real-world application and abnormal network datasets show that MTCM outperforms state- of-the-art deep learning methods, and is robust for different classes of traffic data sets.
AB - Network traffic classification is important for network security and management. State-of-the-art classifiers use deep learning techniques to automatically extract feature vectors from the traffic, which however lose important context of the communication sessions and encapsulated text semantics. In this paper, we present a Multi-Modal Classification method named MTCM to systematically exploit the context for the classification task. We build an adaptive context-aware feature extraction framework over varying-length and dynamic packet sequences, based on the attention-aware graph neural networks and BERT. We next automatically fusion multimodal features with the Multi-Layer Perception (MLP) that unifies the graph and semantic features for the packet stream. Extensive evaluation with real-world application and abnormal network datasets show that MTCM outperforms state- of-the-art deep learning methods, and is robust for different classes of traffic data sets.
KW - context-aware
KW - graph neural network
KW - multi-modal learning
KW - traffic classification
UR - https://www.scopus.com/pages/publications/85177603020
U2 - 10.1109/ICASSP49357.2023.10095124
DO - 10.1109/ICASSP49357.2023.10095124
M3 - 会议稿件
AN - SCOPUS:85177603020
T3 - ICASSP, IEEE International Conference on Acoustics, Speech and Signal Processing - Proceedings
BT - ICASSP 2023 - 2023 IEEE International Conference on Acoustics, Speech and Signal Processing, Proceedings
PB - Institute of Electrical and Electronics Engineers Inc.
T2 - 48th IEEE International Conference on Acoustics, Speech and Signal Processing, ICASSP 2023
Y2 - 4 June 2023 through 10 June 2023
ER -