Feature extraction algorithm for payload based on tree structure representation

Shang Wu; Dongsheng Xu; Shulin Lv

doi:10.1117/12.2626489

21 December 2021 Feature extraction algorithm for payload based on tree structure representation

Shang Wu, Dongsheng Xu, Shulin Lv

Proceedings Volume 12156, International Conference on Algorithms, High Performance Computing, and Artificial Intelligence (AHPCAI 2021); 121560D (2021) https://doi.org/10.1117/12.2626489
Event: International Conference on Algorithms, High Performance Computing, and Artificial Intelligence (AHPCAI 2021), 2021, Sanya, China

Abstract

The payload in the network traffic contains a variety of information related to the traffic. Identifying anomalous attack behaviors through the payload is a crucial method to protect against network attacks effectively. The payload structure is complex, which contains a large number of contents related to the security field, and these contents have contextual semantics strong relevance. To fully express the relevance of payload contents and better improve the quality of payload feature extraction, this paper proposes a feature extraction algorithm for payload based on tree structure representation, called TSR. The experimental results show that, compared with the existing feature extraction algorithms, the ROC-AUC of TSR increases by 3.32% on average, and the PR-AUC increases by 24.15% on average.

Citation Download Citation

Shang Wu, Dongsheng Xu, and Shulin Lv "Feature extraction algorithm for payload based on tree structure representation", Proc. SPIE 12156, International Conference on Algorithms, High Performance Computing, and Artificial Intelligence (AHPCAI 2021), 121560D (21 December 2021); https://doi.org/10.1117/12.2626489

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KEYWORDS

Feature extraction

Data modeling

Performance modeling

Network security

Machine learning

Model-based design

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