A deep learning-based method for HTTP payload classification in attack detection

Ming Zhu; Tao Hong; Qinglan Luo; Xia Shang

doi:10.1117/12.3010403

20 October 2023 A deep learning-based method for HTTP payload classification in attack detection

Ming Zhu, Tao Hong, Qinglan Luo, Xia Shang

Proceedings Volume 12814, Third International Conference on Green Communication, Network, and Internet of Things (CNIoT 2023); 1281423 (2023) https://doi.org/10.1117/12.3010403
Event: Third International Conference on Green Communication, Network, and Internet of Things (CNIoT 2023), 2023, Chongqing, China

Abstract

Attack detection is a crucial process that involves closely monitoring and identifying malicious attacks. To identify and locate such attacks with precision, there is a need for a thorough analysis and classification of malicious payloads. To this end, a deep learning-based method is proposed in this paper, which enables the efficient classification of payloads for attack detection. The method involves segmenting the payloads using regular expressions, which helps in preserving their syntactic structure. Also, an improved TF-IDF algorithm is introduced to construct a streamlined vocabulary that alleviates the slow training problem caused by a large vocabulary. By fusing the features of vectors extracted using CNN and BiLSTM-Attention, the payload content can be effectively represented, yielding more accurate recognition results and improving the problem of low detection accuracy associated with traditional methods. The experimental results reveal that the proposed method achieved an accuracy of 99.21% on the CSIC 2010 dataset, which is significantly higher than that of the traditional method, and has faster training speed. This suggests that the proposed method can detect more stealthy attacks and build a more effective Web attack detection system.

(2023) Published by SPIE. Downloading of the abstract is permitted for personal use only.

Citation Download Citation

Ming Zhu, Tao Hong, Qinglan Luo, and Xia Shang "A deep learning-based method for HTTP payload classification in attack detection", Proc. SPIE 12814, Third International Conference on Green Communication, Network, and Internet of Things (CNIoT 2023), 1281423 (20 October 2023); https://doi.org/10.1117/12.3010403

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KEYWORDS

Education and training

Deep learning

Data modeling

Feature extraction

Feature fusion

Performance modeling

Associative arrays

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