A uniform network flow feature aggregation method based on parallel LSTM

Xin Shou; Lihua Yin; Xi Luo

doi:10.1117/12.3005813

10 October 2023 A uniform network flow feature aggregation method based on parallel LSTM

Xin Shou, Lihua Yin, Xi Luo

Proceedings Volume 12799, Third International Conference on Advanced Algorithms and Signal Image Processing (AASIP 2023); 127992M (2023) https://doi.org/10.1117/12.3005813
Event: 3rd International Conference on Advanced Algorithms and Signal Image Processing (AASIP 2023), 2023, Kuala Lumpur, Malaysia

Abstract

As the Internet continues to evolve, network attacks are becoming increasingly complex, and malicious traffic is taking on more diverse forms. A key factor in the correct identification of whether traffic is benign or malicious is the method used to extract traffic features. While researchers have investigated the extraction of robust features from raw traffic data, most have focused solely on individual packets and have not considered the semantic representations of bytes in the same location across different packets. In this paper, we propose a novel method for extracting aligned byte stream features, which involves splitting packets into several parts to extract aligned bytes. Based on this method, we propose a deep-level network-based flow feature aggregation approach for detecting malicious traffic, which uses multiple LSTMs to aggregate flow features and employs deep-level networks for detection. Extensive experimental evaluations demonstrate the effectiveness of the proposed method.

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

Citation Download Citation

Xin Shou, Lihua Yin, and Xi Luo "A uniform network flow feature aggregation method based on parallel LSTM", Proc. SPIE 12799, Third International Conference on Advanced Algorithms and Signal Image Processing (AASIP 2023), 127992M (10 October 2023); https://doi.org/10.1117/12.3005813

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KEYWORDS

Feature extraction

Education and training

Neural networks

Matrices

Neurons

Artificial neural networks

Deep learning

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