TCMal: A Hybrid Deep Learning Model for Encrypted Malicious Traffic Classification

Li, Mingxing; Song, Xuyan; Zhao, Jingling; Cui, Baojiang

doi:10.1109/iccc56324.2022.10065869

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Cited by 3 publications

References 22 publications

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Deep learning and pre-training technology for encrypted traffic classification: A comprehensive review

Dong,

Yu,

Lin

et al. 2025

Neurocomputing

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Deep learning and pre-training technology for encrypted traffic classification: A comprehensive review

Dong,

Yu,

Lin

et al. 2025

Neurocomputing

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Research on the Application of Transformer in Encrypted Traffic Recognition

Hong,

Gao,

Chen

et al. 2023

2023 2nd International Conference on Machine Learning, Cloud Computing and Intelligent Mining (MLCCIM)

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Artificial Intelligence-Based Anomaly Detection Technology over Encrypted Traffic: A Systematic Literature Review

Ji,

Lee,

Kang

et al. 2024

Sensors

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As cyber-attacks increase in unencrypted communication environments such as the traditional Internet, protected communication channels based on cryptographic protocols, such as transport layer security (TLS), have been introduced to the Internet. Accordingly, attackers have been carrying out cyber-attacks by hiding themselves in protected communication channels. However, the nature of channels protected by cryptographic protocols makes it difficult to distinguish between normal and malicious network traffic behaviors. This means that traditional anomaly detection models with features from packets extracted a deep packet inspection (DPI) have been neutralized. Recently, studies on anomaly detection using artificial intelligence (AI) and statistical characteristics of traffic have been proposed as an alternative. In this review, we provide a systematic review for AI-based anomaly detection techniques over encrypted traffic. We set several research questions on the review topic and collected research according to eligibility criteria. Through the screening process and quality assessment, 30 research articles were selected with high suitability to be included in the review from the collected literature. We reviewed the selected research in terms of dataset, feature extraction, feature selection, preprocessing, anomaly detection algorithm, and performance indicators. As a result of the literature review, it was confirmed that various techniques used for AI-based anomaly detection over encrypted traffic were used. Some techniques are similar to those used for AI-based anomaly detection over unencrypted traffic, but some technologies are different from those used for unencrypted traffic.

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TCMal: A Hybrid Deep Learning Model for Encrypted Malicious Traffic Classification

Cited by 3 publications

References 22 publications

Deep learning and pre-training technology for encrypted traffic classification: A comprehensive review

Deep learning and pre-training technology for encrypted traffic classification: A comprehensive review

Research on the Application of Transformer in Encrypted Traffic Recognition

Artificial Intelligence-Based Anomaly Detection Technology over Encrypted Traffic: A Systematic Literature Review

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