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Encrypted Malicious Traffic Detection Based on Sample Selection Optimization and Data Augmentation
International Conference on Data Science in CyberspaceA large number of network services and applications now use encryption technology to ensure network information security. However, while encryption technology protects user privacy, it also presents new challenges.
Rui-fan Liang +7 more
semanticscholar +1 more source
2025 1st International Conference on Consumer Technology (ICCT-Pacific)
As the number of devices connected to the network has increased exponentially, hyper-connected intelligent network environments have become common.
S. Jeon, Il-Gu Lee
semanticscholar +1 more source
As the number of devices connected to the network has increased exponentially, hyper-connected intelligent network environments have become common.
S. Jeon, Il-Gu Lee
semanticscholar +1 more source
Detecting Encrypted Malicious Traffic with an SPDConv-Enhanced 1D CNN
2025 10th International Conference on Electronic Technology and Information Science (ICETIS)The rapid proliferation of network-layer encryption (e.g., HTTPS, SSH, TLS) safeguards user privacy while simultaneously affording attackers a convenient cloak for malicious activity.
Tianyi Wang +5 more
semanticscholar +1 more source
An Encrypted Malicious Traffic Detection Method Based on Deeper Protocol Features
International Conference on Data Science in CyberspaceNetwork encryption protocols can protect user privacy and data security but can also provide an opportunity to conceal network attacks. To address feature loss and incompleteness in existing encrypted malicious traffic detection methods, this paper ...
Lei Chen +3 more
semanticscholar +1 more source
Encrypted Malicious Traffic Detection Based on Ensemble Learning
2022Fengrui Xiao +3 more
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IEEE Transactions on Information Forensics and Security
Machine learning (ML) is highly effective for accurate encrypted malicious traffic identification by using high-quality training data. In fact, obtaining such data is costly and challenging. As a result, many ML-based models are inevitably trained on low-
Xiang Luo +7 more
semanticscholar +1 more source
Machine learning (ML) is highly effective for accurate encrypted malicious traffic identification by using high-quality training data. In fact, obtaining such data is costly and challenging. As a result, many ML-based models are inevitably trained on low-
Xiang Luo +7 more
semanticscholar +1 more source
IEEE Transactions on Networking
Encrypted Malicious Traffic Detection (EMTD) is challenging due to the limitations of inspecting encrypted content and the scarcity of labeled data. Graph Contrastive Learning (GCL) offers a promising direction by representing traffic as graphs, yet ...
Meihui Zhong +4 more
semanticscholar +1 more source
Encrypted Malicious Traffic Detection (EMTD) is challenging due to the limitations of inspecting encrypted content and the scarcity of labeled data. Graph Contrastive Learning (GCL) offers a promising direction by representing traffic as graphs, yet ...
Meihui Zhong +4 more
semanticscholar +1 more source
Graph based encrypted malicious traffic detection with hybrid analysis of multi-view features
Information Sciences, 2023Yueping Hong +3 more
semanticscholar +1 more source
Hybrid Malicious Encrypted Network Traffic Flow Detection Model
2022Shivaraj Hublikar, N. Shekar V. Shet
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Exploration on Malicious Encrypted Traffic Classification Based on Deep Learning
2023 International Conference on Ambient Intelligence, Knowledge Informatics and Industrial Electronics (AIKIIE), 2023Kai Cheng +8 more
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