Results 141 to 150 of about 21,088 (211)

ReTrial: Robust Encrypted Malicious Traffic Detection via Discriminative Relation Incorporation and Misleading Relation Correction

IEEE Transactions on Information Forensics and Security
Encryption techniques greatly ensure the confidentiality and integrity of network communications. However, they also allow attackers to conceal malicious activities within encrypted traffic, posing severe cybersecurity challenges.
Jianjin Zhao, Qi Li, Zewei Han, Junsong Fu, Gu Nan, Meng Shen, Bharat K. Bhargava   +6 more
semanticscholar   +1 more source

Detection of Unknown Encrypted Malicious Traffic Based on Machine Learning

2025 5th International Conference on Artificial Intelligence, Big Data and Algorithms (CAIBDA)
Aiming at the deficiencies of traditional encrypted malicious traffic detection methods in feature extraction accuracy, detection speed and prior knowledge dependence, this paper proposes an unknown encrypted malicious traffic detection method based on ...
Long Qin, QingBing Ji
openaire   +2 more sources

Detecting encrypted malicious traffic with HEAT: a header-focused deep learning approach

Computer/law journal
The widespread adoption of encryption in network traffic significantly challenges traditional detection methods that rely on payload analysis. Existing approaches often convert traffic into images or sequences for deep learning models, producing ...
Ernest Akpaku, Jinfu Chen, Mukhtar Ahmed, W. Brown-Acquaye, F. Agbenyegah, Rexford Nii Ayitey Sosu   +5 more
semanticscholar   +1 more source

Meta-TFEN: A Multi-Modal Deep Learning Approach for Encrypted Malicious Traffic Detection

International Telecommunication Networks and Applications Conference, 2023
Malware poses a significant threat to internet security. Existing deep learning-based methods for malware traffic detection typically rely on single-modal features, overlooking the heterogeneity of encrypted traffic, thus limiting their detection ...
RuoYang Gu, Jin-long Fei, Hao Yu, Yuefei Zhu, Kai Yang, Fang Guo   +5 more
semanticscholar   +1 more source

Against Malicious SSL/TLS Encryption: Identify Malicious Traffic Based on Random Forest

2020
It has become a significant research direction to resist cyberattacks through traffic identification technology. Traditional traffic identification technology is often based on network port or feature matching, which has become inefficient in the increasingly complex network environment.
Yong Fang, Yijia Xu, Cheng Huang, Liang Liu, Lei Zhang   +4 more
openaire   +1 more source

Encrypted Malicious Traffic Detection with Limited Data Based on Active Learning

International Conference on Parallel and Distributed Systems
Accurate encrypted malicious traffic detection is crucial for improving network service quality. Existing methods leverage the widespread application of machine learning (ML) to distinguish encrypted malicious traffic from normal traffic by learning the ...
Ziyi Chen, Xingwei Wang, Yang Tang, Rongfei Zeng, Yuhai Zhao, Min Huang, Bo Yi   +6 more
semanticscholar   +1 more source

Encrypted Malicious Traffic Detection Using Multi-instance Learning

International Conference on Conceptual Structures
Ziwei Zhang, Jiangyi Yin, Zhao Li, Jiangchao Chen, Meijie Du, Zhongyi Zhang, Qingyun Liu   +6 more
openaire   +2 more sources

Detection of Encrypted Malicious Network Traffic using Machine Learning

MILCOM 2019 - 2019 IEEE Military Communications Conference (MILCOM), 2019
The proliferation of encrypted network traffic necessitates an innovative machine learning traffic analysis approach which does not rely on pattern matching or the payload content of the packets to detect malicious / suspicious communications. Encryption of Internet traffic has increasingly become a typical best practice, making network packet content ...
Michael J. de Lucia, Chase Cotton
openaire   +1 more source

Transfer Learning for Encrypted Malicious Traffic Detection Based on Efficientnet

2021 3rd International Conference on Advances in Computer Technology, Information Science and Communication (CTISC), 2021
With the development of data encryption technology, encrypted traffic has shown an explosive growth trend. More and more malicious network services rely on encryption to evade detection, which brings huge challenges to traditional rule-based traffic classification methods.
Surong Zhang, Youjun Bu, Bo Chen, Xiangyu Lu   +3 more
openaire   +1 more source

CAT-Mamba: A State Space Model Approach for Encrypted Malicious Traffic Detection

International Conference on Data Science in Cyberspace
As a key enabler of energy transformation, the new-type power system is increasingly exposed to sophisticated cybersecurity threats due to its rapid digitalization and interconnection. Although sequence models have become the dominant solution in network
Yunpeng Gao, Gang Wang, Peng Wang, Ying Wang   +3 more
semanticscholar   +1 more source