CBD: A Deep-Learning-Based Scheme for Encrypted Traffic Classification with a General Pre-Training Method [PDF]
Sensors, 2021 With the rapid increase in encrypted traffic in the network environment and the increasing proportion of encrypted traffic, the study of encrypted traffic classification has become increasingly important as a part of traffic analysis.
Xinyi Hu +3 more
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Deep Encrypted Traffic Detection: An Anomaly Detection Framework for Encryption Traffic Based on Parallel Automatic Feature Extraction. [PDF]
Comput Intell Neurosci, 2023 With an increasing number of network attacks using encrypted communication, the anomaly detection of encryption traffic is of great importance to ensure reliable network operation. However, the existing feature extraction methods for encrypted traffic anomaly detection have difficulties in extracting features, resulting in their low efficiency. In this
Long G, Zhang Z.
europepmc +3 more sources
Multi-Task Scenario Encrypted Traffic Classification and Parameter Analysis [PDF]
SensorsThe widespread use of encrypted traffic poses challenges to network management and network security. Traditional machine learning-based methods for encrypted traffic classification no longer meet the demands of management and security. The application of
Guanyu Wang, Yijun Gu
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Semi-2DCAE: a semi-supervision 2D-CNN AutoEncoder model for feature representation and classification of encrypted traffic [PDF]
PeerJ Computer Science, 2023 Traffic classification is essential in network-related areas such as network management, monitoring, and security. As the proportion of encrypted internet traffic rises, the accuracy of port-based and DPI-based traffic classification methods has declined.
Jun Cui +4 more
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A VPN-Encrypted Traffic Identification Method Based on Ensemble Learning
Applied Sciences, 2022 One of the foundational and key means of optimizing network service in the field of network security is traffic identification. Various data transmission encryption technologies have been widely employed in recent years.
Jie Cao +4 more
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Encrypted video traffic clustering demystified [PDF]
Computers & Security, 2020 Cyber threat intelligence officers and forensics investigators often require the behavioural profiling of groups based on their online video viewing activity. It has been demonstrated that encrypted video traffic can be classified under the assumption of using a known subset of video titles based on temporal video viewing trends of particular groups ...
Amit Dvir +4 more
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Encrypted Malicious Traffic Identification Based on Hierarchical Spatiotemporal Feature and Multi-Head Attention [PDF]
Jisuanji gongcheng, 2021 To implement the full encryption of Internet,the accurate detection of encrypted malicious traffic is required,but traditional detection methods rely heavily on expert experience and perform poorly in distiguishment of encrypted traffic feature is not ...
JIANG Tongtong, YIN Weixin, CAI Bing, ZHANG Kun
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Research on Malicious Encrypted Traffic Annotation Method for Android Mobile Application [PDF]
Jisuanji gongcheng, 2020 In order to distinguish malicious traffic generated by running malicious Android applications from normal traffic,this paper proposes a method for annotating malicious traffic of mobile Android applications.For encrypted network traffic,encryption ...
HE Gaofeng, SI Yongrui, XU Bingfeng
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Encrypted Traffic Classification Method Based on Multi-Layer Bidirectional SRU and Attention Model [PDF]
Jisuanji gongcheng, 2022 The encrypted traffic classification method based on traditional Recurrent Neural Network(RNN) typically have poor parallelism and low efficiency.To quickly and accurately classify encrypted traffic, a classification method for encrypted traffic based on
ZHANG Surong, BU Youjun, CHEN Bo, SUN Chongxin, WANG Han, HU Xianjun
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Deep Learning-Based Efficient Analysis for Encrypted Traffic
Applied Sciences, 2023 To safeguard user privacy, critical Internet traffic is often transmitted using encryption. While encryption is crucial for protecting sensitive information, it poses challenges for traffic identification and poses hidden dangers to network security.
Xiaodan Yan
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