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Detection of Malicious Network Flows with Low Preprocessing Overhead
Network, 2022 Machine learning (ML) is frequently used to identify malicious traffic flows on a network. However, the requirement of complex preprocessing of network data to extract features or attributes of interest before applying the ML models restricts their use ...
Garett Fox, Rajendra V. Boppana
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Feature analysis of encrypted malicious traffic
Expert Systems with Applications, 2019 In recent years there has been a dramatic increase in the number of malware attacks that use encrypted HTTP traffic for self-propagation or communication. Antivirus software and firewalls typically will not have access to encryption keys, and therefore direct detection of malicious encrypted data is unlikely to succeed. However, previous work has shown
Shekhawat, Anish Singh +2 more
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Conference on Computer and Communications Security, 2023 As an emerging security paradigm, machine learning (ML) based malicious traffic detection is an essential part of automatic defense against network attacks.
Chuanpu Fu, Qi Li, Ke Xu, Jianping Wu
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Spatial-Temporal Feature with Dual-Attention Mechanism for Encrypted Malicious Traffic Detection
Security and Communication Networks, 2023 While encryption ensures the confidentiality and integrity of user data, more and more attackers try to hide attack behaviours through encryption, which brings new challenges to malicious traffic identification.
Jianyi Liu +6 more
semanticscholar +1 more source
Realtime Malicious Traffic Detection Targeted for TCP Out-of-Order Packets Based on FPGA
IEEE Access, 2023 Currently, with the increasing popularity of high-speed network, in order to protect the network environment, more and more companies start to explore how to efficiently detect malicious traffic.
Zhenguo Hu +3 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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Tor Multipath Selection Based on Threaten Awareness [PDF]
Jisuanji kexueWith the development and application of machine learning and deep learning,attackers can conduct traffic analysis on malicious nodes and malicious AS on Tor user links,thus carrying out de-anonymization attacks on Tor users.At present,one of the common ...
CHEN Shangyu, HU Hongchao, ZHANG Shuai, ZHOU Dacheng, YANG Xiaohan
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CTTGAN: Traffic Data Synthesizing Scheme Based on Conditional GAN
Sensors, 2022 Most machine learning algorithms only have a good recognition rate on balanced datasets. However, in the field of malicious traffic identification, benign traffic on the network is far greater than malicious traffic, and the network traffic dataset is ...
Jiayu Wang +4 more
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Malicious Traffic Detection Using K-means
The Journal of Korean Institute of Communications and Information Sciences, 2016 Various network attacks such as DDoS(Distributed Denial of service) and orm are one of the biggest problems in the modern society. These attacks reduce the quality of internet service and caused the cyber crime. To solve the above problem, signature based IDS(Intrusion Detection System) has been developed by network vendors.
Dong Hyuk Shin +3 more
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Research on Network Malicious Traffic Detection for Post-Exploitation Attack Behavior [PDF]
Jisuanji gongchengExisting post-exploitation behavior studies mainly focus on the host side of the attack and defense countermeasures, and lack pattern analysis and detection methods for the traffic side.
LIANG Songlin, LIN Wei, WANG Jue, YANG Qing
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