Results 11 to 20 of about 21,088 (211)

GCN-ETA: High-Efficiency Encrypted Malicious Traffic Detection [PDF]

Security and Communication Networks, 2022
Encrypted network traffic is the principal foundation of secure network communication, and it can help ensure the privacy and integrity of confidential information. However, it hides the characteristics of the data, increases the difficulty of detecting malicious traffic, and protects such malicious behavior.
Juan Zheng, Zhiyong Zeng, Tao Feng
openaire   +2 more sources

Deep-Forest-Based Encrypted Malicious Traffic Detection

Electronics, 2022
The SSL/TLS protocol is widely used in data encryption transmission. Aiming at the problem of detecting SSL/TLS-encrypted malicious traffic with small-scale and unbalanced training data, a deep-forest-based detection method called DF-IDS is proposed in this paper.
Xueqin Zhang, Min Zhao, Jiyuan Wang, Shuang Li, Yue Zhou, Shinan Zhu   +5 more
openaire   +2 more sources

A Multi-Feature Semantic Fusion Machine Learning Architecture for Detecting Encrypted Malicious Traffic

Journal of Cybersecurity and Privacy
With the increasing sophistication of network attacks, machine learning (ML)-based methods have showcased promising performance in attack detection. However, ML-based methods often suffer from high false rates when tackling encrypted malicious traffic ...
Shiyu Tang, Fei Du, Zulong Diao, Wenjun Fan   +3 more
doaj   +2 more sources

CNNRes-DIndRNN: A New Method for Detecting TLS-Encrypted Malicious Traffic

Future Internet
While ensuring the accuracy of encrypted malicious traffic detection, improving model training speed remains a challenge. In order to solve this challenge, we propose CNNRes-DIndRNN for detecting encrypted malicious traffic classification.
Jinsha Zhang, Xiaoying Wang, Chunhui Li, Qingjie Zhang, Guoqing Yang, Xinyu Li, Fangfang Cui, Ruize Gu, Panpan Qi, Shuai Liu   +9 more
doaj   +2 more sources

GCN-MHA Method for Encrypted Malicious Traffic Detection and Classification

Electronics
Modern network attacks are becoming stealthier and smarter. Attackers use encryption to cover up malicious traffic, which makes it really hard to detect. To solve this problem, this paper introduces a new model called Graph Convolutional Network with Multi-Head Attention (GCN-MHA).
Yanan Liu, Suhao Wang, Zheng Zhang, Tianhao Hou, Jipeng Shen, Pengfei Wang, Shuo Qiu, Lejun Ma   +7 more
openaire   +2 more sources

Accurate Encrypted Malicious Traffic Identification via Traffic Interaction Pattern Using Graph Convolutional Network

Applied Sciences, 2023
Telecommuting and telelearning have gradually become mainstream lifestyles in the post-epidemic era. The extensive interconnection of massive terminals gives attackers more opportunities, which brings more significant challenges to network traffic ...
Guoqiang Ren, Guang Cheng, Nan Fu
doaj   +2 more sources

AFF_CGE: Combined Attention-Aware Feature Fusion and Communication Graph Embedding Learning for Detecting Encrypted Malicious Traffic

Applied Sciences
While encryption enhances data security, it also presents significant challenges for network traffic analysis, especially in detecting malicious activities.
Junhao Liu, Guolin Shao, Hong Rao, Xiangjun Li, Xuan Huang   +4 more
doaj   +2 more sources

Detecting Unknown Encrypted Malicious Traffic in Real Time via Flow Interaction Graph Analysis [PDF]

Network and Distributed System Security Symposium, 2023
In this paper, we propose HyperVision, a realtime unsupervised machine learning (ML) based malicious traffic detection system. Particularly, HyperVision is able to detect unknown patterns of encrypted malicious traffic by utilizing a compact inmemory ...
Chuanpu Fu, Qi Li, Ke Xu
semanticscholar   +1 more source

MTDecipher: robust encrypted malicious traffic detection via multi-task graph neural networks

Cybersecurity
The widespread adoption of encrypted traffic protocols has significantly increased the challenge of detecting malicious traffic. Existing detection methods based on deep learning typically rely on fine-grained features of data packets, such as length ...
Fan Li, Xi Luo, Weihong Han, Binxing Fang, Lihua Yin   +4 more
doaj   +2 more sources

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
doaj   +1 more source