Results 171 to 180 of about 1,097,826 (233)

GSA-DT: A Malicious Traffic Detection Model Based on Graph Self-Attention Network and Decision Tree

IEEE Transactions on Network and Service Management
Malicious attack has shown a rapid growth in recent years, it is very important to accurately detect malicious traffic to defend against malicious attacks. Compared with machine learning and deep learning technologies, graph convolutional neural network (
Saihua Cai, Han Tang, Jinfu Chen, Tianxiang Lv, Wenjun Zhao, Chunlei Huang   +5 more
semanticscholar   +1 more source

LKD-STNN: A Lightweight Malicious Traffic Detection Method for Internet of Things Based on Knowledge Distillation

IEEE Internet of Things Journal
The purpose of malicious traffic detection and identification in the Internet of Things (IoT) is to detect the intrusion of malicious traffic within the IoT network into IoT devices.
Shizhou Zhu, Xiaolong Xu, Juan Zhao, Fu Xiao   +3 more
semanticscholar   +1 more source

MTSecurity: Privacy-Preserving Malicious Traffic Classification Using Graph Neural Network and Transformer

IEEE Transactions on Network and Service Management
Encrypting network traffic is an effective means of safeguarding user privacy and sensitive information. However, it also introduces potential vulnerabilities that can be exploited by network attackers, posing significant security risks to the Internet ...
Jin Yang, Xinyun Jiang, Yulin Lei, Weiheng Liang, Zicheng Ma, Siyu Li   +5 more
semanticscholar   +1 more source

MCRe: A Unified Framework for Handling Malicious Traffic With Noise Labels Based on Multidimensional Constraint Representation

IEEE Transactions on Information Forensics and Security
Due to the limitations of the existing annotation methods, the prevalence of label noise can be caused in realistic malicious traffic datasets, which has a significant impact on the training and evaluation of deep learning-based intrusion detection ...
Qing-jun Yuan, Gaopeng Gou, Yanbei Zhu, Yuefei Zhu, G. Xiong, Yongjuan Wang   +5 more
semanticscholar   +1 more source

Detecting while accessing: A semi-supervised learning-based approach for malicious traffic detection in Internet of Things

China Communications, 2023
In the upcoming large-scale Internet of Things (IoT), it is increasingly challenging to defend against malicious traffic, due to the heterogeneity of IoT devices and the diversity of IoT communication protocols.
Yantian Luo, Hancun Sun, Xu Chen, Ning Ge, Wei Feng, Jianhua Lu   +5 more
semanticscholar   +1 more source

Characterization and classification of malicious Web traffic

Computers & Security, 2014
Abstract Web systems commonly face unique set of vulnerabilities and security threats due to their high exposure, access by browsers, and integration with databases. This study is focused on characterization and classification of malicious cyber activities aimed at Web systems.
Katerina Goseva-Popstojanova, Goce Anastasovski, Ana Dimitrijevikj, Risto Pantev, Brandon Miller   +4 more
openaire   +1 more source

Flow Interaction Graph Analysis: Unknown Encrypted Malicious Traffic Detection

IEEE/ACM Transactions on Networking
Nowadays traffic on the Internet has been widely encrypted to protect its confidentiality and privacy. However, traffic encryption is always abused by attackers to conceal their malicious behaviors.
Chuanpu Fu, Qi Li, Ke Xu
semanticscholar   +1 more source

Identifying malicious botnet traffic using logistic regression

2018 Systems and Information Engineering Design Symposium (SIEDS), 2018
An important source of cyber-attacks is malware, which proliferates in different forms such as botnets. The botnet malware typically looks for vulnerable devices across the Internet, rather than targeting specific individuals, companies or industries. It attempts to infect as many connected devices as possible, using their resources for automated tasks
Rohan Bapat, Abhijith Mandya, Xinyang Liu, Brendan Abraham, Donald E. Brown, Hyojung Kang, Malathi Veeraraghavan   +6 more
openaire   +1 more source

Robust Detection of Malicious Encrypted Traffic via Contrastive Learning

IEEE Transactions on Information Forensics and Security
Traffic encryption is widely used to protect communication privacy but is increasingly exploited by attackers to conceal malicious activities. Existing malicious encrypted traffic detection methods rely on large amounts of labeled samples for training ...
Meng Shen, Jinhe Wu, Ke Ye, Ke Xu, Gang Xiong, Liehuang Zhu   +5 more
semanticscholar   +1 more source

LightGuard: A Lightweight Malicious Traffic Detection Method for Internet of Things

IEEE Internet of Things Journal
The rapid growth of Internet of Things (IoT) devices has expanded the cyber attack surface, posing a challenge to IoT security. Some deep-learning-based detection methods have been designed to detect malicious attacks in the IoT by analyzing network ...
Yuehua Huo, Wei Liang, Junhan Chen, Shangyuan Zhuang, Jiyan Sun   +4 more
semanticscholar   +1 more source