Results 71 to 80 of about 21,088 (211)

A Hybrid Transformer–CNN Framework for Semantic Behavioral Modeling in Office Malware Detection

SECURITY AND PRIVACY, Volume 9, Issue 1, January/February 2026.
ABSTRACT Office documents have emerged as a prevalent attack vector, with adversaries increasingly embedding executable payloads and malicious macros to evade signature‐based detection mechanisms. To address these challenges, this study presents a hybrid Transformer–CNN semantic behavioral modeling framework for Office malware detection.
Sheikh M. Zeeshan Javed, M. Faisal Amjad, Sajjad A. Ghauri, Shahzaib Tahir, Nauman Anwar Baig   +4 more
wiley   +1 more source

Blockchain in Communication Networks: A Comprehensive Review

IET Blockchain, Volume 6, Issue 1, January/December 2026.
This article provides a comprehensive review of blockchain applications in communication networks, focusing on domains such as IoT, 5G, vehicular systems, and decentralised trust infrastructures. It examines key challenges—including scalability, interoperability, and latency—and outlines future directions such as lightweight consensus protocols and AI ...
Quazi Mamun, Zhenni Pan, Jun Wu
wiley   +1 more source

TransGraphNet: robust detection of malicious encrypted network traffic via transformer and graph neural models [PDF]

PeerJ Computer Science
As encrypted network traffic becomes more prevalent, cybercriminals increasingly conceal their malicious activities within encrypted session contents. Traditional methods for detecting malicious encrypted traffic focus on inspecting the plaintext payload
Qiang Shi, Dacheng Shi, Liukun He, Qingjun Xiao, Liang Zheng, Jun Ma, Jingdong He, Qifan Zhang   +7 more
doaj   +2 more sources

Multi-Task Scenario Encrypted Traffic Classification and Parameter Analysis

Sensors
The 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
doaj   +1 more source

Bootstrap Forest based method for Encrypted Network Traffic Analysis

Journal of Information and Organizational Sciences
Encrypting communications and data over the Internet becomes essential in ensuring the privacy of communications and protecting the data from increasing threats. Hence, majority of Internet traffic and networked communications are encrypted now. However,
Shobana Durairaju, Aswani Kumar Cherukuri   +1 more
doaj   +1 more source

Detecting Bot Networks Based On HTTP And TLS Traffic Analysis [PDF]

Journal of Advances in Computer Engineering and Technology, 2020
— Bot networks are a serious threat to cyber security, whose destructive behavior affects network performance directly. Detecting of infected HTTP communications is a big challenge because infected HTTP connections are clearly merged with other types of ...
Zahra Nafarieh
doaj  

GSPB: a global-statistic and packet-byte fusion framework for encrypted traffic classification

Cybersecurity
While encrypted traffic protects user privacy and data security, it is also frequently exploited by malicious actors for illegal activities, e.g., phishing and malware distribution.
Haiyue Li, Jun Tao, Linxiao Yu, Yuantu Luo, Zuyan Wang   +4 more
doaj   +1 more source

Classifying Tor Traffic Encrypted Payload Using Machine Learning

IEEE Access
Tor, a network offering Internet anonymity, presented both positive and potentially malicious applications, leading to the need for efficient Tor traffic monitoring. While most current traffic classification methods rely on flow-based features, these can
Pitpimon Choorod, George Weir, Anil Fernando   +2 more
doaj   +1 more source

ETGuard: Malicious Encrypted Traffic Detection in Blockchain-Based Power Grid Systems

The escalating prevalence of encryption protocols has led to a concomitant surge in the number of malicious attacks that hide in encrypted traffic. Power grid systems, as fundamental infrastructure, are becoming prime targets for such attacks. Conventional methods for detecting malicious encrypted packets typically use a static pre-trained model.
Zhou, Peng, Liu, Yongdong, Ma, Lixun, Zhang, Weiye, Tan, Haohan, Liu, Zhenguang, Huang, Butian   +6 more
openaire   +2 more sources

CLSTM-MT (a Combination of 2-Conv CNN and BiLSTM Under the Mean Teacher Collaborative Learning Framework): Encryption Traffic Classification Based on CLSTM (a Combination of 2-Conv CNN and BiLSTM) and Mean Teacher Collaborative Learning

Applied Sciences
The identification and classification of network traffic are crucial for maintaining network security, optimizing network management, and ensuring reliable service quality.
Xiaozong Qiu, Guohua Yan, Lihua Yin
doaj   +1 more source