Results 51 to 60 of about 3,020 (212)

P2P Botnet Detection Method Based on Graph Neural Network

工程科学与技术, 2022
P2P botnet has become a new network attack platform because of its high concealment and robustness, which poses an increasing threat to cyberspace security.
Honggang LIN, Yunli ZHANG, Nanxin GUO, Lin CHEN   +3 more
doaj  

HSL‐CFS: Hybrid stacked learning with cooperative feature selection for cyberattack detection in smart grids

Energy Conversion and Economics, EarlyView.
Abstract An effective method for detecting cyberattacks is essential to the security of smart grids (SGs). In SGs, data from both cyber and physical domains can support attack detection. However, existing works insufficiently consider the heterogeneity, high dimensionality, and cross‐domain correlations of multi‐source data, affecting model ...
Qize Gao, Qiuyu Lu, June Li, Zhao Peng, Guo Shen, Ming Ni   +5 more
wiley   +1 more source

Survey on Visualization of Information Diffusion over Networks

Computer Graphics Forum, EarlyView.
Abstract Information Diffusion (ID) describes how a value (e.g., a pathogen, a rumor, a packet) spreads through an underlying “medium” network of elements (e.g., a social or computer network). Understanding the information diffusion process is essential to predicting trends, controlling misinformation, and enhancing decision‐making as well as ...
T. Baumgartl, M. Sondag, V. Filipov, M. Tuscher, S. Rajendran, S. Miksch, D. Archambault, A. Arleo, T. von Landesberger   +8 more
wiley   +1 more source

Design of Universal Botnet Experimental Platform [PDF]

Jisuanji gongcheng, 2018
Botnet research in open networks has many drawbacks,such as uncontrollable process,difficult to scale,and unable to repeat experiments.In order to solve this problem,the requirement and design principle of the universal botnet experimental platform with ...
LI Dawei
doaj   +1 more source

GA‐ANN: An Efficient Hybrid Deep Learning Scheme for Network Intrusion Detection in IoT

SECURITY AND PRIVACY, Volume 9, Issue 4, July/August 2026.
ABSTRACT Intrusion detection systems (IDS) are critical to the security of the dynamic internet of things (IoT) environment. The integration of Artificial Intelligence (AI) into IDS has substantially improved network security. Particularly, deep learning techniques have shown strong potential in addressing IoT security challenges.
Naveed Ahmed, M. Siraj Rathore, Azhar Mahmood, Muhammad Daniyal, Md Asri Ngadi   +4 more
wiley   +1 more source

A Survey for Deep Reinforcement Learning Based Network Intrusion Detection

Applied AI Letters, Volume 7, Issue 2, June 2026.
This paper surveys deep reinforcement learning (DRL) for network intrusion detection, evaluating model efficiency, minority attack detection, and dataset imbalance. Findings show DRL achieves state‐of‐the‐art results on public datasets, sometimes surpassing traditional deep learning.
Wanrong Yang, Alberto Acuto, Yihang Zhou, Dominik Wojtczak   +3 more
wiley   +1 more source

Semantic Evolution and Consistency Learning for Robust Malicious Network Traffic Detection

Engineering Reports, Volume 8, Issue 6, June 2026.
This paper proposes a semantic evolution and consistency network (SECN) for malicious traffic detection, modeling attack behaviors as temporally evolving semantics. By integrating dual‐level temporal representation and semantic consistency constraints, SECN achieves robust detection and strong generalization under encrypted, cross‐dataset, and unknown ...
Jing Yang, Wei Tan
wiley   +1 more source

A Systematic Literature Review: Classifying IoT Botnet Data Features Based on Its Lifecycle

IEEE Access
As the Internet of Things (IoT) becomes increasingly indispensable across various domains, the connectivity between humans, machines, and devices intensifies.
Shihao Liu, Fariza Fauzi, Ven Jyn Kok
doaj   +1 more source

Botnet Detection Using On-line Clustering with Pursuit Reinforcement Competitive Learning (PRCL)

Emitter: International Journal of Engineering Technology, 2018
Botnet is a malicious software that often occurs at this time, and can perform malicious activities, such as DDoS, spamming, phishing, keylogging, clickfraud, steal personal information and important data.
Yesta Medya Mahardhika, Amang Sudarsono, Ali Ridho Barakbah   +2 more
doaj   +1 more source

A Meta-Classification Model for Optimized ZBot Malware Prediction Using Learning Algorithms

Mathematics, 2023
Botnets pose a real threat to cybersecurity by facilitating criminal activities like malware distribution, attacks involving distributed denial of service, fraud, click fraud, phishing, and theft identification.
Shanmugam Jagan, Ashish Ashish, Miroslav Mahdal, Kenneth Ruth Isabels, Jyoti Dhanke, Parita Jain, Muniyandy Elangovan   +6 more
doaj   +1 more source