Results 41 to 50 of about 4,679 (205)

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

An anomaly-based botnet detection approach for identifying stealthy botnets [PDF]

2011 IEEE International Conference on Computer Applications and Industrial Electronics (ICCAIE), 2011
Botnets (networks of compromised computers) are often used for malicious activities such as spam, click fraud, identity theft, phishing, and distributed denial of service (DDoS) attacks. Most of previous researches have introduced fully or partially signature-based botnet detection approaches.
Sajjad Arshad, Maghsoud Abbaspour, Mehdi Kharrazi, Hooman Sanatkar   +3 more
openaire   +2 more sources

A mobile botnet detection and response model [PDF]

, 2017
Mobile botnet exploitation in smartphone could implicate to the leakage of sensitive and private information, loss of financial and degradation of smartphone’s performance thus affecting organisations or users that rely on smartphones for business and ...
Azlina Md. Yassin, Md. Yassin, Azlina, JeyaLechumy Perumal Naidu, Norjariah Arif, Arif, Norjariah, Perumal Naidu, Jeya Lechumy, Abdullah, Zubaile   +6 more
core   +1 more source

Botnet Detection Approach Using Graph-Based Machine Learning

IEEE Access, 2021
Detecting botnet threats has been an ongoing research endeavor. Machine Learning (ML) techniques have been widely used for botnet detection with flow-based features.
Afnan Alharbi, Khalid Alsubhi
doaj   +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

Visual analytics with decision tree on network traffic flow for botnet detection [PDF]

, 2018
Visual analytics (VA) is an integral approach combining visualization, human factors, and data analysis. VA can synthesize information and derive insight from massive, dynamic, ambiguous and often conflicting data.
Ismail, Saiful Adli, Amir Sjarif, Nilam Nur, Che Mohd. Yusof, Rasimah, Ya’acob, Suraya, Mohd. Azmi, Nurulhuda Firdaus, Muhammad, Muhammad Khairul Rijal   +5 more
core  

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

Botne and Botnet Detection Survey

, 2013
Among the various forms of malware, Botnets are emerging as the most serious threat, Botnets, remotely controlled by the attackers, and whose members are located in homes, schools, businesses, and governments around the world.
Maisireem Kamal, Manar Ahmad
core   +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