Results 61 to 70 of about 11,129 (248)
International Journal of Intelligent NetworksBotnet detection remains a critical and challenging area in the field of information security, primarily due to the intricate architectures and sophisticated attack mechanisms employed by botnets.
Florentino Benedictus +4 more
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 +4 more
wiley +1 more source
Enhanced PeerHunter: Detecting Peer-to-Peer Botnets Through Network-Flow Level Community Behavior Analysis [PDF]
IEEE Transactions on Information Forensics and Security, 2018 Peer-to-peer (P2P) botnets have become one of the major threats in network security for serving as the fundamental infrastructure for various cyber-crimes.
Di Zhuang, J. M. Chang
semanticscholar +1 more source
DETECTION OF BOTNETS USING INVARIANT REPRESENTATION.
, 2019 Over the past few decades, botnets are known to be a serious threat to the cyber security. The botnets are the systems in a particular network environment that are commanded by the attacker also known as Bot herder through C & C channel and hence targets
V.Bhattacharya., Moinak Bhattacharya
core +2 more sources
Botnet Defense System: Concept, Design, and Basic Strategy
Information, 2020 This paper proposes a new kind of cyber-security system, named Botnet Defense System (BDS), which defends an Internet of Things (IoT) system against malicious botnets. The concept of BDS is “Fight fire with fire”.
Shingo Yamaguchi
doaj +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 +3 more
wiley +1 more source
Cyber espionage through Botnets
Security Journal, 2019 Botnets, the groups of illegally controlled infected devices on the Internet have had a history of two decades already. This history shows an evolution of the infection techniques, the scope of the target devices, and their usage. Thus, the new direction
Zsolt Bederna, Tamás Szádeczky
semanticscholar +1 more source
Botnet Identification on Twitter: A Novel Clustering Approach Based on Similarity
IEEE AccessDue to Twitter’s potential reach and influence, malicious automated accounts and services have been operating and growing without control.
Luis Daniel Samper-Escalante +3 more
doaj +1 more source
A Self-Adaptive Deep Learning-Based System for Anomaly Detection in 5G Networks
IEEE Access, 2018 The upcoming fifth-generation (5G) mobile technology, which includes advanced communication features, is posing new challenges on cybersecurity defense systems.
Lorenzo Fernandez Maimo +4 more
doaj +1 more source
Botnet Detection in IoT Devices Using Random Forest Classifier with Independent Component Analysis
Journal of ICT, 2022 With rapid technological progress in the Internet of Things (IoT), it has become imperative to concentrate on its security aspect. This paper represents a model that accounts for the detection of botnets through the use of machine learning algorithms ...
Nazmus Sakib Akash +4 more
doaj +1 more source