Results 51 to 60 of about 1,235 (177)

Analysis of Botnet Domain Names for IoT Cybersecurity

IEEE Access, 2019
Botnets are widespread nowadays with the expansion of the Internet and commonly occur in many cyber-attacks, resulting in serious threats to network services and users' properties.
Wanting Li, Jian Jin, Jong-Hyouk Lee
doaj   +1 more source

A Blockchain-Enabled Secure Digital Twin Framework for Early Botnet Detection in IIoT Environment

Sensors, 2022
Resource constraints in the Industrial Internet of Things (IIoT) result in brute-force attacks, transforming them into a botnet to launch Distributed Denial of Service Attacks.
Mikail Mohammed Salim, Alowonou Kowovi Comivi, Tojimurotov Nurbek, Heejae Park, Jong Hyuk Park   +4 more
doaj   +1 more source

OntoLogX: Ontology‐Guided Knowledge Graph Extraction From Cybersecurity Logs With Large Language Models

Advanced Intelligent Systems, Volume 8, Issue 6, June 2026.
OntoLogX is an autonomous AI agent that uses large language models to transform unstructured cyber security logs into ontology grounded knowledge graphs. By integrating retrieval augmented generation, iterative correction, and a light‐weight log ontology, OntoLogX produces semantically consistent intelligence that links raw log events to MITRE ATT & CK
Luca Cotti, Idilio Drago, Anisa Rula, Devis Bianchini, Federico Cerutti   +4 more
wiley   +1 more source

Adaptive continual botnet detection with self-supervised prototypical networks and few-shot learning for evolving botnet detection

Frontiers in Artificial Intelligence
IntroductionOne of the problems faced by botnet intrusion detection systems is the detection of unknown attacks or zero-day attacks. Zero-day attacks give security analysts very little time for action since there is only little information that is known ...
Niranjana Junar, Karmel Arockiasamy
doaj   +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

B-CAT: a model for detecting botnet attacks using deep attack behavior analysis on network traffic flows

Journal of Big Data
Threats on computer networks have been increasing rapidly, and irresponsible parties are always trying to exploit vulnerabilities in the network to do various dangerous things.
Muhammad Aidiel Rachman Putra, Tohari Ahmad, Dandy Pramana Hostiadi   +2 more
doaj   +1 more source

Computationally Efficient Deep Federated Learning with Optimized Feature Selection for IoT Botnet Attack Detection

Intelligent Systems with Applications
Internet of Things (IoT) is a technology that has revolutionized various fields, offering numerous benefits, such as remote patient monitoring, enhanced energy efficiency, and automation of routine tasks in homes.
Lambert Kofi Gyan Danquah, Stanley Yaw Appiah, Victoria Adzovi Mantey, Iddrisu Danlard, Emmanuel Kofi Akowuah   +4 more
doaj   +1 more source

Generating Pattern‐Based Datasets for Cyber Attack Detection Using Machine‐Learning Techniques

WIREs Data Mining and Knowledge Discovery, Volume 16, Issue 2, June 2026.
The aim of this work is to review the state of the art in the design, generation, and labeling of attack pattern datasets for training of detection systems based on machine learning. ABSTRACT This work aims to review the state of the art in the design, generation, and labeling of attack pattern datasets for the training of detection systems based on ...
Pedro Díaz García, Diego López Abril, Javier Bermejo Higuera, Víctor Gonzalez Llorente, Miguel Angel Sicilia Urbán   +4 more
wiley   +1 more source

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

Energy Conversion and Economics, Volume 7, Issue 3, Page 207-220, June 2026.
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

BOTNET ATTACK IN COMPUTER NETWORK SECURITY

EPRA International Journal of Multidisciplinary Research (IJMR), 2020
Among the various forms of malware, Botnet is the serious threat which occurs commonly in today’s cyber attacks and cyber crimes. Botnet are designed to perform predefined functions in an automated fashion, where these malicious activities ranges from online searching of data, accessing lists, moving files ...
null S.Nagendra Prabhu, null D.Shanthi
openaire   +1 more source