Results 61 to 70 of about 4,058 (202)

Improving spam botnet detection through convolutional model and geolocation feature enhancement in a novel three-class classification task

International Journal of Intelligent Networks
Botnet 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, Muhammad Aidiel Rachman Putra, Tohari Ahmad, Choiru Za’in, Tony de Souza-Daw   +4 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, Angel Luis Perales Gomez, Felix J. Garcia Clemente, Manuel Gil Perez, Gregorio Martinez Perez   +4 more
doaj   +1 more source

A Comprehensive Systematic Review of Cyber‐Physical Systems Security: Threats, Challenges, and Defense in ICS/OT Environments

Engineering Reports, Volume 8, Issue 7, July 2026.
Overview of the paper organization, illustrating the hierarchical structure of cybersecurity domains in ICS and CPS, including attack analysis, security approaches, offensive tactics, career guidance, and concluding discussions. ABSTRACT The convergence of operational technology (OT) with IP‐based information systems has exposed industrial control ...
M. A. Khalifa, Ahmad Taher Azar, Walid El‐Shafai   +2 more
wiley   +1 more source

Chatbots in a Botnet World

International Journal on Cybernetics & Informatics, 2023
Question-and-answer formats provide a novel experimental platform for investigating cybersecurity questions. Unlike previous chatbots, the latest ChatGPT model from OpenAI supports an advanced understanding of complex coding questions. The research demonstrates thirteen coding tasks that generally qualify as stages in the MITRE ATT&CK framework ...
Forrest McKee, David Noever
openaire   +3 more sources

Analysis of the Infection and the Injection Phases of the Telnet Botnets [PDF]

Journal of Universal Computer Science, 2019
With the number of Internet of Things devices increasing, also the number of vulnerable devices connected to the Internet increases. These devices can become part of botnets and cause damage to the Internet infrastructure.
Tomáš Bajtoš, Pavol Sokol, Andrej Gajdoš, Katarína Lučivjanská, Terézia Mézešová   +4 more
doaj   +3 more sources

Graph–Time IoT IDS: Requirement‐Aligned Impact Evaluation

Engineering Reports, Volume 8, Issue 7, July 2026.
A multi‐view intrusion detection framework (IMPACT‐MVG) combines temporal behavior modeling and graph‐based interaction analysis to detect IoT network attacks. Impact‐centric evaluation using the ICSec score shows that the approach reduces operational damage from intrusions while maintaining efficient, explainable, and privacy‐aware security monitoring.
Kumkum Dubey, Isha Batra, Abhishek Bajpai, Arun Malik, Mohammed Ali Alshara, Tariqullah Jan, Naveed Ahmad, Ruhul Amin Khalil   +7 more
wiley   +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

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

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