Results 71 to 80 of about 3,020 (212)

Understanding the Autonomous Electric Vehicle Cyber Threat Landscape: A Focus on Infrastructure, Threats and Ontology‐Based Modelling

Energy Internet, Volume 3, Issue 1, Page 39-51, April 2026.
ABSTRACT The development of autonomous electric vehicles (AEVs) represents the convergence of two simultaneous automotive revolutions: electric vehicles (EVs) and autonomous vehicles (AVs). AVs require sensors, decision‐making systems and actuation systems to achieve autonomous driving, whereas EVs require intelligent management and real‐time ...
Ohud Alsadi, Abdulrahman Almijlad, Trinadh Pamulapati, Rajiv Ranjan, Devki Jha, Tejal Shah   +5 more
wiley   +1 more source

An Overview of Deep Learning Techniques for Big Data IoT Applications

International Journal of Communication Systems, Volume 39, Issue 4, 10 March 2026.
Reviews deep learning integration with cloud, fog, and edge computing in IoT architectures. Examines model suitability across IoT applications, key challenges, and emerging trends Provides a comparative analysis to guide future deep learning research in IoT environments.
Gagandeep Kaur, Vipin Balyan, Sindhu Hak Gupta   +2 more
wiley   +1 more source

Deep Learning-Based Intrusion Detection System for Detecting IoT Botnet Attacks: A Review

IEEE Access
The proliferation of Internet of Things (IoT) devices has brought about an increased threat of botnet attacks, necessitating robust security measures. In response to this evolving landscape, deep learning (DL)-based intrusion detection systems (IDS) have
Tamara Al-Shurbaji, Mohammed Anbar, Selvakumar Manickam, Iznan H Hasbullah, Nadia Alfriehat, Basim Ahmad Alabsi, Ahmad Reda Alzighaibi, Hasan Hashim   +7 more
doaj   +1 more source

Tomtit‐Raven Evolutionary Selector‐Reinforced Attention‐Driven: A High‐Performance and Computationally Efficient Cyber Threat Detection Framework for Smart Grids

Energy Science &Engineering, Volume 14, Issue 3, Page 1431-1455, March 2026.
Overview of the proposed work. ABSTRACT Identifying cyber threats maintains the security and operational stability of smart grid systems because they experience escalating attacks that endanger both operating data reliability and system stability and electricity grid performance.
Priya R. Karpaga, Praveen Kumar Balachandran, Umawathy Techanamurthy, Muhammad Ammirrul Atiqi Mohd Zainuri   +3 more
wiley   +1 more source

Edge‐Oriented DoS/DDoS Intrusion Detection and Supervision Platform

SECURITY AND PRIVACY, Volume 9, Issue 2, March/April 2026.
ABSTRACT This work presents an Edge Node‐Oriented DoS/DDoS Intrusion Detection and Monitoring Platform, a novel anomaly detection system based on temporal analysis with machine learning (ML) and deep learning (DL) algorithms, specifically designed to operate on edge servers with limited resources.
Geraldo Eufrazio Martins Júnior, Thales Guimaraes Marques, Wendley Souza da Silva, Joaquim Celestino Junior   +3 more
wiley   +1 more source

Multi-phase IRC Botnet and Botnet Behavior Detection Model

CoRR, 2015
Botnets are considered one of the most dangerous and serious security threats facing the networks and the Internet. Comparing with the other security threats, botnet members have the ability to be directed and controlled via C&C messages from the botmaster over common protocols such as IRC and HTTP, or even over covert and unknown applications.
Aymen Hasan Rashid Al Awadi, Bahari Belaton   +1 more
openaire   +2 more sources

Securing the Unseen: A Comprehensive Exploration Review of AI‐Powered Models for Zero‐Day Attack Detection

Expert Systems, Volume 43, Issue 3, March 2026.
ABSTRACT Zero‐day exploits remain challenging to detect because they often appear in unknown distributions of signatures and rules. The article entails a systematic review and cross‐sectional synthesis of four fundamental model families for identifying zero‐day intrusions, namely, convolutional neural networks (CNN), deep neural networks (DNN ...
Abdullah Al Siam, Nuruzzaman Faruqui, Akm Azad, Mohammad Ali Moni   +3 more
wiley   +1 more source

Botnet Behavior Detection using Network Synchronism

, 2011
Botnets’ diversity and dynamism challenge detection and classification algorithms depend heavily on static or protocol-dependant features. Several methods showing promising results were proposed using behavioral-based approaches. The authors conducted an
Zunino Suarez, Alejandro Octavio, Campo, Marcelo Ricardo, García, Sebastián   +2 more
core  

A Practical Botnet Traffic Detection System Using GNN

, 2022
Botnet attacks have now become a major source of cyber-attacks. How to detect botnet traffic quickly and efficiently is a current problem for most enterprises.
Kyungmi Lee, Lee, Ickjai, Li, Jingjin, Chao Chen, Chen, Chao, Lee, Kyungmi, Ickjai Lee, Zhang, Bonan, Jingjin Li, Bonan Zhang   +9 more
core   +1 more source

A Two‐Phase Detection Method Based on Ensemble Feature Fusion for Detecting Distributed Denial of Service (DDoS) Attacks in Cloud Computing Using Deep Learning Algorithm

Engineering Reports, Volume 8, Issue 2, February 2026.
This study introduces a two‐phase method for detecting DDoS attacks in cloud environments using ensemble feature fusion and a hybrid CNN‐LSTM model. By combining meta‐heuristic feature selection with deep learning, the approach achieves over 99% accuracy on benchmark datasets, reducing false positives and improving cybersecurity resilience.
Hind Saad Hussein, Farhad Navabifar, Fariba Majidi, Hayder Kadhim Hammood   +3 more
wiley   +1 more source