Results 61 to 70 of about 1,235 (177)

Stochastic Modeling of IoT Botnet Spread: A Short Survey on Mobile Malware Spread Modeling

IEEE Access, 2020
The Internet of Things (IoT) devices are being widely deployed and have been targeted and victimized by malware attacks. The mathematical modelling for an accurate prediction of malicious spreads of botnets across IoT networks is of great importance ...
Arash Mahboubi, Seyit Camtepe, Keyvan Ansari   +2 more
doaj   +1 more source

AI‐Powered Anomaly Detection for Secure Internet of Things (IoT): Optimising XGBoost and Deep Learning With Bayesian Optimisation

CAAI Transactions on Intelligence Technology, Volume 11, Issue 2, Page 447-463, April 2026.
ABSTRACT Intelligent and adaptive defence systems that can quickly thwart changing cyberthreats are becoming more and more necessary in the dynamic and data‐intensive Internet of things (IoT) environment. Using the NSL‐KDD benchmark dataset, this paper presents an improved anomaly detection system that combines an optimised sequential neural network ...
Seong‐O Shim, Lal Hussain, Mohammed A. Alqarni, Faisal S. Alsubaei, Rasha Jamal Atwah   +4 more
wiley   +1 more source

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

Robust and Noise-Resilient Botnet Detection Framework Using Heterogeneous Radial Basis Function Neural Network

Applied Sciences
The rapid evolution of botnet attacks poses a critical challenge facing cybersecurity, necessitating the development of intrusion detection models that are both highly accurate and computationally efficient.
Lama Awad, Sherenaz Al-Haj Baddar, Azzam Sleit   +2 more
doaj   +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

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

Modular neural network for edge-based detection of early-stage IoT botnet

High-Confidence Computing
The Internet of Things (IoT) has led to rapid growth in smart cities. However, IoT botnet-based attacks against smart city systems are becoming more prevalent.
Duaa Alqattan, Varun Ojha, Fawzy Habib, Ayman Noor, Graham Morgan, Rajiv Ranjan   +5 more
doaj   +1 more source

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

Network Simulator for Botnet DoS Attacks

Information Technologies and Control, 2017
Abstract The most serious appearance of modern malware is Botnet. Botnet is a fast growing problem that is still not well understood and studied. One widely spreading variant of the botnet attacks is an IRC botnet. There are a number of techniques to detect botnet attacks and infections, but they do not have the functionality to prevent malicious ...
Y. Aleksieva, H. Valchanov
openaire   +1 more source