Adversarial Machine Learning in the Context of Network Security: Challenges and Solutions

M Khan, L Ghafoor - Journal of Computational Intelligence …, 2024 - thesciencebrigade.com
With the increasing sophistication of cyber threats, the integration of machine learning (ML)
techniques in network security has become imperative for detecting and mitigating evolving …

Machine learning and deep learning techniques for internet of things network anomaly detection—current research trends

SH Rafique, A Abdallah, NS Musa, T Murugan - Sensors, 2024 - mdpi.com
With its exponential growth, the Internet of Things (IoT) has produced unprecedented levels
of connectivity and data. Anomaly detection is a security feature that identifies instances in …

Unveiling machine learning strategies and considerations in intrusion detection systems: a comprehensive survey

AH Ali, M Charfeddine, B Ammar, BB Hamed… - Frontiers in Computer …, 2024 - frontiersin.org
The advancement of communication and internet technology has brought risks to network
security. Thus, Intrusion Detection Systems (IDS) was developed to combat malicious …

DDoS attack detection in IoT environment using optimized Elman recurrent neural networks based on chaotic bacterial colony optimization

MIT Hussan, GV Reddy, PT Anitha, A Kanagaraj… - Cluster …, 2023 - Springer
Abstract The Internet of Things (IoT) is made up of billions of interconnected devices that can
transmit and receive data over the Internet. IoT devices have many vulnerabilities that …

Detecting cyber threats with a Graph-Based NIDPS

BOT Wen, N Syahriza, NCW Xian, NG Wei… - … Measures for Logistics …, 2024 - igi-global.com
This chapter explores the topic of a novel network-based intrusion detection system (NIDPS)
that utilises the concept of graph theory to detect and prevent incoming threats. With …

Smart and sustainable wireless electric vehicle charging strategy with renewable energy and internet of things integration

S Iqbal, NF Alshammari, M Shouran, J Massoud - Sustainability, 2024 - mdpi.com
This study addresses the challenges associated with electric vehicle (EV) charging in office
environments. These challenges include (1) reliance on manual cable connections,(2) …

[HTML][HTML] Two-step data clustering for improved intrusion detection system using CICIoT2023 dataset

HQ Gheni, WL Al-Yaseen - e-Prime-Advances in Electrical Engineering …, 2024 - Elsevier
The issue of network security is an important and delicate issue when it comes to the privacy
of organizations and individuals, especially when important and sensitive information is …

Classification of intrusion cyber‐attacks in smart power grids using deep ensemble learning with metaheuristic‐based optimization

H Naeem, F Ullah, G Srivastava - Expert Systems, 2024 - Wiley Online Library
The most advanced power grid design, known as a 'smart power grid', integrates information
and communication technology (ICT) with a conventional grid system to enable remote …

Navigating the Cyber Threat Landscape: An In-Depth Analysis of Attack Detection within IoT Ecosystems

S AboulEla, N Ibrahim, S Shehmir, A Yadav, R Kashef - AI, 2024 - mdpi.com
The Internet of Things (IoT) is seeing significant growth, as the quantity of interconnected
devices in communication networks is on the rise. The increased connectivity of devices has …

MULTI-BLOCK: A novel ML-based intrusion detection framework for SDN-enabled IoT networks using new pyramidal structure

AA Toony, F Alqahtani, Y Alginahi, W Said - Internet of Things, 2024 - Elsevier
The ever-expanding Internet of Things (IoT) landscape faces significant security challenges
due to limitations in traffic monitoring, device heterogeneity, and weak security practices …