[HTML][HTML] A novel hybrid unsupervised learning approach for enhanced cybersecurity in the IoT

P Kaliyaperumal, S Periyasamy, M Thirumalaisamy… - Future Internet, 2024 - mdpi.com
The proliferation of IoT services has spurred a surge in network attacks, heightening
cybersecurity concerns. Essential to network defense, intrusion detection and prevention …

A Review of Generative Adversarial Networks for Intrusion Detection Systems: Advances, Challenges, and Future Directions.

M Al-Ajlan, M Ykhlef - Computers, Materials & Continua, 2024 - search.ebscohost.com
The ever-growing network traffic threat landscape necessitates adopting accurate and
robust intrusion detection systems (IDSs). IDSs have become a research hotspot and have …

Bayes-Optimized Adaptive Growing Neural Gas Method for Online Anomaly Detection of Industrial Streaming Data

J Zhang, L Guo, S Gao, M Li, C Hao, X Li, L Song - Applied Sciences, 2024 - mdpi.com
Online anomaly detection is critical for industrial safety and security monitoring but is facing
challenges due to the complexity of evolving data streams from working conditions and …

[CITATA][C] The detection of network cyber attacks using machine learning

M Sarhan - 2023 - espace.library.uq.edu.au
Network Intrusion Detection Systems (NIDSs) are essential components of modern network
security controls, with the primary objective of detecting cyber-attacks and malicious …

[CITATA][C] Graph representation learning for cyberattack detection and forensics

WW Lo - 2023 - espace.library.uq.edu.au
Over the past few decades, machine learning-based cybersecurity attack detection systems
have become widely used to combat sophisticated cyberattacks. Many previous studies …