Theoretical framework can predict how complex networks behave

The University of Hong Kong (HKU) has spearheaded an international research collaboration to develop a pioneering theoretical ...
Devdiscourse

IoT threat detection moves to transparent, decentralized AI systems

A new study published in Frontiers in Computer Science introduces a decentralized cybersecurity model that combines federated ...
IEEE

Evaluating Adversarial Resilience of Deep Reinforcement Learning Algorithms for Network Intrusion Detection

Abstract: Recent advancements in the field of cybersecurity has allowed for greater defensive technologies. The application of machine learning and artificial intelligence in cybersecurity has allowed ...
Scientific Research Publishing

Attack Detection and Alarming System on IOT Facilities Using Random Forest Enabled-Correlation Based Clustering (RF-CBC) Technique ()

1 Department of Mathematical and Computer Sciences, Faculty of Science, University of Medical Sciences, Ondo, Nigeria. 2 Department of Computer Science, University of New Haven, West Haven, USA. 3 ...
IEEE

Unsupervised Real-Time In-Kernel Intrusion Detection System Using Autoencoders and eBPF

Abstract: Traditional intrusion detection systems (IDSs), leveraging machine learning (ML) algorithms, have improved the detection accuracy of unknown attacks by continuously updating ML models but ...
GitHub

Network Intrusion Detection

Cyberattacks are escalating at a staggering rate globally. Intrusion prevention systems continuously monitor network traffic, looking for possible malicious incidents, containing the threat and ...