SecurityWeek

Hacker Conversations: Joey Melo on Hacking AI

Joey Melo explains how he uses jailbreaking and data poisoning to manipulate AI guardrails and harden machine learning models ...
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

Unveiling the Generalizability Gap: A Cross-Domain Evaluation of Machine Learning Algorithms for Network Intrusion Detection

Abstract: The ever-evolving threat landscape necessitates highly adaptable Network Intrusion Detection Systems (NIDS) powered by Machine Learning (ML). However, existing ML models often exhibit ...
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 ...