In today’s digital age, cyber threats are evolving faster than ever, forcing organizations to rethink traditional security measures. AI-powered cybersecurity ...

The Kill Chain models how an attack succeeds. The Attack Helix models how the offensive baseline improves. Tipping Points One person. Two AI subscriptions. Ten government agencies. 150 gigabytes of ...

An international research team developed a multi-stage intrusion detection system that uses supervised and unsupervised AI techniques to detect and mitigate cyber threats in smart renewable energy ...

Abstract: Hyperspectral anomaly detection (HAD) aims to identify targets deviating from normal patterns of background. However, the lack of labeled samples poses significant challenges to the task.

Abstract: The explosive growth of the Internet of Things (IoT) has introduced vast amounts of data and unprecedented security challenges, making effective anomaly detection in IoT environments a ...

Real-time detection of anomalies in data streams is a foundation of modern applied analysis in complex systems. It enables experts to design rapid, efficient, reliable, and high-performance decision ...

The operation of fuel cell electric vehicle-to-grid (FCEV2G) stations presents a significant challenge due to the need to manage onsite hydrogen production, storage, and vehicle dispatch in volatile ...

LogNomaly is a hybrid anomaly detection platform that analyzes system logs using machine learning and explainable AI techniques. The system parses raw logs, extracts features, detects anomalies using ...

Dr. James McCaffrey presents a complete end-to-end demonstration of anomaly detection using k-means data clustering, implemented with JavaScript. Compared to other anomaly detection techniques, ...

The proliferation of digital platforms has enabled fraudsters to deploy sophisticated camouflage techniques, such as multi-hop collaborative attacks, to evade detection. Traditional Graph Neural ...