That challenge is examined in the study Towards Eco-Friendly Cybersecurity: Machine Learning-Based Anomaly Detection with ...

Abstract: The rise of IoT systems and edge computing creates an urgent need for efficient log anomaly detection on resource-constrained devices. Traditional centralized log analysis methods are ...

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, ...

5.1 RQ1: How does our proposed anomaly detection model perform compared to the baselines? 5.2 RQ2: How much does the sequential and temporal information within log sequences affect anomaly detection?

1 Analytics Department, School of Computer Science and Engineering, Vellore Institute of Technology, Vellore, Tamil Nadu, India 2 Department of Data Science, School of Computer Science and Engineering ...

Cloud infrastructure anomalies cause significant downtime and financial losses (estimated at $2.5 M/hour for major services). Traditional anomaly detection methods fail to capture complex dependencies ...

Introduction: Recent advances in artificial intelligence have created opportunities for medical anomaly detection through multimodal learning frameworks. However, traditional systems struggle to ...