Lightweight Anomaly Detection

Lightweight anomaly detection focuses on developing efficient algorithms and systems for identifying unusual patterns in data streams, particularly in resource-constrained environments like edge devices and distributed networks. Current research emphasizes real-time online learning methods, often employing statistical learning or lightweight deep learning architectures, and incorporating techniques like divide-and-conquer strategies and federated learning to improve scalability and privacy. This field is crucial for enhancing the reliability and security of various applications, including intrusion detection, industrial monitoring, and autonomous driving, by enabling timely anomaly detection with minimal computational overhead.