Efficient Anomaly Detection

Efficient anomaly detection aims to identify unusual patterns or events in data streams, crucial for various applications like cybersecurity, industrial monitoring, and cloud system management. Current research emphasizes developing models that are both accurate and computationally efficient, focusing on architectures like autoencoders, transformers, and frequency-domain methods, often incorporating techniques like self-supervised learning and federated learning to handle large datasets and distributed systems. These advancements improve the speed and accuracy of anomaly detection, leading to more robust systems and enabling real-time responses to critical events across diverse domains.