Anomaly Detection Algorithm

Anomaly detection algorithms aim to identify data points deviating significantly from established norms, a crucial task across diverse fields like astronomy, industrial monitoring, and cybersecurity. Current research emphasizes unsupervised methods, particularly Isolation Forests and variations of LSTM networks, as well as classifier-based approaches that leverage the latent space of neural networks for improved anomaly scoring. These advancements are driving improvements in real-time anomaly detection across various data types (including time series and images), enhancing system reliability and enabling more effective decision-making in safety-critical applications.