Anomaly Detection Model

Anomaly detection models aim to identify unusual patterns or outliers in data, crucial for various applications like cybersecurity and predictive maintenance. Current research emphasizes improving model robustness across diverse data types (e.g., time series, images, tabular data) and distribution shifts, often employing deep learning architectures such as transformers, variational autoencoders, and neural networks (including LSTMs and CNNs). This field is vital for enhancing the reliability and trustworthiness of automated systems, driving advancements in explainability, fairness, and efficiency to ensure responsible deployment in high-stakes domains.