Deep Learning Based Anomaly Detection

Deep learning-based anomaly detection aims to identify unusual patterns or outliers in data, crucial for various applications like network security and industrial monitoring. Current research emphasizes developing robust and efficient models, including graph neural networks for complex relationships, hybrid approaches combining deep learning with traditional methods (e.g., DBSCAN), and architectures tailored for time-series data (e.g., LSTMs and Transformers). These advancements improve accuracy, reduce false positives, and enhance interpretability, leading to more reliable and actionable insights across diverse fields.