Swift Hydra: Self-Reinforcing Generative Framework for Anomaly Detection with Multiple Mamba Models
This addresses the problem of improving anomaly detection generalization for critical systems, representing a novel paradigm rather than an incremental improvement.
The paper tackles the challenge of generalizing anomaly detection to unseen anomalies by introducing Swift Hydra, a framework that uses reinforcement learning and generative AI to synthesize diverse anomaly samples for data augmentation, achieving state-of-the-art performance on the ADBench benchmark with short inference times.
Despite a plethora of anomaly detection models developed over the years, their ability to generalize to unseen anomalies remains an issue, particularly in critical systems. This paper aims to address this challenge by introducing Swift Hydra, a new framework for training an anomaly detection method based on generative AI and reinforcement learning (RL). Through featuring an RL policy that operates on the latent variables of a generative model, the framework synthesizes novel and diverse anomaly samples that are capable of bypassing a detection model. These generated synthetic samples are, in turn, used to augment the detection model, further improving its ability to handle challenging anomalies. Swift Hydra also incorporates Mamba models structured as a Mixture of Experts (MoE) to enable scalable adaptation of the number of Mamba experts based on data complexity, effectively capturing diverse feature distributions without increasing the model's inference time. Empirical evaluations on ADBench benchmark demonstrate that Swift Hydra outperforms other state-of-the-art anomaly detection models while maintaining a relatively short inference time. From these results, our research highlights a new and auspicious paradigm of integrating RL and generative AI for advancing anomaly detection.