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.

翻译：尽管多年来已开发出大量异常检测模型，但其对未见异常的泛化能力仍然存在问题，尤其是在关键系统中。本文旨在通过引入Swift Hydra来解决这一挑战，这是一个基于生成式人工智能和强化学习（RL）训练异常检测方法的新框架。该框架通过操作生成模型潜在变量的RL策略，能够合成新颖且多样化的异常样本，这些样本能够绕过检测模型。这些生成的合成样本随后被用于增强检测模型，进一步提高其处理挑战性异常的能力。Swift Hydra还集成了以专家混合（MoE）结构组织的Mamba模型，能够根据数据复杂度可扩展地调整Mamba专家的数量，有效捕获多样化的特征分布，同时不增加模型的推理时间。在ADBench基准测试上的实证评估表明，Swift Hydra在保持相对较短推理时间的同时，优于其他最先进的异常检测模型。基于这些结果，我们的研究揭示了一种将RL与生成式人工智能相结合以推进异常检测的新颖且前景广阔的范式。