Anomaly detection is of paramount importance in many real-world domains, characterized by evolving behavior. Lifelong learning represents an emerging trend, answering the need for machine learning models that continuously adapt to new challenges in dynamic environments while retaining past knowledge. However, limited efforts are dedicated to building foundations for lifelong anomaly detection, which provides intrinsically different challenges compared to the more widely explored classification setting. In this paper, we face this issue by exploring, motivating, and discussing lifelong anomaly detection, trying to build foundations for its wider adoption. First, we explain why lifelong anomaly detection is relevant, defining challenges and opportunities to design anomaly detection methods that deal with lifelong learning complexities. Second, we characterize learning settings and a scenario generation procedure that enables researchers to experiment with lifelong anomaly detection using existing datasets. Third, we perform experiments with popular anomaly detection methods on proposed lifelong scenarios, emphasizing the gap in performance that could be gained with the adoption of lifelong learning. Overall, we conclude that the adoption of lifelong anomaly detection is important to design more robust models that provide a comprehensive view of the environment, as well as simultaneous adaptation and knowledge retention.

翻译：异常检测在众多以行为动态演变为特征的现实领域中至关重要。终身学习作为一种新兴趋势，满足了机器学习模型在动态环境中持续适应新挑战、同时保留过往知识的需求。然而，相较于已广泛研究的分类场景，终身异常检测存在本质不同的挑战，当前为其建立理论基础的努力仍十分有限。本文通过探索、论证与讨论终身异常检测这一议题，试图为其更广泛的应用奠定基础。首先，我们阐述了终身异常检测的相关性，定义了设计兼顾终身学习复杂性的异常检测方法所面临的挑战与机遇。其次，我们刻画了学习场景及场景生成流程，使研究者能够利用现有数据集开展终身异常检测实验。再次，我们在所提出的终身学习场景中对主流异常检测方法进行实验，揭示了采用终身学习所能弥补的性能差距。总体而言，我们认为采用终身异常检测对于设计更鲁棒的模型至关重要，这类模型既能提供对环境的全面视角，又能实现同步适应与知识保留。