We consider the sequential anomaly detection problem in the one-class setting when only the anomalous sequences are available and propose an adversarial sequential detector by solving a minimax problem to find an optimal detector against the worst-case sequences from a generator. The generator captures the dependence in sequential events using the marked point process model. The detector sequentially evaluates the likelihood of a test sequence and compares it with a time-varying threshold, also learned from data through the minimax problem. We demonstrate our proposed method's good performance using numerical experiments on simulations and proprietary large-scale credit card fraud datasets. The proposed method can generally apply to detecting anomalous sequences.

翻译：我们研究了单类设定下的序贯异常检测问题，此时仅能获取异常序列数据。通过求解一个极小极大问题，我们提出了一种对抗性序贯检测器，以针对生成器生成的最坏情况序列找到最优检测器。该生成器利用标记点过程模型捕捉序贯事件中的依赖性。检测器逐序评估测试序列的似然性，并将其与通过极小极大问题从数据中学习到的时变阈值进行比较。我们通过模拟实验和专有大规模信用卡欺诈数据集的数值实验，证明了所提方法的良好性能。该方法可普遍应用于异常序列的检测。