Anomaly detection is essential in many application domains, such as cyber
security, law enforcement, medicine, and fraud protection. However, the
decision-making of current deep learning approaches is notoriously hard to
understand, which often limits their practical applicability. To overcome this
limitation, we propose a framework for learning inherently interpretable
anomaly detectors from sequential data. More specifically, we consider the task
of learning a deterministic finite automaton (DFA) from a given multi-set of
unlabeled sequences. We show that this problem is computationally hard and
develop two learning algorithms based on constraint optimization. Moreover, we
introduce novel regularization schemes for our optimization problems that
improve the overall interpretability of our DFAs. Using a prototype
implementation, we demonstrate that our approach shows promising results in
terms of accuracy and F1 score.

提出从序列数据中学习可解释异常检测器的框架，采用确定性有限自动机 (DFA) 作为模型，并通过约束优化算法和正则化策略提高模型的可解释性和准确性。

离散优化的可解释异常检测

Interpretable Anomaly Detection via Discrete Optimization

Out-of-distribution (OoD) detection for data-based programs is a goal of
paramount importance. Common approaches in the literature tend to train
detectors requiring inside-of-distribution (in-distribution, or IoD) and OoD
validation samples, and/or implement confidence metrics that are often abstract
and therefore difficult to interpret. In this work, we propose to use existing
work from the field of explainable AI, namely the PARTICUL pattern
identification algorithm, in order to build more interpretable and robust OoD
detectors for visual classifiers. Crucially, this approach does not require to
retrain the classifier and is tuned directly to the IoD dataset, making it
applicable to domains where OoD does not have a clear definition. Moreover,
pattern identification allows us to provide images from the IoD dataset as
reference points to better explain the confidence scores. We demonstrates that
the detection capabilities of this approach are on par with existing methods
through an extensive benchmark across four datasets and two definitions of OoD.
In particular, we introduce a new benchmark based on perturbations of the IoD
dataset which provides a known and quantifiable evaluation of the discrepancy
between the IoD and OoD datasets that serves as a reference value for the
comparison between various OoD detection methods. Our experiments show that the
robustness of all metrics under test does not solely depend on the nature of
the IoD dataset or the OoD definition, but also on the architecture of the
classifier, which stresses the need for thorough experimentations for future
work on OoD detection.

该研究提出使用可解释 AI 领域的 PARTICUL 算法以构建更易解释、更具鲁棒性的视觉分类器的异常检测器，无需重新训练分类器，适用于缺乏明确定义的领域，并通过四个数据集和两个 OoD 定义的广泛基准测试证明了该方法的检测能力与现有方法相当，同时强调了分类器结构对鲁棒性的影响。