As with many other tasks, neural networks prove very effective for anomaly detection purposes. However, very few deep-learning models are suited for detecting anomalies on tabular datasets. This paper proposes a novel methodology to flag anomalies based on TracIn, an influence measure initially introduced for explicability purposes. The proposed methods can serve to augment any unsupervised deep anomaly detection method. We test our approach using Variational Autoencoders and show that the average influence of a subsample of training points on a test point can serve as a proxy for abnormality. Our model proves to be competitive in comparison with state-of-the-art approaches: it achieves comparable or better performance in terms of detection accuracy on medical and cyber-security tabular benchmark data.
翻译:与许多其他任务一样,神经网络证明对异常点探测非常有效,然而,很少有深层学习模型适合用于检测表格数据集中的异常点。本文提出了基于TracIn的反常点标记新颖方法,TracIn是最初为可推广目的采用的一种影响计量方法。拟议方法可以增强任何未经监督的深度异常点探测方法。我们使用变式自动编码器测试我们的方法,并表明测试点的次级培训点的平均影响可以替代异常点。我们的模型证明与最先进的方法相比具有竞争力:在检测医学和网络安全表格基准数据的准确性方面,它取得了可比较或更好的业绩。