  Anomaly detection in sequential data has been studied for a long time because
of its potential in various applications, such as detecting abnormal system
behaviors from log data. Although many approaches can achieve good performance
on anomalous sequence detection, how to identify the anomalous entries in
sequences is still challenging due to a lack of information at the entry-level.
In this work, we propose a novel framework called CFDet for fine-grained
anomalous entry detection. CFDet leverages the idea of interpretable machine
learning. Given a sequence that is detected as anomalous, we can consider
anomalous entry detection as an interpretable machine learning task because
identifying anomalous entries in the sequence is to provide an interpretation
to the detection result. We make use of the deep support vector data
description (Deep SVDD) approach to detect anomalous sequences and propose a
novel counterfactual interpretation-based approach to identify anomalous
entries in the sequences. Experimental results on three datasets show that
CFDet can correctly detect anomalous entries.
