Semi-supervised anomaly detection algorithm based on KL divergence (SAD-KL)

TL;DR

This paper introduces SAD-KL, a semi-supervised anomaly detection method that uses KL divergence to handle distribution differences between labeled and unlabeled data, improving detection accuracy and efficiency.

Contribution

The paper proposes a novel semi-supervised anomaly detection algorithm utilizing KL divergence to address distribution gaps between labeled and unlabeled data.

Findings

SAD-KL outperforms existing algorithms in detection probability.

SAD-KL requires less learning time.

The PDFs of local outlier factors follow Burr distribution.

Abstract

The unlabeled data are generally assumed to be normal data in detecting abnormal data via semisupervised learning. This assumption, however, causes inevitable detection error when distribution of unlabeled data is different from distribution of labeled normal dataset. To deal the problem caused by distribution gap between labeled and unlabeled data, we propose a semi-supervised anomaly detection algorithm using KL divergence (SAD-KL). The proposed SAD-KL is composed of two steps: (1) estimating KL divergence of probability density functions (PDFs) of the local outlier factors (LOFs) of the labeled normal data and the unlabeled data (2) estimating detection probability and threshold for detecting normal data in unlabeled data by using the KL divergence. We show that the PDFs of the LOFs follow Burr distribution and use them for detection. Once the threshold is computed, the SAD-KL runs iteratively until the labeling change rate is lower than the predefined threshold. Experiments results show that the SAD-KL shows superior detection probability over the existing algorithms even though it takes less learning time.