Anomaly Detection Based on PMF Encoding and Adversarially Learned Inference

Abstract

In order to solve the problem of increasing the dimension and sparse feature space caused by the categorization coding method in the existing abnormal traffic detection problem, a coding method based on Probability Mass Function (PMF) is proposed. Secondly, in order to improve the ability of abnormal traffic detection algorithms to identify unknown attack type data and improve detection efficiency, we use Adversarially Learned Inference as the basic detection algorithm. The comparison experiments on the standard dataset show that the proposed method has improved the accuracy and detection efficiency greatly compared with the existing anomaly detection methods.