APIB-GAN: A generative adversarial networks based approach for anomaly prediction of internet behavior

Abstract

Anomaly detection focuses on finding non-conforming patterns of expected behavior in the data. In various application fields, these non-conforming patterns are often called anomalies, outliers, etc. With the wide spread of the Internet in various application fields, a large amount of Internet behavior log data is generated, which contains lots of private information. To protect sensitive data, the Internet behavior data of employees in a company are studied. Because of the high cost of manual labeling data, an unsupervised generative model, Anomaly Prediction of Internet behavior based on Generative Adversarial Networks (APIBGAN), is proposed to predict anomalies in online behaviors only with a small amount of labeled data. Three categories of Internet behavior data are studied with APIBGAN: (1) Online behavior data of an employee within a department. (2) Online behavior data of multiple employees in a department. (3) Online behavior data of multiple employees in multiple departments. The prediction accuracy of the above three cases is 87.23 %, 85.13 %, and 83.47 %, respectively, which shows that the outlier of Internet behavior can be accurately predicted only through a three-layers Fully Connected Neural Network (FNN) and it is more effective with the simple data distribution. The experimental results prove that Generative Adversarial Networks (GAN) has broad application prospects for anomaly prediction of Internet behavior data and those data with large volume that are not easy to manual labeling, such as financial data, biomedical data, etc.