Open AccessA Deep One-class Model for Network Anomaly Detection
Author(s) -
Songlin Dai,
Jian Yan,
Xiaoming Wang,
Lin Zhang
Publication year - 2019
Publication title -
iop conference series. materials science and engineering
Language(s) - English
Resource type - Journals
eISSN - 1757-899X
pISSN - 1757-8981
DOI - 10.1088/1757-899x/563/4/042007
Subject(s) - anomaly detection , computer science , artificial intelligence , classifier (uml) , data mining , feature selection , class (philosophy) , pattern recognition (psychology) , anomaly (physics) , one class classification , machine learning , support vector machine , raw data , physics , programming language , condensed matter physics
For traditional network anomaly detection system, the detection performance is related to the selected features and training dataset. But traditional methods adopt handcraft feature selection, which requires heavy human labour and relies on the experts’ knowledge and experience. Besides, the collected dataset for training is not balanced, which makes the prediction of the trained model tends to be biased to the majority class. In this paper, a one-class network anomaly detection model based on the stacked autoencoders was proposed. We use the stacked autoencoders to select the prominent features from the raw collected data, then apply the one-class classification algorithm support vector data description to train a classifier to identify the network traffic into normal data and anomalous data. The experimental results demonstrate the promising results of our approach for network anomaly detection.