Semi-supervised intrusion detection via online laplacian twin support vector machine

Network security has become one of the well-known concerns in the last decades. Machine learning techniques are robust methods in detecting malicious activities and network threats. Most previous works learn offline supervised classifiers while they require large amounts of labeled examples and also should update models because the data change over time in real world applications. To alleviate these problems, we propose a novel online version of laplacian twin support vector machine classifier, which can exploit the geometry information of the marginal distribution embedded in unlabeled data to construct a more accurate and faster semi-supervised classifier. The results of experiments on large network datasets show that Online Lap-TSVM combined by two nonparallel hyper planes improves the accuracy with the comparable computing time and storage to Lap-TSVM.