A Novel Intrusion Detection Scheme Using Cloud Grey Wolf Optimizer
Honghao Yang, Zhiping Zhou · 2018
In order to solve the problems of low detection efficiency caused by the lack of sufficient training set and dynamic change of attacks. A new adaptive and effective method of industrial control network intrusion detection model is proposed, namely, semi-supervised intrusion detection model optimized by a cloud grey wolf optimization (CGWO) algorithm, which takes into account the balance of the exploration and exploitation abilities, simultaneously do parameter setting for semi-supervised learning and the one-class support vector machine. In the training stage utilize semi-supervised learning algorithm modified by the CGWO to obtain a large-scale training dataset by using a few of label data, according to the characteristics of the industrial control network layer data. The performance of the proposed method has been evaluated demonstrable efficiently and reliably by a comparison and analysis of the simulation, in terms of having a high detection rate and a low false alarm rate without feature selection.