Anomaly detection method based on feature selection and support vector machine
Zhang Zha · Jisuanji gongcheng yu sheji · 2013
There are many problems in anomaly detection system,such as high false alarm rate,low detection rate,and bad influence of redundant features.To solve the problems,an anomaly detection method based on the feature selection and support vector machine is proposed.In order to select the feature sets with the highest classification accuracy,a feature selection algorithm is designed,which can compute classification accuracy of the constructed classification model based on features.By combining the selected feature sets and support vector machine,the anomaly detection method can detect and identify whether the data is normal or not.The simulation test results show that the method can improve the detection accuracy,reduce the detection time,and reduce the difficulty processing data by removing noise features.