Title: COMPUTER INTRUSION DETECTION WITH CLASSIFICATION AND ANOMALY DETECTION, USING SVMs
Abstract:This paper describes experiences and results applying Support Vector Machine (SVM) to a Computer Intrusion Detection (CID) dataset. First, issues in supervised classification are discussed, then the i...This paper describes experiences and results applying Support Vector Machine (SVM) to a Computer Intrusion Detection (CID) dataset. First, issues in supervised classification are discussed, then the incorporation of anomaly detection enhancing the modeling and prediction of cyber-attacks. SVM methods are seen as competitive with benchmark methods and other studies, and are used as a standard for the anomaly detection investigation. The anomaly detection approaches compare one class SVMs with a thresholded Mahalanobis distance to define support regions. Results compare the performance of the methods and investigate joint performance of classification and anomaly detection. The dataset used is the DARPA/KDD-99 publicly available dataset of features from network packets, classified into nonattack and four-attack categories.Read More
Publication Year: 2003
Publication Date: 2003-05-01
Language: en
Type: article
Indexed In: ['crossref']
Access and Citation
Cited By Count: 29
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