Anomaly Detection UsingSVMas Classifier and Decision Tree for Optimizing FeatureVectors

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Serkani Elham; GHARAEE HOSSEIN; MOHAMMADZADEH NASER

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Paper Information

Journal: THE ISC INTERNATIONAL JOURNAL OF INFORMATION SECURITY Year:2019 | Volume:11 | Issue:2 Page(s): 159-171

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Title

Anomaly Detection UsingSVMas Classifier and Decision Tree for Optimizing FeatureVectors

Author(s)

Serkani Elham | GHARAEE HOSSEIN | MOHAMMADZADEH NASER | Issue Writer Certificate

Keywords

Intrusion DetectionQ1

Feature SelectionQ1

Support Vector MachinesQ1

Decision TreeQ1

Abstract

With the advancement and development of computer network technologies, the way for intruders has become smoother; therefore, to detect threats and attacks, the importance of Intrusion Detection systems (IDS) as one of the key elements of security is increasing. One of the challenges of Intrusion Detection systems is managing a large amount of network tra c features. Removing unnecessary features is a solution to this problem. Using machine learning methods is one of the best ways to design an Intrusion Detection system. Focusing on this issue, in this paper, we propose a hybrid Intrusion Detection system using the Decision Tree and support vector machine (SVM) approaches. In our method, the Feature Selection is initially done by the C5. 0 Decision Tree pruning, and then the features with the least predictor importance value are removed. After removing each feature, the least square support vector machine (LS-SVM) is applied. The set of features having the highest surface area under the Receiver Operating Characteristic (ROC) curve for LS-SVM are considered as nal features. The experimental results on two KDD Cup 99 and UNSW-NB15 data sets show that the proposed approach improves true positive and false positive criteria and accuracy compared to the best prior work.

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APA: Copy

Serkani, Elham, GHARAEE, HOSSEIN, & MOHAMMADZADEH, NASER. (2019). Anomaly Detection UsingSVMas Classifier and Decision Tree for Optimizing FeatureVectors. THE ISC INTERNATIONAL JOURNAL OF INFORMATION SECURITY, 11(2 ), 159-171. SID. https://sid.ir/paper/241773/en

Vancouver: Copy

Serkani Elham, GHARAEE HOSSEIN, MOHAMMADZADEH NASER. Anomaly Detection UsingSVMas Classifier and Decision Tree for Optimizing FeatureVectors. THE ISC INTERNATIONAL JOURNAL OF INFORMATION SECURITY[Internet]. 2019;11(2 ):159-171. Available from: https://sid.ir/paper/241773/en

IEEE: Copy

Elham Serkani, HOSSEIN GHARAEE, and NASER MOHAMMADZADEH, “Anomaly Detection UsingSVMas Classifier and Decision Tree for Optimizing FeatureVectors,” THE ISC INTERNATIONAL JOURNAL OF INFORMATION SECURITY, vol. 11, no. 2 , pp. 159–171, 2019, [Online]. Available: https://sid.ir/paper/241773/en

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