| Paper Details: | Downloads: 1885 |
| Serial Number: | P1141525386
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| Title: | A Comparison between C4.5, MLP, SVM for Network Intrusion Detection based Feature Selection
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| Authors: | Alaa F. Sheta and Amneh Alamleh
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| Abstract: | The volume of targeted network attacks is increasing continuously over time. This causes great financial loss. Intrusion Detection Systems (IDSs) is one of the main solutions for computer and network security. We need IDS to identify the un-authorized access that attempt to compromise confidentiality, integrity or availability of computer or computer network. In this paper, we attempt to provide new models for intrusion detection (ID) problem using Decision Tree (DT) based C4.5 algorithm, Multi-Layer Perceptron (MLP) and Support Vector Machine (SVM). Number of attacks were classified using the three methods. A training and testing data proposed by DARPA is used to develop and evaluate these proposed models. To enhance the performance of the proposed models and speeding up the detection process, a set of features are selected using the Best First Search (BFS) and the Genetic Search (GS). A comparison between the models developed in each case shall be provided. The proposed models were capable of reducing the complexity while keeping acceptable detection accuracy.
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| Keywords: | Network Security, Intrusion Detection, Classification, C4.5, Artificial Neural Networks, Support Vector Machine, KDD, NSL-KDD
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| Journal/Conference: | International Journal of Computer Networks and Internet Research
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| Volume: | 15
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| Issue: | 1
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| Submission Date: | 6/14/2015 12:00:00 AM
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| Review Date: | 7/7/2015 12:00:00 AM
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| Publishing Date: | 12/15/2015 12:00:00 AM
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| Article Downloads: | 1885
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