Intrusion Detection System Based on Decision Tree and Clustered Continuous Inputs
With the rapid expansion of computer networks during the past decade, security has become a crucial issue for computer systems. Different soft-computing based methods have been proposed in recent years for the development of intrusion detection systems (IDSs). The purpose of this paper is to use ID3...
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| Main Author: | |
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| Format: | Article |
| Language: | English |
| Published: |
Mosul University
2011-07-01
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| Series: | Al-Rafidain Journal of Computer Sciences and Mathematics |
| Subjects: | |
| Online Access: | https://csmj.mosuljournals.com/article_163610_ae9b3b4daf227df4cd257e416ac7d6b9.pdf |
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| Summary: | With the rapid expansion of computer networks during the past decade, security has become a crucial issue for computer systems. Different soft-computing based methods have been proposed in recent years for the development of intrusion detection systems (IDSs). The purpose of this paper is to use ID3 algorithm for IDS and extend it to deal not only with discreet values, but also with continuous ones, by using K_mean algorithm to partition each continuous attribute values to three clusters. The full 10% KDD Cup 99 train dataset and the full Correct test dataset are used. The results of the proposed method show an improvement in the performance as compared to standard ID3 using classical partition method. |
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| ISSN: | 1815-4816 2311-7990 |