Makkithaya, Krishnamoorthi and Subbareddy , NV and Acharya, Dinesh U (2008) Improved C-Fuzzy Decision Tree for Intrusion Detection. World Academy of Science, Engineering and Technology (42). pp. 273-277. ISSN 2010-3778
![[img]](http://eprints.manipal.edu/style/images/fileicons/application_pdf.png) |
PDF
WorldSciencecong2008.pdf - Published Version Download (367kB) |
Abstract
As the number of networked computers grows,intrusion detection is an essential component in keeping networks secure. Various approaches for intrusion detection are currently being in use with each one has its own merits and demerits. This paper presents our work to test and improve the performance of a new class of decision tree c-fuzzy decision tree to detect intrusion.The work also includes identifying best candidate feature sub set to build the efficient c-fuzzy decision tree based Intrusion Detection System (IDS). We investigated the usefulness of c-fuzzy decision tree for developing IDS with a data partition based on horizontal fragmentation. Empirical results indicate the usefulness of our approach in developing the efficient IDS.
| Item Type: | Article |
|---|---|
| Additional Information: | © World Academy of Science, Engineering and Technology |
| Uncontrolled Keywords: | Data mining, Decision tree, Feature selection, Fuzzy c- means clustering, Intrusion detection. |
| Subjects: | Engineering > MIT Manipal > Computer Science and Engineering Engineering > MIT Manipal > MCA |
| Depositing User: | MIT Library |
| Date Deposited: | 04 Jul 2011 09:07 |
| Last Modified: | 04 Jul 2011 09:07 |
| URI: | http://eprints.manipal.edu/id/eprint/480 |
Actions (login required)
 |
View Item |
