Improving Accuracy for Intrusion Detection through Layered Approach Using Support Vector Machine with Feature Reduction-Reference-Cited by-同舟云学术

Improving Accuracy for Intrusion Detection through Layered Approach Using Support Vector Machine with Feature Reduction

Published:2016 Issue: Volume: Page:
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Container-title:Proceedings of the ACM Symposium on Women in Research 2016 - WIR '16
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Author:

Nema Aditi¹,Tiwari Basant¹,Tiwari Vivek²

Affiliation:

1. Bansal Institute of Research and Technology, Bhopal

2. Sagar Institute of Science and Technology, Bhopal

Publisher

ACM Press

Reference18 articles.

1. Kapil Kumar Gupta, Baikunth Nath "Layered Approach Using Conditional Random Fields for Intrusion Detection". IEEE transaction on dependable and secure computing, vol. 7, no. 1 January-march 2010.

2. Overview of Attack Trends, http://www.cert.org/archive/pdf / attack_trends.pdf, 2002

3. NSL KDD dataset Available on:http://http://nsl.cs.unb.ca/NSL-KDD/

4. LI Hui, GUAN Xiao-hong, ZAN Xin. "Network intrusion detection based onsupport vector machine". Journal of computer research and development. 6 (2003), pp. 800--807.

5. Mahbod Tavallaee,Ebrahim Bagheri,Wei Lu, and Ali A.Ghorbani, "A Detailed Analysis of the KDDCUP 99 Data Set," Proceedings of the 2009 IEEE Symposium on Computational Intelligence in Security and Defense Application(CISDA 2009),IEEE 2009.

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