Rough set-hypergraph-based feature selection approach for intrusion detection systems

Defence Science Journal

Volumn 66, Issue 6, 2016, Pages 612-617

  Raman, M R Gauthama ^a   Kannan, K ^a   Pal, S K ^b   Sriram, V S Shankar ^a  

^a SASTRA UNIVERSITY   (India)

^b Metcalfe House   (India)

Author keywords

Feature selection; Hypergraph; Intrusion detection systems; Rough set theory; Helly property

Indexed keywords

BENCHMARKING; CLASSIFICATION (OF INFORMATION); COMPUTER CRIME; CYBERSECURITY; FEATURE SELECTION; NETWORK ARCHITECTURE; NETWORK SECURITY; ROUGH SET THEORY;

FEATURE SELECTION ALGORITHM; FEATURES SELECTION; HELLY PROPERTIES; HYPER GRAPH; IN NETWORKS; INTERNET USERS; INTRUSION DETECTION SYSTEMS; NETWORK-BASED SERVICES; SECURITY THREATS; Κ -HELLY PROPERTY;

INTRUSION DETECTION;

EID: 84994275509     PISSN: 0011748X     EISSN: 0976464X     Source Type: Journal    
DOI: 10.14429/dsj.66.10802     Document Type: Article

References (18)

1. Scarfone, K. & Mell, P. Guide to intrusion detection and prevention systems (idps). NIST special publication, 2007, 800, 94.
2. Stouffer, K.; Joe, F. & Karen, S. Guide to industrial control systems (ICS) security. NIST special publication, 2011, 800(82), 16-16. doi: 10.6028/NIST.SP.800-82
3. Anderson, J.P. Computer security threat monitoring and surveillance. Vol. 17 Technical report, James P. Anderson Company, Fort Washington, Pennsylvania. 1980.
4. Aydin, M.A.; Zaim, A.H., & Ceylan, K.G. A hybrid intrusion detection system design for computer network security. Comp. Elect. Eng., 2009, 35(3), 517-526. doi: 10.1016/j.compeleceng.2008.12.005
5. Gheyas, I.A. & Smith, L.S. Feature subset selection in large dimensionality domains. Pattern Recognition, 2010, 43(1), 5-13. doi: 10.1016/j.patcog.2009.06.009
6. Anaraki, J.R. & Eftekhari, M. Improving fuzzy-rough quick reduct for feature selection. In 19th Iranian Conference on Electrical Engineering Tehran, Iran, 2011.
7. Bretto, A.; Cherifi, H. & Ubéda, S. An efficient algorithm for K-Helly property recognition in a linear hypergraph. In 8th International Workshop on Combinatorial Image Analysis. Electronic Notes in Theoretical Computer Science, 2001. doi:10.1016/S1571-0661(04)80985-X
8. KDD Cup 1999 data set. http://kdd.ics.uci.edu/databases/kddcup99/kddcup99.html (Accessed on 27 January 2016)
9. Witten, I.H. & Frank, E. Data mining: Practical machine learning tools and techniques. Morgan Kaufmann. 2005. 365.
10. Alelyani, S. On feature selection stability: A data perspective. Arizona State University, Tempe, Arizona, 2013. (PhD Thesis).
11. Wroblewski, J. Finding minimal reducts using genetic algorithms. In Proceedings of the 2nd Annual Joint Conference on Information Science, 1995.
12. Wang, X.; Yang, J; Teng, X.; Xia, W. & Jensen, R. Feature selection based on rough sets and particle swarm optimisation. Pattern Recognition Letters, 2007, 28(4), 459-471. doi: 10.1016/j.patrec.2006.09.003
13. Wang, G.Y.; Yu, H. & Yang, D. C. Decision table reduction based on conditional information entropy. Chinese J. Comp., (Chinese), 2002, 25(7), 759-766.
14. Hu, X.; Cercone, N. & Han, J. An attribute-oriented rough set approach for knowledge discovery in databases. In Rough sets, Fuzzy sets and knowledge discovery. Springer, London,1994
15. Hu, K.; Lu, Y. & Shi, C. Feature ranking in rough sets. Artificial Intelligence Comm., 2003, 16(1), 41-50. doi : 10.1.1.14.9476
16. Jiang, F.; Sui, Y. & Zhou, L.A. Relative decision entropybased feature selection approach. Pattern Recognition, 2015, 48(7), 2151-2163. doi : 10.1016/j.patcog.2015.01.023
17. Nivethitha, S.; Kannan K. & Shankar Sriram, V. S. A computational model for ranking cloud service providers using hypergraph based techniques. Future Generation Comput. Sys., 2017, 68,14-30. doi: 10.1016/j.future.2016.08.014
18. Nivethitha, S.; Gauthama Raman, M.R.; Kannan, K. & Shankar Sriram, V. S. Hypergraph based feature selection technique for medical diagnosis. J. Medical Sys., 2016, 40(11), pp. 239. doi: 10.1007/s10916-016-0600-8