Improving Intrusion Detection using Genetic Linear Discriminant Analysis

Yıl 2015, Cilt: 3 Sayı: 1, 34 - 39, 13.01.2015

Azween Abdullah Long Zheng Cai

https://doi.org/10.18201/ijisae.37262

Cited By: 1

Öz

The objective of this research is to propose an efficient soft computing approach with high detection rates and low false alarms while maintaining low cost and shorter detection time for intrusion detection. Our results were promising as they showed the new proposed system, hybrid feature selection approach of Linear Discriminant Analysis and Genetic Algorithm (GA) called Genetic Linear Discriminant Analysis (GLDA) and Support Vector Machines (SVM) Kernels as classifiers with different combinations of NSL-KDD data sets is an improved and effective solution for intrusion detection system (IDS).

Anahtar Kelimeler

IDS; Features selection; Features transformation; NSL-KDD; GLDA; SVM Kernels

Kaynakça

• A. Martinez and A. Kak (2001). "PCA versus LDA", IEEE Transactions on Pattern Analysis and Machine Intelligence,” vol. 23, no. 2, pp. 228-233,.
• China Papers Online (2011). “Study on Application of Hybrid Soft-Computing Technique to Intrusion Detection”.
• Adel Nadjaran Toosi and Mohsen Kahani (2007) “A new approach to intrusion detection based on an evolutionary soft computing model using neuro-fuzzy classifiers,” Department of Computer, Ferdowsi University of Mashhad, Iran.
• Kresimir Delac, Mislav Grgic and Sonja Grgic (2006). “Independent Comparative Study of PCA, ICA, and LDA on the FERET Data Set,” University of Zagreb, FER, Unska 3/XII, Croatia.
• J. McHugh (2000) “Testing intrusion detection systems: a critique of the 1998 and 1999 DARPA intrusion detection,” ACM Transactions on Information and System Security.
• Shailendra Singh, Sanjay Silakari and Ravindra Patel (2011). An Efficient Feature Reduction Technique for Intrusion Detection System, IPCSIT, Vol. 3.
• Ahmad I, Abdullah AB, and Alghamdi (2011). “Intrusion detection using feature subset selction based on MLP,” Scientific Research and Essays, Vol 6(34).
• S. M. Aqil, M. Sadiq Ali Khan and Jawed Naeem (2010). Efficient Probabilistic Classification Methods for NIDS, IJCSIS, Vol. 8, No. 8, November.
• P. Belhumeur, J. Hespanha, and D. Kriegman (1996). Eigenfaces vs. fisherfaces: Recognition using class specific linear projection, Proc Fourth Eur Conf Computer Vision, Vol. 1, 1418, pp. 45–58.
• M. Turk and A. Pentland (1991). “Eigenfaces for recognition,” J Cogn Neurosci 3, 71–86.
• K. Baek, B. Draper, J.R. Beveridge and K. She (2002). "PCA vs. ICA: A Comparison on the FERET Data Set,” Proc. of the Fourth International Conference on Computer Vision, Pattern Recognition and Image Processing, Durham, NC, USA, 8-14, pp. 824-827.
• Chittur A. (2006). “Model Generation for an Intrusion Detection System Using Genetic Algorithms,” High school Honors Thesis.
• Acohido B. (2009). "Hackers breach heartland payment credit card system", 11 March.
• Abraham A. and Jain R. (2008). "Soft computing models for network intrusion detection systems, 15 May.
• Sandhya P., Ajith A., Crina G. and Thomas J. (2005). "Modeling intrusion detection system using hybrid intelligent systems. Journal of Network and Computer Applications,".
• Ilgun K, Kemmerer R.A. and Porras P.A. (1995). "State transition analysis: a rule-based intrusion detection approach," IEEE Trans Software Eng 21(3):181–199.
• Zadeh LA. (1994). "History; bisc during 90’s,".
• Zadeh L.A. (1998). "Roles of soft computing and fuzzy logic in the conception," design and deployment of information/intelligent systems. In: Kaynak O, Zadeh LA, Turksen B, Rudas IJ (eds) Computational intelligence: soft computing and fuzzy-neuro integration with applications, vol 162. Springer, New York.
• Rupali D. (2010). "Feature Reduction for Intrusion Detection Using Linear Discriminant Analysis", (IJCSE) International Journal on Computer Science and Engineering Vol. 02, No. 04, 1072-1078.
• Liao Y. and Vemuri V. R. (2002). "Use of k-nearest neighbor classifier for intrusion detection," Computer Security, vol. 21, no. 5, pp. 439-448.
• Selvakani Kandeeban S. and Rengan S. R. (2010). "Integrated Intrusion Detection System Using Soft Computing", I. J. Network Security 10(2): 87-92. 2008.
• M.Sadiq Ali Khan (2012). "Application of Statistical Process Control Methods for IDS," International Journal of Computer Science Issues, Vol. 9, Issue 6, No 1, November.
• Chittur A. (2006). “Model Generation for an Intrusion Detection System Using Genetic Algorithms,” High school Honors Thesis, accessed in.