IJRE – Volume 2 Issue 1 Paper 5


Author’s Name : Dr N Arumugam

Volume 02 Issue 01  Year 2015  ISSN No:  2349-252X  Page no: 16-19


Abstract – In recent days, due to the rapid expansion of Internet, computer systems are facing vast number of security threats. In spite of numerous detection and defense methodologies proposed for information assurance, it is still very difficult to protect computer systems. As a result, unwanted intrusions take place when the actual software systems are running. Recently soft computing based intrusion Detection systems (IDs) have been subjected to extensive researches because they can detect both misuse and anomaly detection. In this paper the method of learning the Intrusion Detection, rules based on genetic algorithms was presented. The genetic algorithm is employed to derive a set of classification rules from network audit data, and the support-confidence framework is utilized as fitness function to judge the quality of each rule. The generated rules are then used to detect or classify network intrusions in a real-time environment. The proposed representation of rules and the effective fitness function is easier to implement while providing the flexibility to either generally detect network intrusions or precisely classify the types of attacks. Experiments results shows, the characters of an attack such as SMURF and SNMP get attack were summarized through the Modified and corrected KDD 99 data set and the effectiveness and robustness of the approach are proved.

Keywords – Intrusion Detection, Genetic Algorithm, KDD Cup Data Set


  1. A. Adetoye, A. Choi, M. Md. Arshad, and O. Soretire , “Network Intrusion Detection & Response System”, Group Report, September 2003, http://www.cs.ucl.ac.uk/teaching/dcnds/group-reports /2003/2003-hailes-b.pdf (accessed in January 2005).
  2. B. Mukherjee, L. T. Heberlein, and K. N. Levitt, “Network intrusion detection”, IEEE Network, 8(3): 26-41, May/June 1994.
  3. M. Moradi and M. Zulkernine, “A Neural Network Based System for Intrusion Detection and Classification of Attacks”, Proceedings of the 2004 IEEE International Conference on Advances in Intelligent Systems – Theory and Applications, Luxembourg, November 2004.
  4. J. Gomez and D. Dasgupta, “Evolving Fuzzy Classifiers for Intrusion Detection”, Proceedings of the IEEE, 2002.
  5. G. Helmer, J. Wong, V. Honavar and L. Miller, “Automated discovery of concise predictive rules for intrusion detection”, The Journal of Systems and Software, issue 60, pp. 165-175, 2002.
  6. Intrusion Detection Evaluation Program (http://www.ll.mit.edu/mission/communications/ist/corpora/ideval/index.html).
  7. Lippmann, Joshua W. Haines, David J. Fried, Jonathan Korba, “The 1999 DARPA off-line intrusion detection evaluation” The International Journal of Computer and Telecommunications Networking, Volume 34, Issue 4 (October 2000) Page(s): 579 –595.
  8. UCIKDD Archive, http://kdd.ics.uci.edu/ databases /kddcup99/ kddcup99.html) September 2009.
  9. Pohlheim H, “Genetic and Evolutionary Algorithms: Principles Methods and Algorithms”, http://www.gearbx.com/docu/index.html, January 2005.
  10. Sapna S. Kaushik, Dr. Prof. P.R.Deshmukh,” Detection of Attacks in an Intrusion Detection System”, International Journal of Computer Science and Information Technologies, Vol. 2 (3), 2011, 982-986.