Effect of Mutation and Crossover Probabilities on Genetic Algorithm and Signature Based Intrusion Detection System

  • Authors

    • Mr. Prakash N Kalavadekar
    • Dr. Shirish S. Sane
    2018-11-27
    https://doi.org/10.14419/ijet.v7i4.19.28277
  • Intrusion Detection, Security, Signature, Features, J48.

  • Conventional methods of intrusion prevention like firewalls, cryptography techniques or access management schemes, have not provided complete protection to computer systems and networks from refined malwares and attacks. Intrusion Detection Systems (IDS) are giving the right solution to the current issues and became an important part of any security management system to detect these threats and will not generate widespread harm. The basic goal of IDS is to detect attacks and their nature that may harm the computer system. Several different approaches for intrusion detection have been reported in the literature. The signature based concept using genetic algorithm as features selection and, J48 as classifier to detect attack is proposed in this paper. The system was evaluated on KDD Cup 99, NSL-KDD and Kyoto 2006+ datasets.

     

  • References

    1. [1] Min Cai, Kai Hwang and Min Qin “Hybrid intrusion detection with weighted signature generation over anomalous internet episodesâ€, IEEE Transactions on Dependable And Secure Computing, Vol.4 No.1, Jan-March 2007.

      [2] Mohammed A. Ambusaidi, Priyadarshi Nanda “Building an intrusion detection system using a filter-based feature selection algorithmâ€, IEEE Transactions on computers, November 2014.

      [3] Gisung Kim, Seungmin Lee, Sehun Kim “A novel hybrid intrusion detection method integrating anomaly detection with misuse detectionâ€, Expert Systems with Applications, Elsevier Ltd, 2014.

      [4] S. Jajodia L., Popyack D. Barbara, J. Couto and N. Wuy. Adam, “Detecting Intrusions by data mining “, Technical report, Workshop Information Assurance and Security, USA, 2001.

      [5] Bharathi M. Sahana Devi K. J.,â€Hybrid intrusion detection with weighted signature generationâ€,Technical report, Dept of CSE, Chickballapur,2011.

      [6] Siva S. SivathaSindhu, S. Geetha, A. Kannan" Decision tree based light weight intrusion detection using a wrapper approach", Expert Systems with Applications 39 129-141,2012.

      [7] Kapil Kumar Gupta, BaikunthNath, RamamohanaraoKotagiri," Layered Approach Using Conditional Random Fields for Intrusion Detection" IEEE Transactions on Dependable and Secure Computing, Vol.4 No.1, Jan-March 2010

      [8] Dr. SaurabhMukherjeea, Neelam Sharma," Intrusion Detection using Naive Bayes Classifier with Feature Reduction", Procedia Technology, 119 – 128, 2012.

      [9] Bertrand Portier, Froment-Curtil," Data Mining Techniques for Intrusion Detection", The University of Texas at Austin, Dr. Ghosh - EE380L Data Mining Term Paper, Spring 2000.

      [10] L PremaRajeswari, KannanArputharaj," An Active Rule Approach for Network Intrusion Detection with Enhanced C4.5 Algorithm", I. J. Communications, Network and System Sciences, 4, 284-359 Published Online, November 2008.

      [11] Nahla Ben Amor, Salem Benferhat," Naive Bayes vs Decision Trees in Intrusion Detection Systems" , SAC’04, March 14-17, Nicosia, Cyprus,2004.

      [12] Ahmed H. Fares and Mohamed I. Sharawy," Intrusion Detection: Supervised Machine Learning", Journal of Computing Science and Engineering,Vol. 5, No. 4, pp. 305-313, December 2011.

      [13] AdetunmbiA.Olusola., AdeolaS.Oladele and Daramola O.Abosede,"Analysis of KDD 99 Intrusion Detection Dataset for Selection of Relevance Features", Proceedings of the World Congress on Engineering and Computer Science 2010,Vol I WCECS 2010, San Francisco, USA, October 20-22 2010.

      [14] MahbodTavallaee, EbrahimBagheri, Wei Lu and Ali A., Ghorbani,"A Detailed Analysis of the KDD CUP 99 Data Setâ€, Proceedings of the 2009 IEEE Symposium on Computational Intelligence in Security and Defense Applications (CISDA 2009).

      [15] TaisirEldos, Mohammad KhubebSiddiqui and AwsKanan,“The KDD99 Dataset: Statistical Analysis for Feature Selection",Journal of Data Mining and Knowledge Discovery ISSN: 2229-6662 & ISSN: 2229-6670, Volume 3, Issue 3, pp.-88-90, 2012.

      [16] YisehacYohannes,JohnHoddinott,"Classification and Regression Trees: An Introductin",International Food Policy Research Institute,2033 K Street, N.W.Washington, D.C., U.S.A, 2006

      [17] PeymanKabiri and Ali A. Ghorbani, “Research on Intrusion Detection and Response:A Surveyâ€, International Journal of Network Security, Vol.1, No.2, PP.84–102, Sep. 2005.

      [18] Wenke Lee and Salvatore J. Stolfo, “Data Mining Approaches for Intrusion Detectionâ€, 7th USENIX Security Symposium, 1998.

      [19] Ismail Butun, Salvatore D. Morgera, and Ravi Sankar, “A Survey of Intrusion Detection Systems inWireless Sensor Networksâ€, IEEE Communications Surveys & Tutorials, 2013.

      [20] WenyingFeng, Quinglei, Gongzhu Hu, Jimmy Xiangi Huang, “Mining Network data for intrusion detection through combining SVMs with ant colony networksâ€, Future Generation Computer Systems, Elsevier,2013.

      [21] Kapil Kumar Gupta, BaikunthNath, Senior Member, IEEE, and Ramamohanarao Kotagiri, Member, IEEE, “Layered Approach Using Conditional Random Fields for Intrusion Detectionâ€, IEEE Transactions on Dependable and Secure Computing, Vol. 7, No. 1, January-March 2010.

      [22] Prakash Kalavadekar, Dr.Shirish Sane “Effective Intrusion Detection Systems using Hybrid Approachâ€International Journal of Exploring EmergingTrends in Engineering, Voume 3 Issue 2 Mar-Apr-2016

      [23] Prakash Kalavadekar, Dr.Shirish Sane “Effective Intrusion Detection Systems using Genetic Algorithmâ€,International Journal on Emerging Trends in Technology, Voume 4, Special Issue July-2017, pp.8315-8319.

  • Downloads

  • How to Cite

    Prakash N Kalavadekar, M., & Shirish S. Sane, D. (2018). Effect of Mutation and Crossover Probabilities on Genetic Algorithm and Signature Based Intrusion Detection System. International Journal of Engineering & Technology, 7(4.19), 1011-1015. https://doi.org/10.14419/ijet.v7i4.19.28277