Genetic Algorithm to Solve the Problem of Small Disjunct In the Decision Tree Based Intrusion Detection System

Genetic Algorithm to Solve the Problem of Small Disjunct In the Decision Tree Based Intrusion Detection System

Автор: Chandrashekhar Azad, Vijay Kumar Jha

Журнал: International Journal of Computer Network and Information Security(IJCNIS) @ijcnis

Статья в выпуске: 8 vol.7, 2015 года.

Бесплатный доступ

Intrusion detection system is the most important part of the network security system because the volume of unauthorized access to the network resources and services increase day by day. In this paper a genetic algorithm based intrusion detection system is proposed to solve the problem of the small disjunct in the decision tree. In this paper genetic algorithm is used to improve the coverage of those rules which are cope with the problem of the small disjunct. The proposed system consists of two modules rule generation phase, and the second module is rule optimization module. We tested the effectiveness of the system with the help of the KDD CUP dataset and the result is compared with the REP Tree, Random Tree, Random Forest, Na?ve Bayes, and the DTLW IDS (decision tree based light weight intrusion detection system). The result shows that the proposed system provide the best result in comparison to the above mentioned classifiers.

Еще

IDS, Anomaly Detection, Misuse Detection, Genetic Algorithm, Decision Tree, C4.5

Короткий адрес: https://sciup.org/15011448

IDR: 15011448

Список литературы Genetic Algorithm to Solve the Problem of Small Disjunct In the Decision Tree Based Intrusion Detection System

  • Wu, Shelly Xiaonan, and Wolfgang Banzhaf. "The use of computational intelligence in intrusion detection systems: A review." Applied Soft Computing10.1 (2010): 1-35.
  • http://en.wikipedia.org/wiki/Internet_traffic
  • Tsai, Chih-Fong, et al. "Intrusion detection by machine learning: A review."Expert Systems with Applications 36.10 (2009): 11994-12000.
  • Liao, Hung-Jen, et al. "Intrusion detection system: A comprehensive review."Journal of Network and Computer Applications 36.1 (2013): 16-24.
  • Liao, Shu-Hsien, Pei-Hui Chu, and Pei-Yuan Hsiao. "Data mining techniques and applications–A decade review from 2000 to 2011." Expert Systems with Applications 39.12 (2012): 11303-11311.
  • Julisch, Klaus. "Data mining for intrusion detection." Applications of data mining in computer security. Springer US, 2002. 33-62.
  • Fan, Wei, and Albert Bifet. "Mining big data: current status, and forecast to the future." ACM SIGKDD Explorations Newsletter 14.2 (2013): 1-5.
  • D.R. Carvalho, A.A. Freitas,A genetic algorithm-based solution for the problem of small disjuncts, Principles of Data Mining and Knowledge Discovery, Proceedings of 4th European Conference, PKDD-2000. Lyon, France, Lecture Notes in Artificial Intelligence, vol. 1910, Springer-Verlag (2000), pp. 345–352.
  • Carvalho, Deborah R., and Alex A. Freitas. "A genetic-algorithm for discovering small-disjunct rules in data mining." Applied Soft Computing 2.2 (2002): 75-88.
  • Carvalho, Deborah R., and Alex A. Freitas. "A hybrid decision tree/genetic algorithm method for data mining." Information Sciences 163.1 (2004): 13-35.
  • Koshal, Jashan, and Monark Bag. "Cascading of C4. 5 decision tree and support vector machine for rule based intrusion detection system." International Journal of Computer Network and Information Security (IJCNIS) 4.8 (2012): 8.
  • Thaseen, Sumaiya, and Ch Aswani Kumar. "An analysis of supervised tree based classifiers for intrusion detection system." Pattern Recognition, Informatics and Mobile Engineering (PRIME), 2013 International Conference on. IEEE, 2013.
  • Jiang, Feng, Yuefei Sui, and Cungen Cao. "An incremental decision tree algorithm based on rough sets and its application in intrusion detection." Artificial Intelligence Review 40.4 (2013): 517-530.
  • Panda, Mrutyunjaya, Ajith Abraham, and Manas Ranjan Patra. "A hybrid intelligent approach for network intrusion detection." Procedia Engineering 30 (2012): 1-9.
  • Selvi, R., S. Saravan Kumar, and A. Suresh. "An Intelligent Intrusion Detection System Using Average Manhattan Distance-based Decision Tree." Artificial Intelligence and Evolutionary Algorithms in Engineering Systems. Springer India, 2015. 205-212.
  • Senthilnayaki, B., K. Venkatalakshmi, and A. Kannan. "An intelligent intrusion detection system using genetic based feature selection and Modified J48 decision tree classifier." Advanced Computing (ICoAC), 2013 Fifth International Conference on. IEEE, 2013.
  • Muniyandi, Amuthan Prabakar, R. Rajeswari, and R. Rajaram. "Network anomaly detection by cascading k-Means clustering and C4. 5 decision tree algorithm." Procedia Engineering 30 (2012): 174-182.
  • L Prema, Rajeswari., and Arputharaj Kannan. "An active rule approach for network intrusion detection with enhanced C4. 5 algorithm." Int'l J. of Communications, Network and System Sciences 2008 (2008).
  • Mulay, Snehal A., P. R. Devale, and G. V. Garje. "Intrusion detection system using support vector machine and decision tree." International Journal of Computer Applications 3.3 (2010): 40-43.
  • Sivatha Sindhu, Siva S., S. Geetha, and A. Kannan. "Decision tree based light weight intrusion detection using a wrapper approach." Expert Systems with applications 39.1 (2012): 129-141.
  • D.R. Carvalho, A.A. Freitas, A hybrid decision tree/genetic algorithm for coping with the problem of small disjuncts in Data Mining, in: Proceedings of 2000 Genetic and Evolutionary Computation Conference (Gecco-2000), July 2000, Las Vegas, NV, USA, pp. 1061–1068.
  • D.R. Carvalho, A.A. Freitas,A genetic algorithm with sequential niching for discovering small-disjunct rules Proceedings of Genetic and Evolutionary Computation Conference, GECCO-2002, Morgan Kaufmann (2002), pp. 1035–1042.
  • Fernández, Alberto, Salvador García, and Francisco Herrera. "Addressing the classification with imbalanced data: open problems and new challenges on class distribution." Hybrid Artificial Intelligent Systems. Springer Berlin Heidelberg, 2011. 1-10.
  • Weiss, Gary M., and Haym Hirsh. "The problem with noise and small disjuncts."ICML. 1998.
  • Quinlan, John Ross. C4. 5: programs for machine learning. Vol. 1. Morgan kaufmann, 1993.
  • Freund, Y., Mason, L.: The alternating decision tree learning algorithm. In: Proceeding of the Sixteenth International Conference on Machine Learning, Bled, Slovenia, 124-133, 1999.
  • Jerome Friedman, Trevor Hastie, Robert Tibshirani (2000). Additive logistic regression: A statistical view of boosting. Annals of statistics. 28(2):337-407.
  • Leo Breiman (2001). Random Forests. Machine Learning. 45(1):5-32.
  • Ron Kohavi: Scaling Up the Accuracy of Naive-Bayes Classifiers: A Decision-Tree Hybrid. In: Second International Conference on Knoledge Discovery and Data Mining, 202-207, 1996.
  • Freitas, Alex A. "Evolutionary algorithms for data mining." Data Mining and Knowledge Discovery Handbook. Springer US, 2005. 435-467.
  • Alcalá-Fdez, Jesús, et al. "KEEL: a software tool to assess evolutionary algorithms for data mining problems." Soft Computing 13.3 (2009): 307-318.
  • Davis, Lawrence, ed. Handbook of genetic algorithms. Vol. 115. New York: Van Nostrand Reinhold, 1991.
  • Abebe Tesfahun, D. Lalitha Bhaskari,"Effective Hybrid Intrusion Detection System: A Layered Approach", IJCNIS, vol.7, no.3, pp.35-41, 2015.DOI: 10.5815/ijcnis.2015.03.05.
  • Bilal Maqbool Beigh,"A New Classification Scheme for Intrusion Detection Systems", IJCNIS, vol.6, no.8, pp.56-70, 2014.
  • Amrit Pal Singh, Manik Deep Singh,"Analysis of Host-Based and Network-Based Intrusion Detection System", IJCNIS, vol.6, no.8, pp.41-47, 2014.
  • Chandrashekhar Azad, Vijay Kumar Jha,"Data Mining in Intrusion Detection: A Comparative Study of Methods, Types and Data Sets", IJITCS, vol.5, no.8, pp.75-90, 2013.
  • Jiawei, H., & Kamber, M. (2001). Data mining: concepts and techniques. San Francisco, CA, itd: Morgan Kaufmann, 5.
Еще
Статья научная
QR-код для установки приложения SciUp Наведите камеру и скачайте бесплатное приложение SciUp