An Intelligent Intrusion Detection System Using Average Manhattan Distance-based Decision Tree

Conference paper
Part of the Advances in Intelligent Systems and Computing book series (AISC, volume 324)


Recently, security is an important challenge in Internet-based communication. In such a scenario, intrusion detection systems help to secure the information through the identification of normal and abnormal behaviors. In order to model these behaviors accurately and to improve the performance of the intrusion detection system, intelligent decision tree algorithm based on average Manhattan distance algorithm (IDTAMD) is proposed in this paper. In this proposed new classification algorithm for effective decision making in the network data set. Moreover, an attribute selection algorithm called modified heuristic greedy algorithm [1] is used to select itemsets from redundant data. The experimental results obtained in this work show high detection rates and reduce the false alarm rate. This system has been tested using the tenfold cross-validations on the KDD’99 Cup data set. The results have been tested with tenfold cross-validation.


Intrusion detection system (IDS) Attribute selection Modified heuristic greedy Itemsets 


  1. 1.
    J. Onpans, S. Rasmequan, B. Jantarakongkul, K. Chinnasarn, A. Rodtook, Intrusion feature selection using modified heuristic greedy algorithm of itemset. 13th International Symposium on Communications and Information Technologies (ISCIT) (2013), pp. 627–632Google Scholar
  2. 2.
    D.E. Denning, An intrusion detection model. IEEE Trans. Softw. Eng. 51(8), 12–26 (2003)Google Scholar
  3. 3.
    F. Zhu, N. Ye, W. Yu, S. Xu, G. Li, Boundary detection and sample reduction for one-class support vector machines. Neurocomputing 123, 166–173 (2014)CrossRefGoogle Scholar
  4. 4.
    S.A. Mulay, P.R. Devale, G.V. Garje, Intrusion detection system using support vector machine and decision tree. Int. J. Comput. Appl. 3(3), 0975–8887 (2010)Google Scholar
  5. 5.
    G. Madzarov, D. Gjorgjevikj, I. Chorbev, A multiclass SVM classifier utilizing binary decision tree. Informatica 33, 233–241 (2009)MathSciNetGoogle Scholar
  6. 6.
    W.K. Lee, S.J. Stolfo, A data mining framework for building intrusion detection models, in Proceedings of the IEEE Symposium on Security and Privacy (1999), pp. 120–132Google Scholar
  7. 7.
    S. Ganapathy, K. Kulothungan, S. Muthurajkumar, M. Vijayalakshmi, P. Yogesh, A. Kannan, Intelligent feature selection and classification techniques for intrusion detection in networks: a survey. J. Wirel. Commun. Networking 271, 1–16 (2013)Google Scholar
  8. 8.
    C. Yang, H. Ge, G. Yao, L. Ma, Quick complete attribute reduction algorithm. 2009 6th International Conference on Fuzzy Systems and Knowledge Discovery, IEEE (2010), pp. 576–580Google Scholar
  9. 9.
    G. Geng, N. Li, S. Gong, Feature selection method for network intrusion based on fast attribute reduction of fuzzy rough set. 2012 International Conference on Industrial Control and Electronics Engineering (2012), pp. 530–534Google Scholar
  10. 10.
    H. Om, A. Kundu, A hybrid system for reducing the false alarm rate of anomaly intrusion detection system. 1st International Conference on Recent Advances in Information Technology (2012), pp. 1–6Google Scholar
  11. 11.
    D.M. Farid, J. Dormant, N. Harbi, H.H. Nguyen, M.Z. Rahman, Adaptive network intrusion detection learning: attribute selection and classification. International Conference on Computers Systems Engineering (2009)Google Scholar
  12. 12.
    F. Zhang, D. Wang, An effective feature selection approach for network intrusion detection. 8th International IEEE Conference on Networking, Architecture and Storage (2013), pp. 307–311Google Scholar
  13. 13.
    C. Jin, L. De-lin, M. Fen-xiang, An improved ID3 decision tree algorithm, in Proceedings of 4th International Conference on Computer Science and Education (2009), pp. 33–38Google Scholar

Copyright information

© Springer India 2015

Authors and Affiliations

  1. 1.St. Peter’s UniversityChennaiIndia
  2. 2.Panimalar Institute of TechnologyChennaiIndia
  3. 3.SMK Fomra Institute of TechnologyChennaiIndia

Personalised recommendations