National Journal of System and Information Technology

Open Access Open Access  Restricted Access Subscription Access
Open Access Open Access Open Access  Restricted Access Restricted Access Subscription Access

Clustering Based Outlier Detection Method for Network Based Intrusion Detection


     

   Subscribe/Renew Journal


The discovery of objects with exceptional behavior is an outstanding challenge from a knowledge discovery standpoint and has received considerable attention in many applications such as network attacks, fraud detection. This paper proposes a simple clustering based algorithm to detect outlying objects. The main problem for network intrusion detection system is the ability to exploit ambiguities in the traffic stream. Network-Based Intrusion Detection monitors network traffic for particular network segment and analyzes the network and application protocol activity to identify suspicious activity. There are several recently developed outlier detection schemes to detect attacks in a network. In this paper, the proposed algorithm is applied to network intrusion detection system to detect ambiguities or violations in the network traffic stream.

Keywords

Outlier Detection, Clustering, Network Based Intrusion Detection
Subscription Login to verify subscription
User
Notifications
Font Size


  • [Han, J. and M. Kamber, 2006, “Data Mining: Concepts and Techniques”, Morgan Kaufmann, 2nd Ed.

  • Moh’d Belal Al- Zoubi, “An Effective Clustering-Based Approach for Outlier Detection”, European Journal of Scientific Research, ISSN 1450-216X Vol.28 No.2 (2009), pp.310-316

  • Anna Koufakou, Jimmy SEcretan, John Reeder, Kelvin Cardona, and Michael Georgiopoulos, “Fast Parallel Outlier Detection for Categorical Datasets using MapReduce”, 2008 International Joint Conference on Neural Networks.

  • Karen Scarfone, Peter Mell, “Guide to Intrusion Detection and Prevention Systems (IDPS)”, Recommendations of the National Institute of Standards and Technology, February2007.

  • Breunig, M. M., Kriegel, H.P., Ng, R. T., and Sander, J., “LOF: Identifying density-based Local Outliers”, Proc. Of the ACM SIGMOD International Conference on Management of Data, 2007

  • Knorr, E., Ng, R, and Tucakov, V., “Distance-based Outliers: Algorithms and Applications”, Very Large Databases VLDB Journal, 2000

  • Barnett, V., Lewis, T. “Outliers in Statistical Data”, John Wiley, 1994. Opim Salim Sitompul and Shahrul Azman Noah, A Transformation-oriented Methodology to Knowledge-based Conceptual Data Warehouse Design ,Journal of Computer Science 2 (5): 460-465, 2006.


Abstract Views: 261

PDF Views: 4




  • Clustering Based Outlier Detection Method for Network Based Intrusion Detection

Abstract Views: 261  |  PDF Views: 4

Authors

Abstract


The discovery of objects with exceptional behavior is an outstanding challenge from a knowledge discovery standpoint and has received considerable attention in many applications such as network attacks, fraud detection. This paper proposes a simple clustering based algorithm to detect outlying objects. The main problem for network intrusion detection system is the ability to exploit ambiguities in the traffic stream. Network-Based Intrusion Detection monitors network traffic for particular network segment and analyzes the network and application protocol activity to identify suspicious activity. There are several recently developed outlier detection schemes to detect attacks in a network. In this paper, the proposed algorithm is applied to network intrusion detection system to detect ambiguities or violations in the network traffic stream.

Keywords


Outlier Detection, Clustering, Network Based Intrusion Detection

References