View Article

Article Details

File Missing!
JournalInternational Journal of Computer Applications
TitleReduction of False Alarm Rate by using K-NN and Naive Bayes: A Review
Index TermAutomated Systems
AbstractInterruption location is basic in orchestrate security. Most present framework interruption location structures (NIDSs) employ either misuse recognition or anomaly discovery. In any case, misuse recognition can't recognize darken interruptions, and anomaly location generally has high false positive rate. To overcome the imperatives of the two techniques, they intertwine both anomaly and misuse recognition into the NIDS. This paper presents a hybrid interruption recognition framework based on the combination of k-Means and two classifiers which are K-nearest neighbor and Naive Bayes. This paper includes picking features using an entropy based segment assurance computation that uses imperative properties and expels the irredundant qualities. The whole observation in this study is performed on KDD-99 Data set which is accepted at world level for surveying execution of various interruption recognition frameworks. The consequent stage is grouping stage using k-Means. The proposed framework can recognize all interruptions and categorize them into four segments: Denial of Service, User to Root, Remote to nearby and test. The main goal is to minimize the false ready rate of IDS.
KeywordsKDD, NIDS, DoS, R2L, U2R, DR, FPR.
No. of Pages4
Author NamesNavita Datta, Rajeev Kumar, Reeta Bhardwaj
  1. James P. Anderson, “Computer security threat monitoring and surveillance,” Technical Report 98-17, James P. Anderson Co., Fort Washington, Pennsylvania, USA, April 1980.
  2. D. E. Denning, “An intrusion detection model,” IEEE Transaction on Software Engineering, SE-13(2), 1987, pp. 222-232.
  3. Daniel Barbara, Julia Couto, Sushil Jajodia, Leonard Popyack and Ningning Wu, “ADAM: Detecting intrusion by data mining,” IEEE Workshop on Information Assurance and Security, West Point, New York, June 5-6, pp. 11-16, 2001.
  4. Debra Anderson, Thane Frivold, and Alfonso Valdes, “NIDES Next-generation Intrusion Detection Expert System (NIDES)”, A Summary, Computer Science Laboratory,SRI-CSL-95-07,May 1995
  5. Te-Shun Chou and Tsung-Nan Chou, “Hybrid Classified Systems for Intrusion Detection,” Seventh Annual Communications Networks and Services Research Conference, pp. 286-291, 2009.
  6. N.B. Amor, S. Benferhat, and Z. Elouedi, “Naïve Bayes vs.decision trees in intrusion detection systems,” Proc. of 2004 ACM Symposium on Applied Computing, 2004, pp. 420-424.
  7. Yihua Liao and V. Rao Vimuri, “Using K-nearest Neighbour Classifier for Intrusion Detection,” Department Of Computer Scinece, University Of California.
  8. T. S. Chou, K. K. Yen, and J. Luo, Network Intrusion Detection Design Using Feature Selection of Soft Computing Paradigms,” World Academic of Science, Engineering and Technology, 47, pp. 529-541, 2008.
  9. Z. Muda, W. Yassin, M.N. Sulaiman and N.I. Udzir, “A K-Means and Naive Bayes Learning Approach for Better Intrusion Detection,” Information Technology Journal, 10, pp. 648-655, 2011.
  10. MIT linconin labs, 1999 ACM Conference on Knowledge Discovery and Data Mining (KDD)
  11. The KDD Archive. KDD99 cup dataset, 1999,
  12. M. Tavlle, E. Bagheri, W. Lu, and A. A. Gorbani, “A detailed analysis of the KDD CUP 99 Data Set,” Proc. of IEEE Symposium 1st Int’l Conf. on Recent Advances in Information Technology | RAIT-2012 |Computational Intelligence for Security and Defense Applications (CISDA'09), pp. 1-6, 2009.
  13. Mukkamala S., Janoski G., and Sung A.H., “Intrusion detection using neural networks and support vector machines,” In Proc. of the IEEE International Joint Conference on Neural Networks, 2002, pp.1702-1707.
  14. J. Zhang and M. Zulkernine, “A Hybrid Network Intrusion Detection Technique Using Random Forests,” Proc. of IEEE First International Conference on Availability, Reliability and Security (ARES’06), p. 8, 2006.
  15. D. Md. Farid, N. Harbi, S. Ahmmed, Md. Z. Rahman, and C. M. Rahman, “Mining Network Data for Intrusion Detection through Naïve Bayesian with Clustering”, World Academy of science, Engineering and Technology, 66, pp. 341-345, 2010.

Publishing Information

Start Page No.3
Editor's Choice