CFP last date
22 April 2024
Call for Paper
May Edition
IJCA solicits high quality original research papers for the upcoming May edition of the journal. The last date of research paper submission is 22 April 2024

Submit your paper
Know more
The week's pick
Responsive image
Enhancing Privacy Preservation: Multi-Attribute Protection with P-Sensitive K-Anonymity

Twinkle Patel Kiran Amin
Random Articles
Reseach Article

Reduction of False Alarm Rate by using K-NN and Naive Bayes: A Review

by Navita Datta, Rajeev Kumar, Reeta Bhardwaj
journal cover thumbnail

International Journal of Computer Applications
Foundation of Computer Science (FCS), NY, USA
Volume 180 - Number 3
Year of Publication: 2017
Authors: Navita Datta, Rajeev Kumar, Reeta Bhardwaj
10.5120/ijca2017915985

Navita Datta, Rajeev Kumar, Reeta Bhardwaj . Reduction of False Alarm Rate by using K-NN and Naive Bayes: A Review. International Journal of Computer Applications. 180, 3 ( Dec 2017), 3-6. DOI=10.5120/ijca2017915985

@article{ 10.5120/ijca2017915985,
author = { Navita Datta, Rajeev Kumar, Reeta Bhardwaj },
title = { Reduction of False Alarm Rate by using K-NN and Naive Bayes: A Review },
journal = { International Journal of Computer Applications },
issue_date = { Dec 2017 },
volume = { 180 },
number = { 3 },
month = { Dec },
year = { 2017 },
issn = { 0975-8887 },
pages = { 3-6 },
numpages = {9},
url = { https://ijcaonline.org/archives/volume180/number3/28778-2017915985/ },
doi = { 10.5120/ijca2017915985 },
publisher = {Foundation of Computer Science (FCS), NY, USA},
address = {New York, USA}
}
%0 Journal Article
%1 2024-02-07T00:59:34.985991+05:30
%A Navita Datta
%A Rajeev Kumar
%A Reeta Bhardwaj
%T Reduction of False Alarm Rate by using K-NN and Naive Bayes: A Review
%J International Journal of Computer Applications
%@ 0975-8887
%V 180
%N 3
%P 3-6
%D 2017
%I Foundation of Computer Science (FCS), NY, USA
Abstract

Interruption 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.

References
  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) http://www.acm.org/sigs/sigkdd/kddcup/index.php?section=1999
  11. The KDD Archive. KDD99 cup dataset, 1999, http://kdd.ics.uci.edu/databases/kddcup99/kddcup99.html.
  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.
Index Terms

Computer Science
Information Sciences

Keywords

KDD NIDS DoS R2L U2R DR FPR.

Powered by PhDFocusTM