Improved K-Mean Algorithm for Detection Rate in Intrusion Detection System with AI

Authors(3) :-Susheel Kumar Tiwari, Dr. Chandikaditya Kumawat, Dr. Manish Shrivastava

Intrusion location framework is a need of the present data security area. It assumes an imperative job in discovery of odd activity in a system and cautions the system chairmen to oversee such movement. The work displayed in this proposal is an endeavor to identify such movement peculiarities in the systems by creating and dissecting the activity stream information This IDS exhibited in this postulation actualizes the k-implies approach of information digging for interruption discovery and the exception recognition approach utilizing neighborhood exception factor to distinguish the irregularities present in the rush hour gridlock stream. The k-implies approach utilizes bunching systems to amass the movement stream information into typical and odd groups. The calculation is a cycle system and necessitates that the quantity of bunches, k, be given from the earlier. This choice of k esteem itself is an issue and once in a while it is difficult to anticipate before the quantity of bunches that would be there in information. This issue is settled by utilizing a metaheuristic strategy, .the man-made consciousness approach are utilized in k-mean calculation which make alterations that expansion the estimation of their target work at every single step and give better recognition rate in interruption identification framework.

Authors and Affiliations

Susheel Kumar Tiwari
Research Scholar,Mewar University,Chittorgarh,Rajasthan, India
Dr. Chandikaditya Kumawat
Professor, Department of CSE, Mewar University,Chittorgarh, Rajasthan, India
Dr. Manish Shrivastava
Professor & Head,LNCT Bhopal Affiliated to RGPV Bhopal, Madhya Pradesh, India

Intrusion Detection system, K-Mean, Data Mining

  1. A.M Chandrasekhar, K.Raghuveer,” Intrusion detection technique by using K-means, Fuzzy Neural Network and SVM classifiers”, proceedings of ICCCI,pp1-7,2013(IEEE).
  2. Praveen P Naik, Prashantha S J.”An Approach for Building Intrusion Detection System by Using Data Mining Techniques ”International Journal of Emerging Engineering Research and Technology (IJEERT) Volume 2, Issue 2, May 2014, PP 112-118.
  3. Amine Boukhtouta, Nour-Eddine Lakhdari,” Towards Fingerprinting Malicious Traffic", The 4th International Conference on Ambient Systems, Networks and Technologies (Science Direct).
  4. David Mudzingwa and Rajeev Agrawal.” Evaluating Intrusion Detection and Prevention Systems Using Tomahawk and Wireshark”, Department of Electronics, Computer and Information Technology North Carolina A&T State University, Greensboro, NC, USA.
  5. Mrs. GhatgeDipali D. - “Network Traffic Intrusion Detection System using Decision Tree & K-Means Clustering Algorithm” (IJETTCS) International Journal of Emerging Trends & Technology in Computer Science, Volume 2, Issue 5, September - October 2013.
  6. T. Subbhulakshmi1, S. G. Keerthiga2 and R. Dharini3 - “Real-Time Intelligent Multilayer Attack Classification System” ICTACT Journal On Soft Computing, January 2014, Volume: 04, Issue: 02.
  7. S. PraylaShyry, Efficient Identification of Bots by KMeans Clustering.
  8. S. Terry, B. Chow, 1999 DARPA Intrusion Detection Evaluation Data Set, edu/mission/ communi cations/cyber/CSTcorpora/ideval/data/1999data.html.

Publication Details

Published in : Volume 4 | Issue 5 | March-April 2018
Date of Publication : 2018-04-14
License:  This work is licensed under a Creative Commons Attribution 4.0 International License.
Page(s) : 277-283
Manuscript Number : CSEIT184535
Publisher : Technoscience Academy

ISSN : 2456-3307

Cite This Article :

Susheel Kumar Tiwari, Dr. Chandikaditya Kumawat, Dr. Manish Shrivastava, "Improved K-Mean Algorithm for Detection Rate in Intrusion Detection System with AI", International Journal of Scientific Research in Computer Science, Engineering and Information Technology (IJSRCSEIT), ISSN : 2456-3307, Volume 4, Issue 5, pp.277-283, March-April-2018.
Journal URL :

Article Preview

Follow Us

Contact Us