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Reseach Article

An Accurate IDS design using KDD CUP 99’s Dataset

by Ashok Panwar, D. Srinivasa Rao, G. Sriram
International Journal of Computer Applications
Foundation of Computer Science (FCS), NY, USA
Volume 181 - Number 47
Year of Publication: 2019
Authors: Ashok Panwar, D. Srinivasa Rao, G. Sriram
10.5120/ijca2019918646

Ashok Panwar, D. Srinivasa Rao, G. Sriram . An Accurate IDS design using KDD CUP 99’s Dataset. International Journal of Computer Applications. 181, 47 ( Apr 2019), 44-49. DOI=10.5120/ijca2019918646

@article{ 10.5120/ijca2019918646,
author = { Ashok Panwar, D. Srinivasa Rao, G. Sriram },
title = { An Accurate IDS design using KDD CUP 99’s Dataset },
journal = { International Journal of Computer Applications },
issue_date = { Apr 2019 },
volume = { 181 },
number = { 47 },
month = { Apr },
year = { 2019 },
issn = { 0975-8887 },
pages = { 44-49 },
numpages = {9},
url = { https://ijcaonline.org/archives/volume181/number47/30473-2019918646/ },
doi = { 10.5120/ijca2019918646 },
publisher = {Foundation of Computer Science (FCS), NY, USA},
address = {New York, USA}
}
%0 Journal Article
%1 2024-02-07T01:09:24.125592+05:30
%A Ashok Panwar
%A D. Srinivasa Rao
%A G. Sriram
%T An Accurate IDS design using KDD CUP 99’s Dataset
%J International Journal of Computer Applications
%@ 0975-8887
%V 181
%N 47
%P 44-49
%D 2019
%I Foundation of Computer Science (FCS), NY, USA
Abstract

IDS or intrusion detection systems are well known network anomaly detection technique in network technology. According to the IDS, it is used for monitoring and analysis of network traffic. By analyzing the network traffic data it observe the behavior of network and report if any anomaly in network behavior occurred. In addition of this technology is also helpful for discovering any attack condition in network. Therefore the proposed work is intended to design and develop an accurate analysis method, which works on KDD CUP 99’s Data. The proposed work first involve the feature selection technique using the correlation coefficient based technique and then the selected features are used for training and testing of three popular classifiers namely bays classifier, C4.5 decision tree and KNN algorithm. The experiments are performed using the k-fold cross validation technique. The experimental results shows the KNN and C4.5 decision tree algorithm produces similar accuracy and higher as compared to bays classifier. But the time consumption of the KNN classifier is 10 times higher than the C4.5 and Bays classification techniques.

References
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  9. AUTHORS PROFILE
  10. ASHOK PANWAR received has Three Year Polytechnic Diploma in Computer Science and Engineering, B.E. / B. Tech. and M.E. / M. Tech. Degree both in Computer Science and Engineering. He is Currently working as an Technical Officer in ECIL (Electronics Corporation of India Limited), Hyderabad, India, against the site requirements of NPCIL (Nuclear Power Corporation of India Limited), Tarapur, Mumbai, Maharashtra, Working in ACS (Access Control System) Department, as well as Research Scholar, Ex. Empl
  11. D. SRINIVASA RAO M.Tech, Ph.D is working as an Associate Professor in the Department of Computer Science & Engineering at Medi-Caps University, Indore, Madhya Pradesh, India. He has 22 years of teaching experience. His area of interest in Adhoc Networks, Distributed Systems, Network Security & Image Processing. He has guided more than 60 Post Graduate Students. He has published 2 books and 18 papers in international journals. He presented 2 papers in National Conferences, 1 paper in Inte
  12. G. SRIRAM M.Tech, Ph.D is working as an Assistant Professor in the Department of Computer Science, School of Distance Education, Andhra University, Visakhapatnam, India. He has 13 years of teaching experience. His area of interest in Adhoc Networks, Data Mining & Networks Security. He has guided 25 Graduate Students. He has published 5 papers in international journals. He has attended 10 National Workshops / FDP / Seminars etc.
Index Terms

Computer Science
Information Sciences

Keywords

KDD CUP dataset Classification data mining network security IDS design