The week's pick
Random Articles
Reseach Article
A Data Mining Approach for Attribute Selection in Intrusion Detection System
| International Journal of Computer Applications |
| Foundation of Computer Science (FCS), NY, USA |
| Volume 172 - Number 1 |
| Year of Publication: 2017 |
| Authors: Richa Pandey, Janmejay Pant |
10.5120/ijca2017915056
|
Richa Pandey, Janmejay Pant . A Data Mining Approach for Attribute Selection in Intrusion Detection System. International Journal of Computer Applications. 172, 1 ( Aug 2017), 11-14. DOI=10.5120/ijca2017915056
Abstract
Data Mining is a collection of tools and techniques for extraction of useful data from large amount of databases and now a days there are many intruders who try to steal useful data or to change the originality of data.An Intrusion Detection System (IDS) is a method which is use for defence method which check the activities of the computer network and reports the malicious activities to the network administrator if there is any. As the Intruders do more than one attempts to gain access to the network and try to destroy the authentication of the organization’s data. The security which is the main issue for any organization have to take steps for maintaining the originality of the data. Thus intrusion detection field has been an important research issue in today’s world. In this paper we are going to discover an approach for attribute selection which helps in improvement of the accuracy which will be shown by ROC curve which is Receiving Operating Characteristic Curve. By gaing the results by do algorithm we will have the best way to improve in the curve.
References
- Intrusion detection system https://en.wikipedia.org/wiki/Intrusion_detection_system
- International Journal of Advances in Engineering & Technology, July 2013. ©IJAET ISSN: 22311963 1319 Vol. 6, Issue 3, pp. 1319-1324 Feature selection using random forest in intrusion detection system Sneh Lata Pundir and Amrita Department of CSE, Sharda University, Greater Noida, India
- Ashalata Panigrahi and Manas Ranjan Patra, “An ANN Approach for Network intrusion detection using entropy based feature selection”. International Journal of Network Security & Its Applications (IJNSA), Vol.7, No.3, May 2015
- An Introduction to the WEKA Data Mining System by Zdravko, Ingrid Russell University of Hartford.
- Nsl-Kdd data set for network-based intrusion detection system. Available: http:// iscx.ca/NSL-KDD/.
- Feature Selection for High-Dimensional Data: A Fast Correlation-Based Filter Solution Lei Yu Huan Liu
- Shelly Gupta et al./ Indian Journal of Computer Science and Engineering (IJCSE) DATA MINING CLASSIFICATION TECHNIQUES APPLIED FOR BREAST CANCER DIAGNOSIS AND PROGNOSIS
- Journal of Machine Learning Research 11 (2010) 2533-2541 Submitted 6/10; Revised 8/10; Published 9/10 WEKA—Experiences with a Java Open-Source Project
- Harshit Saxena, Vineet Richaariya, “Intrusion Detection in KDD99 Dataset using SVM-PSO and Feature Reduction with Information Gain”, International Journal of Computer Applications (0975 – 8887) Volume 98– No.6, July 2014.
- Nupur N. Majethiya and Dipak C. Patel, “Efficient Intrusion Detection System with Reduced Dimensionality”, International Journal of Emerging Trends & Technology in Computer Science (IJETTCS) Volume 4, Issue 2, March-April 2015 ISSN 2278-6856.
- Abebe Tesfahun, D. Lalitha Bhaskari “Effective Hybrid Intrusion Detection System: A Layered Approach”, I. J. Computer Network and Information Security, 2015, 3, 35-41.
- Sunil Choudhary and Pankaj Dalal, “An Architecture for Network Intrusion Detection System based on DAG Classification”, International Journal of Advanced Research in Computer and Communication Engineering, Vol. 4, Issue 4, April 2015.
- Venkata Suneetha Takkellapati et.al “Network Intrusion Detection system based on Feature Selection and Triangle area Support Vector Machine” International Journal of Engineering Trends and Technology- Volume 3 Issue 4- 2012.
Index Terms
Keywords