A Scalable Feature Selection Approach For IDS
Bharti Harode
, Anurag Jain , Chetan Agrawal
Intrusion Detection, NSL-KDD, Classification Techniques, Feature Selection.
Communication plays a vital role in everyone’s life, as it is the only way for ideas and data to be exchanged amongst each other. There are various ways by which we can communicate with one another such as Telephones, Radio, Television and most recently is the Internet. With internet communication effortlessly communication is possible at anytime from anywhere to anyplace on the planet which increases the productivity of work. Computer network was developed to make the communication easier amidst individuals. The tremendous growth in network and accessibility of internet increased the security issues in the field of networking. Over the last few years as the usage of internet has increased, the no of attacks over the network has increased, its performance along with security has also been affected to multiple folds. Intrusion is set of actions that attempt to compromise the integrity, confidentiality, or availability of a network and its resource and an intrusion detection system (IDS) is a system for the detection of such intrusions. Intrusion Detection System consists of three components Data collection, normalization and classification. In this paper InfoGain, a feature selection technique is used for the reducing the size of dataset. Different classifiers viz Naïve Bayes, Random Forest and J48 were implemented using WEKA to find the algorithm with best accuracy.
"A Scalable Feature Selection Approach For IDS", IJSDR - International Journal of Scientific Development and Research (www.IJSDR.org), ISSN:2455-2631, Vol.4, Issue 7, page no.425 - 429, July-2019, Available :https://ijsdr.org/papers/IJSDR1907072.pdf
Volume 4
Issue 7,
July-2019
Pages : 425 - 429
Paper Reg. ID: IJSDR_190860
Published Paper Id: IJSDR1907072
Downloads: 000347227
Research Area: Engineering
Country: Gwalior, MADHYA PRADESH, India
ISSN: 2455-2631 | IMPACT FACTOR: 9.15 Calculated By Google Scholar | ESTD YEAR: 2016
An International Scholarly Open Access Journal, Peer-Reviewed, Refereed Journal Impact Factor 9.15 Calculate by Google Scholar and Semantic Scholar | AI-Powered Research Tool, Multidisciplinary, Monthly, Multilanguage Journal Indexing in All Major Database & Metadata, Citation Generator
Publisher: IJSDR(IJ Publication) Janvi Wave
