Welcome to the InfoSci Platform
A Roughset Based Ensemble Framework for Network Intrusion Detection System

A Roughset Based Ensemble Framework for Network Intrusion Detection System

Sireesha Rodda, Uma Shankar Erothi
Copyright: © 2018 |Volume: 5 |Issue: 3 |Pages: 18
ISSN: 2334-4598|EISSN: 2334-4601|EISBN13: 9781522547037|DOI: 10.4018/IJRSDA.2018070105
Cite Article Cite Article

MLA

Rodda, Sireesha, and Uma Shankar Erothi. "A Roughset Based Ensemble Framework for Network Intrusion Detection System." IJRSDA vol.5, no.3 2018: pp.71-88. http://doi.org/10.4018/IJRSDA.2018070105

APA

Rodda, S. & Erothi, U. S. (2018). A Roughset Based Ensemble Framework for Network Intrusion Detection System. International Journal of Rough Sets and Data Analysis (IJRSDA), 5(3), 71-88. http://doi.org/10.4018/IJRSDA.2018070105

Chicago

Rodda, Sireesha, and Uma Shankar Erothi. "A Roughset Based Ensemble Framework for Network Intrusion Detection System," International Journal of Rough Sets and Data Analysis (IJRSDA) 5, no.3: 71-88. http://doi.org/10.4018/IJRSDA.2018070105

Export Reference

Mendeley
Favorite Full-Issue Download

Abstract

Designing an effective network intrusion detection system is becoming an increasingly difficult task as the sophistication of the attacks have been increasing every day. Usage of machine learning approaches has been proving beneficial in such situations. Models may be developed based on patterns differentiating attack traffic from network traffic to gain insight into the network activity to identify and report attacks. In this article, an ensemble framework based on roughsets is used to efficiently identify attacks in a multi-class scenario. The proposed methodology is validated on benchmark KDD Cup '99 and NSL_KDD network intrusion detection datasets as well as six other standard UCI datasets. The experimental results show that proposed technique RST achieved better detection rate with low false alarm rate compared to bagging and RSM.

Request Access

You do not own this content. Please login to recommend this title to your institution's librarian or purchase it from the IGI Global bookstore.