Title: Multi-criteria decision support for feature selection in network anomaly detection system

Authors: C. Seelammal; K. Vimala Devi

Addresses: Department of IT, Sethu Institute of Technology, Tamil Nadu, India ' Department of CSE, Velammal Engineering College, Chennai, Tamil Nadu, India

Abstract: The growth of computer networks from LAN to cloud, virtualisation and mobility always keeps intrusion detection system (IDS) as a critical component in the field of network security infrastructure. Tremendous growth and usage of internet raises concerns about how to protect and communicate the digital information in a safe manner. The market for security solutions for next-generation is rapidly evolving and constantly changing to accommodate today's threat. Many intrusion detection techniques, methods and algorithms are implemented to detect these novel attacks. But there's no clear feature set, uncertainty bounds established as a baseline for dynamic environments. The main objective of this paper is to determine and provide the best feature selection for next generation dynamic environments using multi-criteria decision making, decision tree learning with emphasis on optimisation (contingency of weight allocation) and handling large datasets.

Keywords: intrusion detection; multi-criteria; classification; anomaly; data mining; feature selection; machine learning.

DOI: 10.1504/IJDATS.2018.094132

International Journal of Data Analysis Techniques and Strategies, 2018 Vol.10 No.3, pp.334 - 350

Received: 14 May 2016
Accepted: 31 Jan 2017
Published online: 17 Aug 2018 *

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