Open AccessFuzzy ECOC Framework for Network Intrusion Detection System
Author(s) -
Uma Shankar Rao Erothi,
Sireesha Rodda
Publication year - 2019
Publication title -
international journal of recent technology and engineering
Language(s) - English
Resource type - Journals
ISSN - 2277-3878
DOI - 10.35940/ijrte.c5783.098319
Subject(s) - computer science , intrusion detection system , benchmark (surveying) , data mining , computer security , fuzzy logic , network security , the internet , constant false alarm rate , variety (cybernetics) , machine learning , frame (networking) , artificial intelligence , computer network , world wide web , geodesy , geography
Many aspects of our life now continually rely on computers and internet. Data sharing among networks is a major challenge in several areas, including communication, national security, medicine, marketing, finance and even education. Many small scale and large scale industries are becoming vulnerable to a variety of cyber threats due to increase in the usage of computers over network. We propose Fuzzy-ECOC frame work for network intrusion detection system, which can efficiently thwart malicious attacks. The focus of the paper is to enforce cyber security threats, generalization rules for classifying potential attacks, preserving privacy among data sharing and multi-class imbalance problem in intrusion data. The Fuzzy-ECOC framework is validated on highly imbalanced benchmark NSL_KDD intrusion dataset as well as six other UCI datasets. The experimental results show that Fuzzy-ECOC achieved best detection rate and least false alarm rate.