Fuzzy ECOC Framework for Network Intrusion Detection System
Uma Shankar Rao Erothi1, Sireesha Rodda2

1Uma Shankar Rao Erothi, Dept of CSE, RAGHU Institute of Technology, Visakhapatnam, India.
2Sireesha Rodda, Dept of CSE, GITAM University, Visakhapatnam, India.

Manuscript received on 12 August 2019. | Revised Manuscript received on 18 August 2019. | Manuscript published on 30 September 2019. | PP: 6826-6833 | Volume-8 Issue-3 September 2019 | Retrieval Number: C5783098319/2019©BEIESP | DOI: 10.35940/ijrte.C5783.098319
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© The Authors. Blue Eyes Intelligence Engineering and Sciences Publication (BEIESP). This is an open access article under the CC-BY-NC-ND license (http://creativecommons.org/licenses/by-nc-nd/4.0/)

Abstract: 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.
Keywords: Network Intrusion Detection System, Fuzzy Classification, ECOC, multi Class Imbalance, Machine Learning.

Scope of the Article:
Machine Learning