Evolving neural network intrusion detection system for MCPS

Nishat Mowla, Inshil Doh, Kijoon Chae

Research output: Chapter in Book/Report/Conference proceedingConference contributionpeer-review

4 Scopus citations

Abstract

Medical Cyber Physical Systems (MCPS) are some of the most promising next generation technologies so far. Like many other systems connected to a wider network such as internet, MCPS are also vulnerable to various forms of network attacks. For detecting such diverse forms of attack, we need smart and efficient mechanisms. Human intelligence is good enough to track such attacks but when it is a huge number of traffic it is no more a feasible process to detect them manually as it is time consuming and computationally intensive. Machine learning techniques embracing artificial intelligence are emerging as powerful tools to detect abnormalities in the network data. Supervised Neural Networks are some of the most efficient techniques to perform such classification. In this paper, we propose an evolving neural network technique that evolves based on classification, elimination and prioritization while focusing on time, space and accuracy to efficiently classify the four major types of network attack traffic found in an effectively pruned KDD dataset. We also show a leap of performance with hyper-parameter optimization which highly enhances the benefit of our proposed mechanism. Finally, the new performance gain is compared with a boosted Decision Tree. We believe our proposed mechanism can be adopted to new forms of attack categories and sub-categories.

Original languageEnglish
Title of host publicationIEEE 20th International Conference on Advanced Communication Technology
Subtitle of host publicationOpening New Era of Intelligent Things, ICACT 2018
PublisherInstitute of Electrical and Electronics Engineers Inc.
Pages1040-1045
Number of pages6
ISBN (Electronic)9791188428007
DOIs
StatePublished - 23 Mar 2018
Event20th IEEE International Conference on Advanced Communication Technology, ICACT 2018 - Chuncheon, Korea, Republic of
Duration: 11 Feb 201814 Feb 2018

Publication series

NameInternational Conference on Advanced Communication Technology, ICACT
Volume2018-February
ISSN (Print)1738-9445

Conference

Conference20th IEEE International Conference on Advanced Communication Technology, ICACT 2018
Country/TerritoryKorea, Republic of
CityChuncheon
Period11/02/1814/02/18

Bibliographical note

Publisher Copyright:
© 2018 Global IT Research Institute (GiRI).

Keywords

  • Intrusion Detection System
  • Machine Learning
  • MCPS
  • Neural Networks

Fingerprint

Dive into the research topics of 'Evolving neural network intrusion detection system for MCPS'. Together they form a unique fingerprint.

Cite this