Deep Learning Based Intrusion Detection Systems Techniques in IoT-Survey

Samay Kalpesh Patel, Sapna Sadhwani, Raja Muthalagu, Pranav Mothabhau Pawar

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

Abstract

Industry 4.0 is changing the way we communicate and operate as a society, its bringing changes in technologies, industries and a part of this industry Internet of Thing (IoT), they are devices which communicate with each other and are being integrated slowly in all sectors.This creates number of concerns especially towards security and privacy. Cyber intrusion attacks form a major part of the concern as it compromises integrity of sensitive data and are growing in volume with variations increasing rapidly. High complexity of such intrusion attacks has defeated most of the traditional defense techniques This paper focuses on exploring research that was conducted in area of IoT security, specifically in improvement of Intrusion detection system using Deep learning techniques. The results and methods are also discussed which can form a potential base for further research.

Original languageEnglish
Title of host publicationProceedings of 3rd IEEE International Conference on Computational Intelligence and Knowledge Economy, ICCIKE 2023
EditorsAnand Kumar, Ved Prakash Mishra, Vishal Naranje, Apurv Yadav
PublisherInstitute of Electrical and Electronics Engineers Inc.
Pages53-58
Number of pages6
ISBN (Electronic)9798350338263
DOIs
StatePublished - 2023
Externally publishedYes
Event3rd IEEE International Conference on Computational Intelligence and Knowledge Economy, ICCIKE 2023 - Dubai, United Arab Emirates
Duration: 9 Mar 202310 Mar 2023

Publication series

NameProceedings of 3rd IEEE International Conference on Computational Intelligence and Knowledge Economy, ICCIKE 2023

Conference

Conference3rd IEEE International Conference on Computational Intelligence and Knowledge Economy, ICCIKE 2023
Country/TerritoryUnited Arab Emirates
CityDubai
Period9/03/2310/03/23

Bibliographical note

Publisher Copyright:
© 2023 IEEE.

Keywords

  • Cyber-Attacks
  • Deep learning
  • IDS
  • IoT
  • IoT Architecture
  • Security

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