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Deep learning models for cyber security in IoT networks

Research Output: Chapter in Book/Report/Conference proceeding Conference contribution Peer-review

Abstract

In this paper we propose deep learning models for the cyber security in IoT (Internet of Things) networks. IoT network is as a promising technology which connects the living and non-living things around the world. The implementation of IoT is growing fast but the cyber security is still a loophole, so it is susceptible to many cyber-attack and for the success of any network it most important that the network is completely secure, otherwise people could be reluctant to use this technology. DDoS (Distributed Denial of Service) attack has affected many IoT networks in recent past that has resulted in huge losses. We have proposed deep learning models and evaluated those using latest CICIDS2017 datasets for DDoS attack detection which has provided highest accuracy as 97.16% also proposed models are compared with machine learning algorithms. This paper also identifies open research challenges for usage of deep learning algorithm for IoT cyber security.

Publication Information

Output type

Research Output: Chapter in Book/Report/Conference proceeding Conference contribution Peer-review

Original language

English

Publication milestones

  • Published - 14/03/2019

Publication status

Published - 14/03/2019

Publisher

Institute of Electrical and Electronics Engineers, United States

ISBN (Print)

9781728105543, 9781728105536, 9781728105550

External Publication IDs

  • ORCID: /0000-0002-3654-6035/work/114937989
  • Scopus: 85063885958

Host publication title

2019 IEEE 9th Annual Computing and Communication Workshop and Conference, CCWC 2019

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