Deep Learning Models for Cyber Security in IoT Networks

Monika Roopak, Gui Yun Tian, Jonathon Chambers

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

259 Citations (Scopus)


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.

Original languageEnglish
Title of host publication2019 IEEE 9th Annual Computing and Communication Workshop and Conference, CCWC 2019
EditorsSatyajit Chakrabarti, Himadri Nath Saha
PublisherInstitute of Electrical and Electronics Engineers Inc.
Number of pages6
ISBN (Electronic)9781728105543, 9781728105536
ISBN (Print)9781728105550
Publication statusPublished - 14 Mar 2019
Externally publishedYes
Event9th IEEE Annual Computing and Communication Workshop and Conference - Las Vegas, United States
Duration: 7 Jan 20199 Jan 2019
Conference number: 9


Conference9th IEEE Annual Computing and Communication Workshop and Conference
Abbreviated titleCCWC 2019
Country/TerritoryUnited States
CityLas Vegas


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