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.
|Title of host publication
|2019 IEEE 9th Annual Computing and Communication Workshop and Conference, CCWC 2019
|Satyajit Chakrabarti, Himadri Nath Saha
|Institute of Electrical and Electronics Engineers Inc.
|Number of pages
|Published - 14 Mar 2019
|9th IEEE Annual Computing and Communication Workshop and Conference - Las Vegas, United States
Duration: 7 Jan 2019 → 9 Jan 2019
Conference number: 9
|9th IEEE Annual Computing and Communication Workshop and Conference
|7/01/19 → 9/01/19