Intrusion detection with deep learning on internet of things heterogeneous network

Published on Sep 1, 2021in IAES International Journal of Artificial Intelligence
· DOI :10.11591/IJAI.V10.I3.PP735-742
Sharipuddin Sharipuddin1
Estimated H-index: 1
(Sriwijaya University),
Benni Purnama3
Estimated H-index: 3
(Sriwijaya University)
+ 5 AuthorsRahmat Budiarto13
Estimated H-index: 13
(Al Baha University)
Sources
Abstract
The difficulty of the intrusion detection system in heterogeneous networks is significantly affected by devices, protocols, and services, thus the network becomes complex and difficult to identify. Deep learning is one algorithm that can classify data with high accuracy. In this research, we proposed deep learning to intrusion detection system identification methods in heterogeneous networks to increase detection accuracy. In this paper, we provide an overview of the proposed algorithm, with an initial experiment of denial of services (DoS) attacks and results. The results of the evaluation showed that deep learning can improve detection accuracy in the heterogeneous IoT.
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