Intrusion detection with deep learning on internet of things heterogeneous network
| Autor: | Sharipuddin Sharipuddin, Benni Purnama, Rahmat Budiarto, Deris Stiawan, Darmawijoyo Hanapi, Mohd. Yazid Idris, Eko Arip Winanto, Kurniabudi Kurniabudi |
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| Rok vydání: | 2021 |
| Předmět: |
Identification methods
Information Systems and Management Computer science business.industry Intrusion detection system Deep learning Features extraction Principal component analysis Artificial Intelligence Control and Systems Engineering Heterogeneous Artificial intelligence Electrical and Electronic Engineering business Internet of Things Heterogeneous network Computer network |
| Zdroj: | IAES International Journal of Artificial Intelligence (IJ-AI). 10:735 |
| ISSN: | 2252-8938 2089-4872 |
| DOI: | 10.11591/ijai.v10.i3.pp735-742 |
| Popis: | 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 internet of things (IoT). |
| Databáze: | OpenAIRE |
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