Open AccessIntrusion detection with deep learning on internet of things heterogeneous network
Author(s) -
Sharipuddin Sharipuddin,
Benni Purnama,
Kurniabudi Kurniabudi,
Eko Arip Winanto,
Deris Stiawan,
Darmawijoyo Hanapi,
Mohd. Yazid Idris,
Rahmat Budiarto
Publication year - 2021
Publication title -
iaes international journal of artificial intelligence
Language(s) - English
Resource type - Journals
eISSN - 2252-8938
pISSN - 2089-4872
DOI - 10.11591/ijai.v10.i3.pp735-742
Subject(s) - computer science , intrusion detection system , deep learning , internet of things , the internet , artificial intelligence , denial of service attack , identification (biology) , heterogeneous network , machine learning , data mining , computer network , computer security , world wide web , wireless network , telecommunications , botany , wireless , biology
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).