Intrusion detection with deep learning on internet of things heterogeneous network

Sharipuddin Sharipuddin, Benni Purnama, Kurniabudi Kurniabudi, Eko Arip Winanto, Deris Stiawan, Darmawijoyo Hanapi, Mohd. Yazid Idris, Rahmat Budiarto


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).


Deep learning; Features extraction; Heterogeneous; Intrusion detection system; Principal component analysis

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