IAES International Journal of Artificial Intelligence (IJ-AI)
Vol 10, No 3: September 2021

Intrusion detection with deep learning on internet of things heterogeneous network

Sharipuddin Sharipuddin (Universitas Sriwijaya)
Benni Purnama (Universitas Sriwijaya)
Kurniabudi Kurniabudi (Universitas Sriwijaya)
Eko Arip Winanto (Universiti Teknologi Malaysia)
Deris Stiawan (Universitas Sriwijaya)
Darmawijoyo Hanapi (Universitas Sriwijaya)
Mohd. Yazid Idris (Universiti Teknologi Malaysia)
Rahmat Budiarto (Albaha University)

Article Info

Publish Date
01 Sep 2021

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 internet of things (IoT).

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Journal Info
IAES International Journal of Artificial Intelligence (IJ-AI)
Website

Abbrev

IJAI

Publisher

Institute of Advanced Engineering and Science

Subject

Computer Science & IT Engineering

Description

IAES International Journal of Artificial Intelligence (IJ-AI) publishes articles in the field of artificial intelligence (AI). The scope covers all artificial intelligence area and its application in the following topics: neural networks; fuzzy logic; simulated biological evolution algorithms (like ...