Intrusion detection with deep learning on internet of things heterogeneous network(plagiarism)

Darmawijoyo, Darmawijoyo (2023) Intrusion detection with deep learning on internet of things heterogeneous network(plagiarism). Turnitin Universitas Sriwijaya. (Submitted)

Text (Sharipuddin., Purnama, B., Kurniabudi., Winanto, E.A., Stiawan, D., Darmawijoyo., dkk. (2021) Intrusion detection with deep learning on internet of things heterogeneous network (Plagiarism). Turnitin Universitas Sriwijaya.) J68 Turnitin.pdf - Other Available under License Creative Commons Public Domain Dedication. Download (1MB)

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).

Item Type:	Other
Subjects:	Q Science > QA Mathematics > QA1-43 General #3 Repository of Lecturer Academic Credit Systems (TPAK) > Results of Ithenticate Plagiarism and Similarity Checker
Divisions:	06-Faculty of Education and Educational Science > 84202-Mathematics Education (S1)
Depositing User:	Darmawijoyo Darmawijoyo
Date Deposited:	21 Jun 2023 04:36
Last Modified:	21 Jun 2023 04:36
URI:	http://repository.unsri.ac.id/id/eprint/111656

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