Enhanced Deep Learning Intrusion Detection in IoT Heterogeneous Network with Feature Extraction(plagiarism)

Darmawijoyo, Darmawijoyo (2023) Enhanced Deep Learning Intrusion Detection in IoT Heterogeneous Network with Feature Extraction(plagiarism). Turnitin Universitas Sriwijaya. (Submitted)

Text (Sharipuddin., Winanto, E.A., Purnama, B., Kurniabudi., Stiawan, D., Hanapi, D., dkk. (2021) Enhanced Deep Learning Intrusion Detection in IoT Heterogeneous Network with Feature Extraction (Plagiarism). Turnitin Universitas Sriwijaya.) J70 Turnitin.pdf - Other Available under License Creative Commons Public Domain Dedication. Download (1MB)

Abstract

Heterogeneous network is one of the challenges that must be overcome in Internet of Thing Intrusion Detection System (IoT IDS). The difficulty of the IDS significantly is caused by various devices, protocols, and services, that make the network becomes complex and difficult to monitor. Deep learning is one algorithm for classifying data with high accuracy. This research work incorporated Deep Learning into IDS for IoT heterogeneous networks. There are two concerns on IDS with deep learning in heterogeneous IoT networks, i.e.: limited resources and excessive training time. Thus, this paper uses Principle Component Analysis (PCA) as features extraction method to deal with data dimensions so that resource usage and training time will be significantly reduced. The results of the evaluation show that PCA was successful reducing resource usage with less training time of the proposed IDS with deep learning in heterogeneous networks environment. Experiment results show the proposed IDS achieve overall accuracy above 99%.

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 04:37
Last Modified:	21 Jun 2023 04:37
URI:	http://repository.unsri.ac.id/id/eprint/111666

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