DETEKSI SERANGAN DENIAL OF SERVICE (DOS) PADA PROTOCOL JARINGAN IEC 61850 SUPERVISORY CONTROL AND DATA ACQUISITION (SCADA) DENGAN MENGGUNAKAN METODE DEEP NEURAL NETWORK (DNN)

FAHD, MOHAMMAD AT'THAWRI ABEL and Deris, Stiawan (2024) DETEKSI SERANGAN DENIAL OF SERVICE (DOS) PADA PROTOCOL JARINGAN IEC 61850 SUPERVISORY CONTROL AND DATA ACQUISITION (SCADA) DENGAN MENGGUNAKAN METODE DEEP NEURAL NETWORK (DNN). Undergraduate thesis, Sriwijaya University.

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Abstract

DoS attack detection is important because SCADA networks are used in large companies, making them vulnerable to attack. In this research, the deep neural network (DNN) method is used to detect DoS attacks in the dataset. The dataset used in this study is the IEC61850SecurityDataset in 2019 which contains GOOSE protocol data on SCADA networks that have been contaminated with DoS. The dataset is balanced using the oversampling technique. The model shows unsatisfactory results at a data ratio of 70:30 and gets satisfactory results at a data ratio of 80:20, with an accuracy of 96.75%, precision 100%, recall 93.46 and f1 score 96.62%. These results can show that the model has consistent and reliable performance in the detection task.

Item Type:	Thesis (Undergraduate)
Uncontrolled Keywords:	Supervisory Control and Data Acquisition, Attack Detection, Denial of Service, Deep Neural Network
Subjects:	Q Science > Q Science (General) > Q300-390 Cybernetics > Q325.5 Machine learning Q Science > QA Mathematics > QA75-76.95 Calculating machines > QA76.9.A25 Computer security. Systems and Data Security. Q Science > QA Mathematics > QA8.9-QA10.3 Computer science. Artificial intelligence. Computational complexity. Data structures (Computer scienc. Mathematical Logic and Formal Languages T Technology > T Technology (General) > T1-995 Technology (General)
Divisions:	09-Faculty of Computer Science > 56201-Computer Systems (S1)
Depositing User:	Mohammad At'thawri Abel Fahd
Date Deposited:	29 May 2024 06:07
Last Modified:	29 May 2024 06:07
URI:	http://repository.unsri.ac.id/id/eprint/146030

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