PENGOPTIMALAN LONG SHORT-TERM MEMORY (LSTM) DENGAN AUTOENCODER UNTUK MENDETEKSI BOTNET

HARDRIAN, MARCHO and Heryanto, Ahmad (2022) PENGOPTIMALAN LONG SHORT-TERM MEMORY (LSTM) DENGAN AUTOENCODER UNTUK MENDETEKSI BOTNET. Undergraduate thesis, Sriwijaya University.

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Abstract

Botnet is a group of programs that have been infected by malware and connected to the internet network that has been controlled by certain parties. Using Long Short-Term Memory (LSTM) can help detect botnet attack data. LSTM, can return better accuracy or better Confusion Matrix. Before entering the detection process, the data first goes through the autoencoder process. The Autoencoder process is used so that the data used is smaller and results in a more efficient computing process. Based on the results of the research that has been done, the Long Short Term-Memory (LSTM) was successfully applied in the Botnet attack detection system, with the best results obtained an accuracy value of 99.86%, a specificity of 99.88%, a sensitivity value of 99.86%, a precision value of 99.95%, and the f1-score is 99.90%.

Item Type:	Thesis (Undergraduate)
Uncontrolled Keywords:	LONG SHORT-TERM MEMORY (LSTM)
Subjects:	T Technology > T Technology (General) > T1-995 Technology (General)
Divisions:	09-Faculty of Computer Science > 56201-Computer Systems (S1)
Depositing User:	Marcho Hardrian
Date Deposited:	03 Feb 2023 06:02
Last Modified:	03 Feb 2023 06:02
URI:	http://repository.unsri.ac.id/id/eprint/87929

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