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

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Penilaian

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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%.

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Availability

Inventory Code	Barcode	Call Number	Location	Status
2307000792	T87929	T879292023	Central Library (Referens)	Available but not for loan - Not for Loan

Detail Information

Series Title

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Call Number

T879292023

Publisher

Inderalaya : Jurusan Sistem Komputer, Fakultas Ilmu Komputer Universitas Sriwijaya., 2023

Collation

xv, 79 hlm.; ilus.; 29 cm

Language

Indonesia

ISBN/ISSN

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Classification

006.307

Content Type

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Media Type

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Carrier Type

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Edition

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Subject(s)

Sistem Pakar
Jurusan Sistem Komputer

Specific Detail Info

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Statement of Responsibility

SEPTA

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