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All Journal Jurnal Teknologi Informasi : Jurnal Keilmuan dan Aplikasi Bidang Teknik Informatika
Constantin Menteng
Univesitas Amikom Yogyakarta
Computer Science & IT Decision Sciences, Operations Research & Management Electrical & Electronics Engineering Engineering Library & Information Science

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MODEL DETEKSI SERANGAN SSH-BRUTE FORCE BERDASARKAN DEEP BELIEF NETWORK Constantin Menteng; Arief Setyanto; Hanif Al Fatta
Jurnal Teknologi Informasi: Jurnal Keilmuan dan Aplikasi Bidang Teknik Informatika Vol. 17 No. 2 (2023): Jurnal Teknologi Informasi : Jurnal Keilmuan dan Aplikasi Bidang Teknik Inform
Publisher : Universitas Palangka Raya

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.47111/jti.v7i2.8151

Abstract

Deep Belief Networks are deep learning models that utilize stacks of Restricted Boltzmann Machines (RBM) or sometimes Autoencoders. Autoencoder is a neural network model that has the same input and output. The autoencoder learns the input data and attempts to reconstruct the input data. The solution in this study can provide several tests on DBN such as detecting recall accuracy and better classification precision. By using this algorithm, it is hoped that we as users can overcome problems that occur quite often such as brute force attacks in our accounts and within the company. And the results obtained from this DBN experiment are with an accuracy value of 90.27%, recall 90.27%, precession 91.67%, F1-score 90.51%. The results of this study are the data values of accuracy, recall, precession, and f1-score data used to detect brute force attacks are quite efficient using the deep model of the deep belief network.
Co-Authors Arief Setyanto HANIF AL FATTA