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

MODEL DETEKSI SERANGAN SSH-BRUTE FORCE BERDASARKAN DEEP BELIEF NETWORK

Constantin Menteng (Univesitas Amikom Yogyakarta)
Arief Setyanto (Unknown)
Hanif Al Fatta (Unknown)

Article Info

Publish Date
05 Aug 2023

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.

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Journal Info
Jurnal Teknologi Informasi : Jurnal Keilmuan dan Aplikasi Bidang Teknik Informatika
Website

Abbrev

JTI

Publisher

Universitas Palangka Raya

Subject

Computer Science & IT Decision Sciences, Operations Research & Management Electrical & Electronics Engineering Engineering Library & Information Science

Description

Jurnal Teknologi Informasi (JTI) diterbitkan adalah Jurnal Jurusan Teknik Informatika Universitas Palangka Raya dengan ISSN 1907-896X, E-ISSN 2656-0321. Jurnal Teknologi Informasi (JTI) merupakan Jurnal Keilmuan dan Aplikasi Bidang Teknik Informatika yang menyajikan hasil penelitian yang fokus pada ...