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REDUKSI DIMENSI FITUR SERANGAN BRUTEFORCE MENGGUNAKAN METODE AUTOENCODER LSTM (AE-LSTM) PADA SISTEM PENDETEKSI SERANGAN SIBER

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Penilaian

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Bruteforce attack is an attack that uses the trial-error method to obtain user information, especially passwords. To overcome these problems, previous research has applied learning algorithms to cyber attack detection systems and obtained satisfactory results. In the cyber attack approach, there is a misdiagnosis that can arise due to high dimensional data. This study aims to apply the autoencoder algorithm as an attack feature dimension reduction method and the Long Short-Term Memory (LSTM) algorithm as a bruteforce attack classification method. The dataset used in this study is the CICIDS2018 dataset which contains Attack Data (FTP-Bruteforce and SSH-Bruteforce) and Normal Data (Benign). Data classification results obtained good accuracy of 99.9970%, with Recall of 99.9970%, Specificity of 99.9969%, Precision of 99.9969% and F1-Score of 99.9977%.

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Availability

Inventory Code	Barcode	Call Number	Location	Status
2307002532	T114151	T1141512023	Central Library (Referens)	Available but not for loan - Not for Loan

Detail Information

Series Title

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Call Number

T1141512023

Publisher

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

Collation

xviii, 157 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

Other version/related

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