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KLASIFIKASI SERANGAN BOTNET MENGGUNAKAN METODE BI-DIRECTIONAL LONG SHORT-TERM MEMORY

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Penilaian

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Botnet attacks have become a major threat on the internet in recent years. Because a botnet is a collection of programs in which there is malware, both of which are connected to each other within the scope of the internet network, which can communicate with a collection of similar malware bot programs to do work that is detrimental and targets the intended victim. There are three objectives in this research, among others, to build a model of the Bi-Directional LSTM method for the ability to classify botnet attacks on the CIC-IDS 2018 dataset. Second, apply PCA feature selection to optimize the classification of botnet attacks. And thirdly Knowing the results of the classification performance of Botnet attacks seen from the results of accuracy, specificity, recall, precision. Therefore, to overcome the previous problem, the deep learning method was used. The Deep Learning method used is the BI-Directional LSTM method which is a branch of LSTM which has the advantage of having two layers, namely the forward layer and the backward layer so that it allows additional information enhancement and improves memory capabilities. This research has three benefits, including applying the Bi-Directional Long Short-Term Memory method for classifying Botnet attacks. The second is to optimize the Bi-Directional Long Short Term Memory method so as to get a high accuracy value. The third is to find out the performance of Bi-Directional Long Short-Term Memory results to classify Botnet attacks. This research was conducted by training the 2018 CIC-IDS dataset on machine learning with the provision of tuning hyperparameters and comparing results with different ratios of training data and test data so that the best evaluation results were obtained with an accuracy value of 99.82% accuracy, 99.76% precision, 99.89 recall. %, and a specificity of 99.82%.

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Availability

Inventory Code	Barcode	Call Number	Location	Status
2207005396	T85403	T854032022	Central Library (Referens)	Available but not for loan - Not for Loan

Detail Information

Series Title

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Call Number

T854032022

Publisher

Inderalaya : Jurusan Sistem Komputer, Fakultas Ilmu Komputer Universitas Sriwijaya., 2022

Collation

xiv, 89 hlm.; ilus.; 29 cm

Language

Indonesia

ISBN/ISSN

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Classification

004.507

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)

Jurusan Sistem Komputer
random access memory/RAM

Specific Detail Info

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Statement of Responsibility

SEPTA

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