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ENHANCED STACKED PADA LONG SHORT TERM MEMORY UNTUK MENINGKATKAN KEMAMPUAN KLASIFIKASI SERANGAN DDOS PADA DATASET CICDDOS 2019

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Penilaian

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Distributed Denial Of Service (DDoS) attacks are a type of cyber attack against websites. DDoS is marked by the amount of fake traffic that floods the server, system or internet network. As a result, the target website cannot be accessed because it is unable to manage too much traffic entering the server. There are three objectives in this research, including building a Long Shoer Term Memory Stacked model to classify DDoS attacks on network traffic records with the CICDDoS 2019 dataset. The second is to test the model in terms of time and resources needed when compared to existing models. The three produce the model with the best performance in the best performance in the classification of DDoS attacks. The Deep Learning method used is the BI-Directional LSTM method which is a branch of LSTM which has the advantage of having 2 layers, namely the forward layer and the backward layer so that it allows additional information enhancement and improves memory capabilities. This research was conducted by training the CIC-DDoS 2019 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 98.16%, precision 96.93 %, recall 99.39%, specificity 96.99% and f1 score 98.14%.

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Availability

Inventory Code	Barcode	Call Number	Location	Status
2307000077	T86531	T865312023	Central Library (Referens)	Available but not for loan - Not for Loan

Detail Information

Series Title

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Call Number

T865312023

Publisher

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

Collation

xvi, 73 hlm.; ilus.; 29 cm

Language

Indonesia

ISBN/ISSN

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Classification

005.707

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
Data dalam sistem-sistem komputer

Specific Detail Info

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Statement of Responsibility

SEPTA

Other version/related

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