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KLASIFIKASI SERANGAN DDOS PADA DATASET CICDDOS2019 MENGGUNAKAN METODE CNN LONG SHORT-TERM MEMORY

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Denial of Service (DoS) is one of the popular cyber attacks targeting websites of well-known organizations and has the potential to have high economic and time costs. One of the most common types of DoS is the Distributed Denial-of-Service (DDoS) attack which aims to bring down the target service using various distributed resources. In an effort to handle Distributed Denial-of-Service (DDoS) attacks, one important step that must be taken is to classify the type of attack that occurs. One of the classification methods that can be used is the Convolutional Neural Network (CNN) Long Short-Term Memory (LSTM). This study was conducted to design an optimal CNN-LSTM architecture for DDoS attack classification, obtain accurate classification results in efficient time using CNN-LSTM on complex DDoS attacks and implement a DDoS attack classification model using the CNN-LSTM method on a network security system and monitor it in real-time. The dataset used in this study uses the CSE-CIC-IDS2019 dataset obtained from the University of New Brunswick Canada website. The results of this study show that the CNN LSTM method model is able to classify DDoS attacks on the CICDDoS2019 dataset well. The application of data preprocessing can be implemented well and precisely, which can be seen in the 50:50 ratio with an accuracy of 98.82%. The results of the DDoS attack classification performance have proven to run well by producing an accuracy of 98.82%, a precision of 98.75%, a recall of 98.89%, and a specificity of 98.77%.

Availability
Inventory Code Barcode Call Number Location Status
2507005398T182705T1827052025Central Library (Reference)Available but not for loan - Not for Loan
Detail Information
Series Title
-
Call Number
T1827052025
Publisher
Indralaya : Prodi Sistem Komputer, Fakultas Ilmu Komputer Universitas Sriwijaya.,
Collation
xxiii, 443 hlm.; ilus.; tab.; 29 cm.
Language
Indonesia
ISBN/ISSN
-
Classification
004.650 7
Content Type
Text
Media Type
unmediated
Carrier Type
-
Edition
-
Subject(s)
Prodi Sistem Komputer
Convolutional Neural Network (CNN)
Specific Detail Info
-
Statement of Responsibility
Other version/related
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