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

PANGESTU, MUHAMMAD ALDI and Heryanto, Ahmad (2025) KLASIFIKASI SERANGAN DDOS PADA DATASET CICDDOS2019 MENGGUNAKAN METODE CNN LONG SHORT-TERM MEMORY. Undergraduate thesis, Sriwijaya University.

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Abstract

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%.

Item Type:	Thesis (Undergraduate)
Uncontrolled Keywords:	Distributed Denial-of-Service (DDoS), Convolutional Neural Network (CNN) Long Short-Term Memory (LSTM), CICDDoS2019
Subjects:	Q Science > Q Science (General) > Q334-342 Computer science. Artificial intelligence. Algorithms. Robotics. Automation. T Technology > TA Engineering (General). Civil engineering (General) > TA1-2040 Engineering (General). Civil engineering (General) > TA158.7 Computer network resources Including the Internet
Divisions:	09-Faculty of Computer Science > 56201-Computer Systems (S1)
Depositing User:	Muhammad Aldi Pangestu
Date Deposited:	12 Aug 2025 01:56
Last Modified:	12 Aug 2025 01:56
URI:	http://repository.unsri.ac.id/id/eprint/182705

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