DDoS attack detection system using Artificial Neural Network (ANN) / Muhammad Syamil Yusri

DDoS attacks represent a substantial danger to network availability and security. Because of their sophistication, traditional DDoS detection technologies sometimes struggle to effectively identify and neutralise such attacks. In this project, offer a novel method for detecting DDoS attacks using Ar...

Full description

Saved in:

Bibliographic Details
Main Author:	Yusri, Muhammad Syamil
Format:	Thesis
Language:	English
Published:	2024
Subjects:	Neural networks (Computer science)
Online Access:	https://ir.uitm.edu.my/id/eprint/95729/1/95729.pdf
Tags:	Add Tag No Tags, Be the first to tag this record!

id	my-uitm-ir.95729
record_format	uketd_dc
spelling	my-uitm-ir.957292024-05-23T03:20:56Z DDoS attack detection system using Artificial Neural Network (ANN) / Muhammad Syamil Yusri 2024 Yusri, Muhammad Syamil Neural networks (Computer science) DDoS attacks represent a substantial danger to network availability and security. Because of their sophistication, traditional DDoS detection technologies sometimes struggle to effectively identify and neutralise such attacks. In this project, offer a novel method for detecting DDoS attacks using Artificial Neural Networks (ANNs). To detect aberrant network traffic associated with DDoS attacks, the system uses the capability of ANNs, which are capable of learning complicated patterns and generating accurate predictions. The suggested system is divided into two stages: training and detection. A systematic strategy is used in the study framework, which includes problem identification, data gathering, preprocessing, ANN implementation, and performance assessment. The project's goal is to improve network security by identifying and categorising DDoS attacks properly. The implementation of ANNs using the Sequential model was tested using different training/testing splits (80/20, 70/30, 60/40) and epochs. The 70/30 split regularly outperformed others, with an accuracy rate of 92.29%. Detailed parameter tweaking was carried out for the 80/20, 70/30, and 60/40 splits, finding that a three-hidden-layer architecture with 256, 128, and 8 neurons produced the highest accuracy of 92.54%. Multiple ANN models were evaluated, and the best-performing model achieved 92.54% accuracy, 93.01% precision, 91.05% recall, and a 92.10% F1-score. However, the research recognises several limits, such as the time-consuming nature of ANN model training, possible scaling concerns due to hardware restrictions, and fluctuation in dataset appropriateness. To overcome these constraints, the project propose future recommendations like as using advanced techniques like parallel processing, automated methods for dynamic dataset selection, and hybrid approaches that combine ANN with other methods to increase accuracy. 2024 Thesis https://ir.uitm.edu.my/id/eprint/95729/ https://ir.uitm.edu.my/id/eprint/95729/1/95729.pdf text en public degree Universiti Teknologi MARA, Terengganu College of Computing, Informatics and Media Abdul Latif, Mohd Hanapi
institution	Universiti Teknologi MARA
collection	UiTM Institutional Repository
language	English
advisor	Abdul Latif, Mohd Hanapi
topic	Neural networks (Computer science)
spellingShingle	Neural networks (Computer science) Yusri, Muhammad Syamil DDoS attack detection system using Artificial Neural Network (ANN) / Muhammad Syamil Yusri
description	DDoS attacks represent a substantial danger to network availability and security. Because of their sophistication, traditional DDoS detection technologies sometimes struggle to effectively identify and neutralise such attacks. In this project, offer a novel method for detecting DDoS attacks using Artificial Neural Networks (ANNs). To detect aberrant network traffic associated with DDoS attacks, the system uses the capability of ANNs, which are capable of learning complicated patterns and generating accurate predictions. The suggested system is divided into two stages: training and detection. A systematic strategy is used in the study framework, which includes problem identification, data gathering, preprocessing, ANN implementation, and performance assessment. The project's goal is to improve network security by identifying and categorising DDoS attacks properly. The implementation of ANNs using the Sequential model was tested using different training/testing splits (80/20, 70/30, 60/40) and epochs. The 70/30 split regularly outperformed others, with an accuracy rate of 92.29%. Detailed parameter tweaking was carried out for the 80/20, 70/30, and 60/40 splits, finding that a three-hidden-layer architecture with 256, 128, and 8 neurons produced the highest accuracy of 92.54%. Multiple ANN models were evaluated, and the best-performing model achieved 92.54% accuracy, 93.01% precision, 91.05% recall, and a 92.10% F1-score. However, the research recognises several limits, such as the time-consuming nature of ANN model training, possible scaling concerns due to hardware restrictions, and fluctuation in dataset appropriateness. To overcome these constraints, the project propose future recommendations like as using advanced techniques like parallel processing, automated methods for dynamic dataset selection, and hybrid approaches that combine ANN with other methods to increase accuracy.
format	Thesis
qualification_level	Bachelor degree
author	Yusri, Muhammad Syamil
author_facet	Yusri, Muhammad Syamil
author_sort	Yusri, Muhammad Syamil
title	DDoS attack detection system using Artificial Neural Network (ANN) / Muhammad Syamil Yusri
title_short	DDoS attack detection system using Artificial Neural Network (ANN) / Muhammad Syamil Yusri
title_full	DDoS attack detection system using Artificial Neural Network (ANN) / Muhammad Syamil Yusri
title_fullStr	DDoS attack detection system using Artificial Neural Network (ANN) / Muhammad Syamil Yusri
title_full_unstemmed	DDoS attack detection system using Artificial Neural Network (ANN) / Muhammad Syamil Yusri
title_sort	ddos attack detection system using artificial neural network (ann) / muhammad syamil yusri
granting_institution	Universiti Teknologi MARA, Terengganu
granting_department	College of Computing, Informatics and Media
publishDate	2024
url	https://ir.uitm.edu.my/id/eprint/95729/1/95729.pdf
_version_	1804889972981891072

DDoS attack detection system using Artificial Neural Network (ANN) / Muhammad Syamil Yusri

Similar Items