SYN Flood detection via machine learning / Muhammad Muhaimin Aiman Mazlan

In the era of technology, Firewall have become an important component for protecting interconnection of the computer resource and network environment. Recently, one the most popular attack is denial of service (DoS) that attempt to be malicious pattern to compromise a server or a network resource. T...

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Bibliographic Details
Main Author:	Mazlan, Muhammad Muhaimin Aiman
Format:	Student Project
Language:	English
Published:	Faculty of Computer and Mathematical Sciences 2018
Subjects:	Instruments and machines Electronic computers. Computer science Artificial immune systems. Immunocomputers
Online Access:	http://ir.uitm.edu.my/id/eprint/21337/ http://ir.uitm.edu.my/id/eprint/21337/1/PPb_MUHAMMAD%20MUHAIMIN%20AIMAN%20MAZLAN%20M%20CS%2018_5.pdf

id	uitm-21337
recordtype	eprints
spelling	uitm-213372018-10-24T01:59:00Z http://ir.uitm.edu.my/id/eprint/21337/ SYN Flood detection via machine learning / Muhammad Muhaimin Aiman Mazlan Mazlan, Muhammad Muhaimin Aiman Instruments and machines Electronic computers. Computer science Artificial immune systems. Immunocomputers In the era of technology, Firewall have become an important component for protecting interconnection of the computer resource and network environment. Recently, one the most popular attack is denial of service (DoS) that attempt to be malicious pattern to compromise a server or a network resource. The current problem and issue regarding of existing project is cannot handle the attack by shutdown the connection between inbound and outbound network. Therefore, the aim of this project is to develop a firewall software called “FIREARMS” that can prevent one type of DDoS which is SYN-Flood attack. The core detection and prevention algorithm which is the support vector machine (SVM) were implemented in this project. The software will be trained by using NSL KDD Cup dataset in order to make it learns about the Neptune attack and as a result, it will be able to detect and prevent such attack. The significant of this project is it can be detect any of the SYN-Flood attack accurately and avoid many of false alarm rate. In a conclusion, the software would be able to prevent the computer from SYN-Flood attack by using FIREARMS software. This will help user to secure their network during the connection of their computer to the internet. Faculty of Computer and Mathematical Sciences 2018 Student Project NonPeerReviewed text en http://ir.uitm.edu.my/id/eprint/21337/1/PPb_MUHAMMAD%20MUHAIMIN%20AIMAN%20MAZLAN%20M%20CS%2018_5.pdf Mazlan, Muhammad Muhaimin Aiman (2018) SYN Flood detection via machine learning / Muhammad Muhaimin Aiman Mazlan. [Student Project] (Unpublished)
repository_type	Digital Repository
institution_category	Local University
institution	Universiti Teknologi MARA
building	UiTM Institutional Repository
collection	Online Access
language	English
topic	Instruments and machines Electronic computers. Computer science Artificial immune systems. Immunocomputers
spellingShingle	Instruments and machines Electronic computers. Computer science Artificial immune systems. Immunocomputers Mazlan, Muhammad Muhaimin Aiman SYN Flood detection via machine learning / Muhammad Muhaimin Aiman Mazlan
description	In the era of technology, Firewall have become an important component for protecting interconnection of the computer resource and network environment. Recently, one the most popular attack is denial of service (DoS) that attempt to be malicious pattern to compromise a server or a network resource. The current problem and issue regarding of existing project is cannot handle the attack by shutdown the connection between inbound and outbound network. Therefore, the aim of this project is to develop a firewall software called “FIREARMS” that can prevent one type of DDoS which is SYN-Flood attack. The core detection and prevention algorithm which is the support vector machine (SVM) were implemented in this project. The software will be trained by using NSL KDD Cup dataset in order to make it learns about the Neptune attack and as a result, it will be able to detect and prevent such attack. The significant of this project is it can be detect any of the SYN-Flood attack accurately and avoid many of false alarm rate. In a conclusion, the software would be able to prevent the computer from SYN-Flood attack by using FIREARMS software. This will help user to secure their network during the connection of their computer to the internet.
format	Student Project
author	Mazlan, Muhammad Muhaimin Aiman
author_facet	Mazlan, Muhammad Muhaimin Aiman
author_sort	Mazlan, Muhammad Muhaimin Aiman
title	SYN Flood detection via machine learning / Muhammad Muhaimin Aiman Mazlan
title_short	SYN Flood detection via machine learning / Muhammad Muhaimin Aiman Mazlan
title_full	SYN Flood detection via machine learning / Muhammad Muhaimin Aiman Mazlan
title_fullStr	SYN Flood detection via machine learning / Muhammad Muhaimin Aiman Mazlan
title_full_unstemmed	SYN Flood detection via machine learning / Muhammad Muhaimin Aiman Mazlan
title_sort	syn flood detection via machine learning / muhammad muhaimin aiman mazlan
publisher	Faculty of Computer and Mathematical Sciences
publishDate	2018
url	http://ir.uitm.edu.my/id/eprint/21337/ http://ir.uitm.edu.my/id/eprint/21337/1/PPb_MUHAMMAD%20MUHAIMIN%20AIMAN%20MAZLAN%20M%20CS%2018_5.pdf
first_indexed	2023-09-18T23:06:21Z
last_indexed	2023-09-18T23:06:21Z
_version_	1777418509002735616

SYN Flood detection via machine learning / Muhammad Muhaimin Aiman Mazlan

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