SYN Flood detection via machine learning / Muhammad Muhaimin Aiman Mazlan - Mazlan, Muhammad Muhaimin Aiman (Faculty of Computer and Mathematical Sciences, 2018)
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.