Mohd Yusof, Mohd Azahari and Mohd Safar, Noor Zuraidin and Abdullah, Zubaile and Hamid Ali, Firkhan Ali and Mohamad Sukri, Khairul Amin and Jofri, Muhamad Hanif and Mohamed, Juliana and Omar, Abdul Halim and Bahrudin, Ida Aryanie and Mohamed Ali @ Md Hani, Mohd Hatta (2024) DDoS Classification using Combined Techniques. International Journal of Advanced Computer Science and Applications, 15 (1). pp. 551-557.
![[img]](http://eprints.uthm.edu.my/style/images/fileicons/text.png) |
Text
J17424_0b14450bdb1b1d7104fe305c68705989.pdf Restricted to Registered users only Download (1MB) | Request a copy |
Abstract
Now-a-days, the attacker's favourite is to disrupt a network system. An attacker has the capability to generate various types of DDoS attacks simultaneously, including the Smurf attack, ICMP flood, UDP flood, and TCP SYN flood. This DDoS issue encouraged the design of a classification technique against DDoS attacks that enter a computer network environment. The technique is called Packet Threshold Algorithm (PTA) and is combined with several machine learning to classify incoming packets that have been captured and recorded. Apart from that, the combination of techniques can differentiate between normal packets and DDoS attacks. The performance of all techniques in the research achieved high detection accuracy while mitigating the issue of a high false positive rate. The four techniques focused in this research are PTA-SVM, PTA-NB, PTA-LR and PTA-KNN. Based on the results of detection accuracy and false positive rate for all the techniques involved, it proves the PTA-KNN technique is a more effective technique in the context of detection of incoming packets whether DDoS attacks or normal packets
| Item Type: | Article |
|---|---|
| Uncontrolled Keywords: | DDoS; machine learning; accuracy; false positive rate |
| Subjects: | T Technology > T Technology (General) |
| Divisions: | Faculty of Computer Science and Information Technology > FSKTM |
| Depositing User: | Mr. Mohamad Zulkhibri Rahmad |
| Date Deposited: | 13 May 2024 11:51 |
| Last Modified: | 13 May 2024 11:51 |
| URI: | http://eprints.uthm.edu.my/id/eprint/10936 |
Actions (login required)
 |
View Item |