PACKET THRESHOLD ALGORITHM COUPLED WITH MACHINE LEARNING FOR DDoS CLASSIFICATION ATTACKS

MOHD YUSOF, MOHD AZAHARI and ABD SAMAD, NOR HAFIZA and ADNAN, RUKHIYAH PACKET THRESHOLD ALGORITHM COUPLED WITH MACHINE LEARNING FOR DDoS CLASSIFICATION ATTACKS. The Asian Journal of Professional and Business Studies, 2 (2).

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Abstract

Today, DDoS attacks are the most common Internet threats. DDoS attacks are generated by attackers from anywhere to disable a company's servers from being accessed by users worldwide. An attacker can easily launch one or more types of DDoS attacks at a time. DDoS attacks that can be generated by attackers include Slowloris, UDP flood, Smurf, HTTP flood, TCP SYN flood and more. Therefore, we have proposed a technique called the Packet Threshold Algorithm (PTA) in this paper, where it is combined with several machine learning to classify normal packet and DDoS attacks, namely UDP flood, Smurf, TCP SYN flood and Ping of Death. There are four machine learning, which are K-Nearest Neighbor (KNN), Naïve Bayes, Logistic Regression and Support Vector Machine (SVM) combined with the Packet Threshold Algorithm (PTA) to reduce false positive rate to obtain high detection accuracy. Among the four combinations of techniques, PTA-KNN has been considered as the best technique in the context of reduction of false positive rate. The determination of this best technique is based on the PTA-KNN has achieved the highest detection accuracy (99.83%) compared to the other three techniques with only 0.02% false positive rate. The determination of this best technique is based on the PTA-KNN has achieved the highest detection accuracy (99.83%) compared to the other three techniques with only 0.02% false positive rate.

Item Type:	Article
Uncontrolled Keywords:	DDoS attack, False positive rate, Detection accuracy, Machine learning
Subjects:	L Education > L Education (General) L Education > LC Special aspects of education L Education > LC Special aspects of education > LC5201 Education extension. Adult education. Continuing education
Divisions:	Faculty of Business, Accountancy and Social Sciences
Depositing User:	Mrs Shariffah Shuhaiza Syed Mohd Nor
Date Deposited:	18 Jun 2021 04:35
Last Modified:	17 Aug 2021 04:43
URI:	http://eprints.kuptm.edu.my/id/eprint/1785

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