OPTIMASI NAÏVE BAYES UNTUK KLASIFIKASI SERANGAN DDOS DENGAN ARTIFICIAL BEE COLONY

MUFLIH, AJRUL AMILIN and Rini, Dian Palupi and Stiawan, Deris (2020) OPTIMASI NAÏVE BAYES UNTUK KLASIFIKASI SERANGAN DDOS DENGAN ARTIFICIAL BEE COLONY. Undergraduate thesis, Sriwijaya University.

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Abstract

Distributed Denial of Service (DDoS) is one of the most powerful threat on the internet. DDoS attacks target websites and online services. Taking knowledge from DDoS attack data with machine learning to learn the pattern is very important to prevent and handle such attacks, one of them is by classification using the Naïve Bayes method. To improve the performance of Naïve Bayes results, it can be done by combining them using Artificial Bee Colony during the classification process for selecting DDoS attack attribute data to be used. This study examines the effect of Naïve Bayes optimization for DDoS attack classification using Artificial Bee Colony. The accuracy of DDoS attack classification with the highest NBABC is 99.95% and only Naïve Bayes is 91.55%. The accuracy of the results shows that the application of Artificial Bee Colony for the Naïve Bayes classification has an effect with an increase in accuracy of 8.4%.

Item Type:	Thesis (Undergraduate)
Uncontrolled Keywords:	DDoS, Naïve Bayes, Artificial Bee Colony
Subjects:	Q Science > Q Science (General) > Q300-390 Cybernetics > Q325.5 Machine learning
Divisions:	09-Faculty of Computer Science > 55201-Informatics (S1)
Depositing User:	Users 7378 not found.
Date Deposited:	14 Aug 2020 04:31
Last Modified:	14 Aug 2020 04:31
URI:	http://repository.unsri.ac.id/id/eprint/33054

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