OPTIMASI NAÏVE BAYES UNTUK KLASIFIKASI SERANGAN DDOS DENGAN ARTIFICIAL BEE COLONY

Published on Jul 24, 2020
Ajrul Amilin Muflih , Dian Palupi Rini4
Estimated H-index: 4
,
Deris Stiawan10
Estimated H-index: 10
Source
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 Naive Bayes method. To improve the performance of Naive 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 Naive 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 Naive Bayes is 91.55%. The accuracy of the results shows that the application of Artificial Bee Colony for the Naive Bayes classification has an effect with an increase in accuracy of 8.4%.
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