OPTIMASI NAIVE BAYES UNTUK KLASIFIKASI SERANGAN DDOS DENGAN PARTICLE SWARM OPTIMIZATION

NURWINTO, COKRO and Rini, Dian Palupi and Stiawan, Deris (2020) OPTIMASI NAIVE BAYES UNTUK KLASIFIKASI SERANGAN DDOS DENGAN PARTICLE SWARM OPTIMIZATION. Undergraduate thesis, Sriwijaya University.

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Abstract

DDoS is one of dangerous internet / cyber attacks. One of solution for overcoming the problem with identifying a traffic network wheter if it contains DDoS attack or not. Identification requires a lot of data for recognizing the pattern of DDoS attack so that it can be prevented as soon as possible. But, the traffic network it self has contain a lot of data per seconds. Therefore, a classification algorithm that can process a lot of data at one time is required to solve the problem, one of them is Naïve Bayes algorithm. One of Naïve Bayes weaknesses is its accuracy that depends on how many attributes of the data being used. Therefore, the Particle Swarm Optimization algorithm is used as attributes reduction algorithm, and also enhance Naïve Bayes’s accuracy. The accuracy result of Naïve Bayes is 91,55% and Naïve Bayes optimized with Particle Swarm Optimization is 99,13%, resulting accuracy enhancement by 7,58%. Keywords : Naïve Bayes, Particle Swarm Optimzation, DDoS, Classification.

Item Type: Thesis (Undergraduate)
Uncontrolled Keywords: Naïve Bayes, Particle Swarm Optimzation, DDoS
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 7379 not found.
Date Deposited: 14 Aug 2020 04:28
Last Modified: 14 Aug 2020 04:28
URI: http://repository.unsri.ac.id/id/eprint/33053

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