OPTIMASI NAIVE BAYES UNTUK KLASIFIKASI SERANGAN DDOS DENGAN GENETIC ALGORITHM

SALIM, AGUS and Rini, Dian Palupi and Stiawan, Deris (2020) OPTIMASI NAIVE BAYES UNTUK KLASIFIKASI SERANGAN DDOS DENGAN GENETIC ALGORITHM. Undergraduate thesis, Sriwijaya University.

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Abstract

DDoS is one type of internet attack that can threaten internet users, especially web application users, DDoS attacks can cause a server to malfunction on a network, which is caused by the large amount of bandwidth traffic that is launched by an attacker through DDoS. Therefore, we need a way to identify a network traffic, that is by carrying out a classification process on a network traffic data to find out whether it is an attack or not. because the amount of network traffic data is very much per second, a classification algorithm is used to overcome this problem. One classification algorithm that is able to handle a lot of data at once is the Naïve Bayes algorithm. One of the weaknesses of Naïve Bayes is the dependency of the results of accuracy generated based on many attributes used, therefore Genetic Algorithm (GA) is used as an optimization algorithm to reduce the use of attributes and increase the accuracy of the Naïve Bayes algorithm. The accuracy of Naïve Bayes was 91.55% and Naïve Bayes was optimized with Genetic Algorithm98.56% Obtained increased accuracy obtained by 5.4%

Item Type:	Thesis (Undergraduate)
Uncontrolled Keywords:	Detection of DDoS Traffic attacks. Machine Learning, Data Mining, Genetic Algorithm, Naïve Bayes
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 7377 not found.
Date Deposited:	14 Aug 2020 04:25
Last Modified:	14 Aug 2020 04:25
URI:	http://repository.unsri.ac.id/id/eprint/33061

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