DETEKSI SMURF DDOS PADA JARINGAN SOFTWARE DEFINED NETWORK MENGGUNAKAN METODE NAIVE BAYES

RIZKI, SYUKRAN and Stiawan, Deris and Heryanto, Ahmad (2019) DETEKSI SMURF DDOS PADA JARINGAN SOFTWARE DEFINED NETWORK MENGGUNAKAN METODE NAIVE BAYES. Undergraduate thesis, Sriwijaya University.

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Abstract

In this study, the authors detected using the Naïve Bayes method for distributed smurf attack (DDoS) on a software defined network (SDN) implemented in mininet simulations. Smurf Attack is a dos attack by sending an icmp request packet without a reply from the server. In detecting, it can recognize unique attributes that are considered as attack patterns from smurf ddos such as frame length, icmp type, and icmp identifier. The attack pattern is used as a detecting parameter in detecting using the Naïve Bayes method. The scenario uses six hosts, three hosts as attackers, two hosts as clients and one host as a server running http services. The results of tests that have been carried out by detecting using the Naïve Bayes method will be compared with the Snort Intrusion detection System, the Naïve Bayes method has a 99.96% accuracy presentation while the IDS snort has an accuracy of 99.99%.

Item Type:	Thesis (Undergraduate)
Uncontrolled Keywords:	Intrusion Detection System, Software Defined Network, Smurf Attack, Naive Bayes
Subjects:	Q Science > Q Science (General) > Q334-342 Computer science. Artificial intelligence. Algorithms. Robotics. Automation.
Divisions:	09-Faculty of Computer Science > 56201-Computer Systems (S1)
Depositing User:	Users 3950 not found.
Date Deposited:	07 Jan 2020 06:51
Last Modified:	07 Jan 2020 06:51
URI:	http://repository.unsri.ac.id/id/eprint/23208

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