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ANALISA SISTEM IDENTIFIKASI DISTRIBUTED DENIAL OF SERVICE MENGGUNAKAN K-NEAREST NEIGHBORS PADA SOFTWARE DEFINED NETWORK

Azis, Muhammad Misbahul (2019) ANALISA SISTEM IDENTIFIKASI DISTRIBUTED DENIAL OF SERVICE MENGGUNAKAN K-NEAREST NEIGHBORS PADA SOFTWARE DEFINED NETWORK. Bachelors Degree (S1) thesis, University of Muhammadiyah Malang.

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Abstract

The need for networks to prioritize performance to support a network efficiency is important. Determination of more and more complex configurations with increasingly complex networks, makes networks easier and harder to apply to a large network topology. Software Defined Network (SDN) appears with a solution that can solve the problem. Basically the concept of Software Defined Network is determined by the controller and the data plane/forwarding, so that it is able to manage so many networks in the controller. But the controller does not have sufficient security to protect against network attacks such as DDoS, SYN Flooding Attack so that the controller will be the target of the attacker. This study approved the application of machine learning applications on the controller to approve attacks such as DDoS and SYN Flooding Attack. In this study the controller used is the Ryu controller. The Ryu controller uses the python programming language. In this study using a linear topology on Mininet and create a packet .pcap format for testing attacks carried out. It can be accepted that the average number of incoming and outgoing packages at the same time is successful in mitigating packages that are considered DDoS.

Item Type: Thesis (Bachelors Degree (S1))
Student ID: 201510370311192
Thesis Advisors: Yufis Azhar (0728088701), Syaifuddin (0716118701)
Keywords: Software Defined Network, Ryu Controller, K-Nearest Neighbors, Mininet, SYN-Flooding Attack
Subjects: T Technology > T Technology (General)
Divisions: Faculty of Engineering > Department of Informatics (55201)
Depositing User: 201510370311192
Date Deposited: 06 Nov 2019 06:58
Last Modified: 06 Nov 2019 06:58
URI : http://eprints.umm.ac.id/id/eprint/54811

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