KLASIFIKASI SERANGAN UDP FLOOD PADA JARINGAN INTERNET OF THINGS (IOT) MENGGUNAKAN METODE SUPPORT VECTOR MACHINE (SVM)

ALFIAN, BUDIMAN and Stiawan, Deris (2022) KLASIFIKASI SERANGAN UDP FLOOD PADA JARINGAN INTERNET OF THINGS (IOT) MENGGUNAKAN METODE SUPPORT VECTOR MACHINE (SVM). Undergraduate thesis, Sriwijaya University.

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Abstract

The Internet of things (IoT) is a network that can connect any device to the internet. Denial of Service (DoS) attacks on IoT use reasonable service requests to gain excessive processing and network resources and prevent legitimate users from accessing them. One of the Denial of Service attacks is the User Datagram Protocol (UDP) which is a common form of Denial of Service (DoS) attack, where it works by impersonating large number of fake identities with malicious node, i.e. Internet Protocol (IP) spoofing. In this research, the classification of UDP Flood attacks will be carried out. The results of the Sigmoid Kernel Support Vector Machine classification using 80% split training data and 20% testing show an accuracy of 99.7%, a precision of 5.7%, a recall of 100%, and an F1 – score of 10.8%. For Split training data 70% and Testing 30% produce an accuracy of 99.7%, precision of 6.2%, recall of 100%, and F1 – a score of 11.8%. And for Split training data 60% and Testing 40% show results of accuracy of 99.7%, precision of 6.2%, recall of 100%, and F1 – Score of 11.8%.

Item Type:	Thesis (Undergraduate)
Uncontrolled Keywords:	Classification, Denial of Service, UDP Flood, Support Vector Machine
Subjects:	Q Science > QA Mathematics > QA75-76.95 Calculating machines > QA76.9.E94 Computer system performance. Computer Communication Networks. Computer science. Logic design. Operating systems (Computers). T Technology > TK Electrical engineering. Electronics Nuclear engineering > TK5101-6720 Telecommunication Including telegraphy, telephone, radio, radar, television > TK5105.5.S72 Computer networks
Divisions:	09-Faculty of Computer Science > 56201-Computer Systems (S1)
Depositing User:	Budiman Alfian
Date Deposited:	13 Jan 2023 08:15
Last Modified:	13 Jan 2023 08:15
URI:	http://repository.unsri.ac.id/id/eprint/86178

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