PENGENALAN POLA SERANGAN DENIAL OF SERVICE (UDP FLOOD) PADA JARINGAN INTERNET OF THINGS (IOT) DENGAN ALGORITMA DECISION TREE C4.5
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Institusion
Universitas Sriwijaya
Author
RIKI ANDIKA (STUDENT ID : 09011181320015)
Deris Stiawan (LECTURER ID : 0003047905)
Deris Stiawan (LECTURER ID : 0003047905)
Subject
TJ1180.I34 Machining-Machine tools-Numerical control-Computer integrated manufacturing systems-Artificial intelligence
Datestamp
2019-10-01 06:56:43
Abstract :
Recognition of attack pattern is one of the way that can be used to be able to detect attacks that occur on the network. The attacks can be identified by pattern is the denial of service (udp flood) that occurs on the internet of things, the internet of things network built in this research has seven nodes with sensor data which is then sent to the server, using the protocol and xbee protocol. Denial of service attack (upd flood) is a connectionless attack that floats so it can make the network busy with abnormal traffic. The pattern of denial service (udp flood) can be identified by several parameters such as IP time to live, IP length, UDP length and payload. In this research using the C4.5 decision tree algorithm that is used to classify normal data and attack data to obtain effective results with the final research obtained an accuracy value of 99.98% precision of 99.99% and a recall value of 100%.
Recognition of attack pattern is one of the way that can be used to be able to detect attacks that occur on the network. The attacks can be identified by pattern is the denial of service (udp flood) that occurs on the internet of things, the internet of things network built in this research has seven nodes with sensor data which is then sent to the server, using the protocol and xbee protocol. Denial of service attack (upd flood) is a connectionless attack that floats so it can make the network busy with abnormal traffic. The pattern of denial service (udp flood) can be identified by several parameters such as IP time to live, IP length, UDP length and payload. In this research using the C4.5 decision tree algorithm that is used to classify normal data and attack data to obtain effective results with the final research obtained an accuracy value of 99.98% precision of 99.99% and a recall value of 100%.
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RAMA_56201_09011181320015_0003047905_06_ref.pdf
RAMA_56201_09011181320015_0003047905_07_lamp.pdf
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