PERBANDINGAN METODE SELEKSI FITUR PADA SISTEM KLASIFIKASI BOTNET IoT MENGGUNKAN ALGORITMA RANDOM FOREST

QURAHMAN, M. TAUFIQ and Stiawan, Deris (2021) PERBANDINGAN METODE SELEKSI FITUR PADA SISTEM KLASIFIKASI BOTNET IoT MENGGUNKAN ALGORITMA RANDOM FOREST. Undergraduate thesis, Sriwijaya University.

	Text RAMA_56201_09011381722092.pdf - Accepted Version Restricted to Repository staff only Available under License Creative Commons Public Domain Dedication. Download (7MB) | Request a copy
	Text RAMA_56201_09011381722092_TURNITIN.pdf - Accepted Version Restricted to Repository staff only Available under License Creative Commons Public Domain Dedication. Download (14MB) | Request a copy
Preview	Text RAMA_56201_09011381722092_0003047905_01_front_ref.pdf - Accepted Version Available under License Creative Commons Public Domain Dedication. Download (1MB) | Preview
	Text RAMA_56201_09011381722092_0003047905_02.pdf - Accepted Version Restricted to Repository staff only Available under License Creative Commons Public Domain Dedication. Download (902kB) | Request a copy
	Text RAMA_56201_09011381722092_0003047905_03.pdf - Accepted Version Restricted to Repository staff only Available under License Creative Commons Public Domain Dedication. Download (1MB) | Request a copy
	Text RAMA_56201_09011381722092_0003047905_04.pdf - Accepted Version Restricted to Repository staff only Available under License Creative Commons Public Domain Dedication. Download (2MB) | Request a copy
	Text RAMA_56201_09011381722092_0003047905_05.pdf - Accepted Version Restricted to Repository staff only Available under License Creative Commons Public Domain Dedication. Download (532kB) | Request a copy
	Text RAMA_56201_09011381722092_0003047905_06_ref.pdf - Bibliography Restricted to Repository staff only Available under License Creative Commons Public Domain Dedication. Download (620kB) | Request a copy
	Text RAMA_56201_09011381722092_0003047905_07_lamp.pdf - Accepted Version Restricted to Repository staff only Available under License Creative Commons Public Domain Dedication. Download (3MB) | Request a copy

Abstract

Botnet attacks are one of the most serious threats of many threats in the rapid development of Internet of Things (IoT) devices. The more complex IoT devices make the detection or classifying time of attacks longer and consume a lot of memory. This study used MedBIoT datasets from Tallinn University Of Technology. Extra trees feature selection method and correlation feature selection are applied to select the best features. In addition, the random forest algorithm is also applied to the classification process. Classification results using selected features are able to obtain excellent levels of accuracy, sensitivity, specificity, precision, and F1 scores with faster processing times and with relatively low levels of misclassification.

Item Type:	Thesis (Undergraduate)
Uncontrolled Keywords:	Internet of Things, Botnet Classification, Feature Selection, Random Forest, Machine Learning,
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 13661 not found.
Date Deposited:	04 Aug 2021 06:31
Last Modified:	04 Aug 2021 06:31
URI:	http://repository.unsri.ac.id/id/eprint/51127

Actions (login required)

View Item