DETEKSI SERANGAN TROJAN METASPLOIT PADA ANDROID DENGAN METODE SUPPORT VECTOR MACHINE (SVM)

ILDIANSYAH, M. AFIF and Stiawan, Deris (2024) DETEKSI SERANGAN TROJAN METASPLOIT PADA ANDROID DENGAN METODE SUPPORT VECTOR MACHINE (SVM). Undergraduate thesis, Sriwijaya University.

	Text RAMA_56201_09011182025022.pdf - Accepted Version Restricted to Repository staff only Available under License Creative Commons Public Domain Dedication. Download (1MB) | Request a copy
	Text RAMA_56201_09011182025022_TURNITIN.pdf - Accepted Version Restricted to Repository staff only Available under License Creative Commons Public Domain Dedication. Download (2MB) | Request a copy
	Text RAMA_56201_09011182025022_0003047905_01_front_ref.pdf - Accepted Version Available under License Creative Commons Public Domain Dedication. Download (370kB)
	Text RAMA_56201_09011182025022_0003047905_02.pdf - Accepted Version Restricted to Repository staff only Available under License Creative Commons Public Domain Dedication. Download (148kB) | Request a copy
	Text RAMA_56201_09011182025022_0003047905_03.pdf - Accepted Version Restricted to Repository staff only Available under License Creative Commons Public Domain Dedication. Download (265kB) | Request a copy
	Text RAMA_56201_09011182025022_0003047905_04.pdf - Accepted Version Restricted to Repository staff only Available under License Creative Commons Public Domain Dedication. Download (338kB) | Request a copy
	Text RAMA_56201_09011182025022_0003047905_05.pdf - Accepted Version Restricted to Repository staff only Available under License Creative Commons Public Domain Dedication. Download (33kB) | Request a copy
	Text RAMA_56201_09011182025022_0003047905_06_ref.pdf - Bibliography Restricted to Repository staff only Available under License Creative Commons Public Domain Dedication. Download (110kB) | Request a copy
	Text RAMA_56201_09011182025022_0003047905_07_lamp.pdf - Accepted Version Restricted to Repository staff only Available under License Creative Commons Public Domain Dedication. Download (181kB) | Request a copy

Abstract

Support Vector Machine (SVM) is a machine learning algorithm that falls into the category of supervised learning. SVM works by constructing an optimal hyperplane that separates two classes in a feature space. This hyperplane is chosen in such a way that it has a maximum margin, which is the largest distance between data points from both classes. In addition to the hyperplane, hyperparameters are also one of the things that need to be considered in this method. The dataset comes from the COMNETS research experiment. Support Vector Machine successfully classifies malware and benign network traffic. With model evaluation using the Confusion Matrix and hyperparameter optimization using the Gridsearch CV method, the model is able to provide good detection performance by obtaining an accuracy of 89.37%.

Item Type:	Thesis (Undergraduate)
Uncontrolled Keywords:	Support Vector Machine, Metasploit
Subjects:	Q Science > Q Science (General) > Q300-390 Cybernetics > Q325.5 Machine learning Q Science > Q Science (General) > Q334-342 Computer science. Artificial intelligence. Algorithms. Robotics. Automation. T Technology > T Technology (General) > T1-995 Technology (General)
Divisions:	09-Faculty of Computer Science > 56201-Computer Systems (S1)
Depositing User:	M. Afif Ildiansyah
Date Deposited:	20 Aug 2024 01:53
Last Modified:	20 Aug 2024 01:53
URI:	http://repository.unsri.ac.id/id/eprint/155713

Actions (login required)

View Item