KLASIFIKASI SMS MALWARE PADA PLATFORM ANDROID DENGAN METODE SUPPORT VECTOR MACHINE

RAHARJO, AGENG and Bazhar, Iman Saladin (2023) KLASIFIKASI SMS MALWARE PADA PLATFORM ANDROID DENGAN METODE SUPPORT VECTOR MACHINE. Undergraduate thesis, Sriwijaya University.

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Abstract

Android is the most popular mobile software platform across the globe. The worldwide app downloads reached 352.9 billion in 2021. However, it still faces serious security threats due to its open-source nature. Android is susceptible to various malware variants that are packaged within APK (Android Package Kit) files and have permissions for SMS (Short Message Service). SMS is a technology used for sending text messages on Android devices. With the rapid advancement of technology, SMS is not immune to attacks or malicious activities. This type of malware can be automatically downloaded without the user's knowledge through short messages or SMS. Once downloaded, the malware can install other malicious applications on the user's device, posing a risk to privacy and personal data security. This research utilizes a dataset from CICAndMal2017 that focuses more on SMS malware, specifically the jifake and fakenotify types, using the Support Vector Machine algorithm. The classification results using the RBF kernel achieved a precision of 94.67%, recall of 94.67%, F-1 Score of 94.67%, and an accuracy of 95%. Meanwhile, the Support Vector Machine with the Polynomial kernel obtained a precision of 89.67%, recall of 90.33%, F1-Score of 89.67%, and an accuracy of 90%.

Item Type:	Thesis (Undergraduate)
Uncontrolled Keywords:	Short Message Service, SMS Malware Classification, Random Forest, Machine Learning
Subjects:	T Technology > T Technology (General) > T1-995 Technology (General)
Divisions:	09-Faculty of Computer Science > 56201-Computer Systems (S1)
Depositing User:	Ageng Raharjo
Date Deposited:	28 Jul 2023 07:46
Last Modified:	28 Jul 2023 07:46
URI:	http://repository.unsri.ac.id/id/eprint/123372

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