SISTEM KLASIFIKASI ANDROID RANSOMWARE BERBASIS DEEP NEURAL NETWORK (DNN)

AFIFAH, NURUL and Stiawan, Deris and Nurmaini, Siti (2019) SISTEM KLASIFIKASI ANDROID RANSOMWARE BERBASIS DEEP NEURAL NETWORK (DNN). Master thesis, Sriwijaya University.

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Abstract

Currently there are tons of applications available on the Google Play Store. But some of these applications have been infiltrated by malware files. On Android, each program is contained in apk file, which contains all program code, resources, assets, certificates, and manifest files. Users can download applications from any website and copy the apk file to the device, which further increases the popularity of Android. In open the network still provides many possibilities for the attacker. For example, spread via email and phishing sites. This is the root of the problem if the application that has been infiltrated by the malware file has been installed on the Android user's device, then automatically the file on the Android device can be blocked by malware. The type of malware that is very dangerous and growing rapidly is the type of ransomware. Given their rapid growth, there is an urgent need to develop an effective countermeasure solution by classifying the ransomware using the DNN algorithm and optimizing it by initializing the weights of Xavier and He. The results were significant, with a score of 99.96% for training and testing accuracy, 99.95% for precision, 99.99% for sensitivity, 99.07% for specificity and 99.97% for F1-score. The greater the value obtained from the test results, the better the performance in the testing system.

Item Type:	Thesis (Master)
Uncontrolled Keywords:	Ransomware, DNN, Xavier, He, Android
Subjects:	T Technology > T Technology (General) > T57.6-57.97 Operations research. Systems analysis
Divisions:	09-Faculty of Computer Science > 55101-Informatics (S2)
Depositing User:	Users 3511 not found.
Date Deposited:	04 Dec 2019 02:42
Last Modified:	04 Dec 2019 02:42
URI:	http://repository.unsri.ac.id/id/eprint/19791

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