A Bayesian probability model for Android malware detection

Sharfah Ratibah, Tuan Mat and Mohd Faizal, Ab Razak and Mohd Nizam, Mohmad Kahar and Juliza, Mohamad Arif (2021) A Bayesian probability model for Android malware detection. ICT Express. pp. 1-8. ISSN 2405-9595. (In Press / Online First) (In Press / Online First)

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The unprecedented growth of mobile technology has generated an increase in malware and raised concerns over malware threats. Different approaches have been adopted to overcome the malware attacks yet this spread is still increasing. To combat this issue, this study proposes an Android malware detection system based on permission features using Bayesian classification. The permission features were extracted via the static analysis technique. The 10,000 samples for the judgement were obtained from AndroZoo and Drebin databases. The experiment was then conducted using two algorithms for feature selection: information gain and chi-square. The best accuracy rate of detection of permission features achieved was 91.1%.

Item Type: Article
Uncontrolled Keywords: Mobile phone, Android malware, Bayesian, Information gain, Chi-square
Subjects: Q Science > QA Mathematics > QA75 Electronic computers. Computer science
Faculty/Division: Institute of Postgraduate Studies
Faculty of Computing
Depositing User: Noorul Farina Arifin
Date Deposited: 05 Nov 2021 04:23
Last Modified: 05 Nov 2021 04:23
URI: http://umpir.ump.edu.my/id/eprint/32536
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