Che Akmal, Che Yahaya and Ahmad Firdaus, Zainal Abidin and Salwana, Mohamad and Ernawan, Ferda and Mohd Faizal, Ab Razak (2020) Automated feature selection using boruta algorithm to detect mobile malware. International Journal of Advanced Trends in Computer Science and Engineering, 9 (5). pp. 9029-9036. ISSN 2278-3091. (Published)
|
Pdf
Automated Feature Selection using Boruta Algorithm to Detect Mobile Malware.pdf Download (455kB) | Preview |
Abstract
The usage of android system is rapidly growing in mobile devices. Android system might also incur severe different malware dangers and security threats such as infections, root exploit, Trojan, and worms. The malware has potential to compromise and steal the private data, classified data, instant messages, private business contacts, and confidential schedule. Malware detection is needed due to the malware continuously evolve rapidly. This research proposed automated feature selection using Boruta algorithm to detect the malware. The proposed method adopts machine learning prediction and optimizes the selecting features in order to reduce the model of machine learning complexity. Boruta algorithm is used to select features automatically for assisting the machine learning. The experimental results show that the proposed method is able to reach 99.73% accuracy in machine learning classification.
| Item Type: | Article |
|---|---|
| Uncontrolled Keywords: | Android; static analysis; machine learning; features. |
| Subjects: | Q Science > QA Mathematics > QA75 Electronic computers. Computer science |
| Faculty/Division: | Institute of Postgraduate Studies Faculty of Computing |
| Depositing User: | Miss Amelia Binti Hasan |
| Date Deposited: | 12 Dec 2024 00:40 |
| Last Modified: | 12 Dec 2024 00:40 |
| URI: | http://umpir.ump.edu.my/id/eprint/43141 |
| Download Statistic: | View Download Statistics |
Actions (login required)
 |
View Item |