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Pakorn Leesutthipornchai. Malware classification for mobile application using permission manifest. (). King Mongkut's University of Technology North Bangkok. Central Library. : , 2024.
Malware classification for mobile application using permission manifest
Name: Warasit Changrangkarn
Organization :
Thammasat University. Faculty of Science and Technology
Email :
warasit.cha@dome.tu.ac.th
Name: Pakorn Leesutthipornchai
Organization :
Thammasat University. Faculty of Science and Technology
Email :
pakornl@tu.ac.th
LCSH:
Mobile apps
LCSH:
Computer software
Abstract:
Currently, there are a large number of mobile
phone or smartphone users. A survey by the National Statistical
Office found that about 63.8 million people aged 6 years and
older, or 94.8% of the population, amounting to 60.5 million
people, use mobile phones and may fall victim to malicious
attacks or data theft. This research presents the detection of
malware in applications on the Android operating system with
access rights on the Google Play Store by analyzing and
comparing the access rights data of applications before the
installation process. It is expected that this can identify malware
applications before users proceed with the installation.
A thousand applications data set with 63 permissions is
preprocessed and applied to five machine learning algorithms
(Naïve Bayes, k-Nearest Neighbors, Support Vectors Machine,
Multi-layer Perceptron, and Random Forest). The data set is
split to test set evaluation (10%) and 10-fold cross validation
(90%). Then the data set is oversampling to eliminate overfitting
problem. The results showed that Support Vectors Machine has
the highest accuracy metric (96% accuracy) without overfitting
problem.
King Mongkut's University of Technology North Bangkok. Central Library
Address:
BANGKOK
Email:
library@kmutnb.ac.th
Created:
2024
Modified:
2025-06-06
Issued:
2025-06-06
บทความ/Article
application/pdf
BibliograpyCitation :
In IEEE Thailand Section (IEEE Computer Society Thailand Chapter) and Prince of Songkla University. College of Computing. The 21st International Joint Conference on Computer Science and Software Engineering (JCSSE 2024)) (pp.482-487). Phuket : Prince of Songkla University
eng
©copyrights King Mongkut's University of Technology North Bangkok
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0.03303 วินาที
Warasit Changrangkarn
| Title | Contributor | Type |
|---|---|---|
| Malware classification for mobile application using permission manifest
มหาวิทยาลัยเทคโนโลยีพระจอมเกล้าพระนครเหนือ Warasit Changrangkarn;Pakorn Leesutthipornchai | บทความ/Article |
Pakorn Leesutthipornchai
| Title | Contributor | Type |
|---|---|---|
| Thai fake news detection using machine learning model
มหาวิทยาลัยเทคโนโลยีพระจอมเกล้าพระนครเหนือ Kotchakorn Tiemtud;Pakorn Leesutthipornchai | บทความ/Article | |
| Malware classification for mobile application using permission manifest
มหาวิทยาลัยเทคโนโลยีพระจอมเกล้าพระนครเหนือ Warasit Changrangkarn;Pakorn Leesutthipornchai | บทความ/Article |