Developing secured android applications by mitigating code vulnerabilities with machine learning.

Senanayake, Janaka; Kalutarage, Harsha; Al-Kadri, Mhd Omar; Petrovski, Andrei; Piras, Luca

Abstract

Mobile application developers sometimes might not be serious about source code security and publish apps to the marketplaces. Therefore, it is essential to have a fully automated security solutions generator to integrate security-by-design into the development practices, especially for the Android platform. This research proposes a Machine Learning (ML) based highly accurate method to detect Android source code vulnerabilities. A new labelled dataset containing Android source code vulnerability samples was generated initially. The dataset was used to train binary and multi-class classification based ML models, to identify code issues by following a static analysis approach. The proposed model can detect code vulnerabilities with a 0.90 F1-Score and vulnerability categories (CWE) with a 0.96 F1-Score. By integrating this with the Android development environment, app developers can analyse source code and identify security vulnerabilities in real-time. The proposed framework can be extended to suggest suitable patches to overcome the source code issues by providing real-time fixes in future.

Citation

SENANAYAKE, J., KALUTARAGE, H., AL-KADRI, M.O., PETROVSKI, A. and PIRAS, L. 2022. Developing secured android applications by mitigating code vulnerabilities with machine learning. In ASIA CCS '22: proceedings of the 17th ACM (Association for Computing Machinery) Asia conference on computer and communications security 2022 (ASIA CCS 2022), 30 May - 3 June 2022, Nagasaki, Japan. New York: ACM [online], pages 1255-1257. Available from: https://doi.org/10.1145/3488932.3527290

• Presentation Conference Type: Conference Paper (published)
• Conference Name: 17th Asia Conference on computer and communications security 2022 (ASIA CCS 2022)
• Start Date: May 30, 2022
• End Date: Jun 2, 2022
• Acceptance Date: Feb 7, 2022
• Online Publication Date: May 30, 2022
• Publication Date: May 30, 2022
• Deposit Date: Jul 29, 2022
• Publicly Available Date: Jul 29, 2022
• Publisher: Association for Computing Machinery (ACM)
• Peer Reviewed: Peer Reviewed
• Pages: 1255-1257
• Book Title: ASIA CCS '22: proceedings of the 17th Asia Conference on computer and communications security 2022 (ASIA CCS 2022)
• ISBN: 9781450391405
• DOI: https://doi.org/10.1145/3488932.3527290
• Keywords: Android; Code vulnerability detection; Static analysis; Vulnerability dataset; Machine learning; Secure mobile apps
• Public URL: https://rgu-repository.worktribe.com/output/1713062