Feature-Based Semi-Supervised Learning Approach to Android Malware Detection

Engineering Proceedings

article

Mariam Memon¹  Adil Ahmed Unar¹  Syed Saad Ahmed²  Ghulam Hussain Daudpoto¹  Rabeea Jaffari¹ 

[1] Software Engineering Department, Mehran University of Engineering and Technology;Computer Systems Engineering Department, Mehran University of Engineering and Technology

关键词: malware detection;    android malware;    static analysis;    machine learning;    semi-supervised learning;   

DOI  :  10.3390/engproc2023032006

来源: mdpi

【 摘 要 】

The development of signature-based methods or Machine Learning (ML) techniques on static data has dominated automated malware detection on android platforms. However, these techniques may not detect dangerous activities that only manifest during runtime. Furthermore, there is already a significant volume of unlabeled malware data available, making the production of datasets through supervised ML approach of manual labelling expensive. For anti-virus researchers, the process of malware development poses a significant engineering challenge because they lack an effective method for capturing potentially new harmful files while removing clean and well-known files. In this research, we propose a semi-supervised ML technique to detect android malware from android permissions and Application Programmer Interface (API) call logs. The ML technique is incorporated into an android application to scan the installed applications and detect the corresponding levels of maliciousness with success. The results depict the feasibility of our proposed method and application.

【 授权许可】

Unknown