International Journal of Innovative Research in Computer and Communication Engineering
ISSN Approved Journal | Impact factor: 8.771 | ESTD: 2013 | Follows UGC CARE Journal Norms and Guidelines
| Monthly, Peer-Reviewed, Refereed, Scholarly, Multidisciplinary and Open Access Journal | High Impact Factor 8.771 (Calculated by Google Scholar and Semantic Scholar | AI-Powered Research Tool | Indexing in all Major Database & Metadata, Citation Generator | Digital Object Identifier (DOI) |
| TITLE | Android Malware Detection using Machine Learning |
|---|---|
| ABSTRACT | The increasing use of Android smartphones has resulted in a rapid rise in malicious applications. Due to the open nature of the Android platform, attackers can easily distribute malware through third-party applications. Conventional malware detection techniques are limited in identifying new and unknown threats. This paper proposes a machine learning-based Android malware detection system using static analysis. Important features such as application permissions and API calls are extracted from APK files and used for classification. The experimental results show that the proposed approach effectively detects malicious applications with high accuracy and low computational cost. |
| AUTHOR | SHRIRAKSHA A S, AYESHA KHANAM, MIZBA BANU, BIBI AYESHA BANU, BIBI AMEENA BANU Assistant Professor, Dept. of ISE, Jain Institute of Technology, Davangere, Karnataka, India UG Students, Dept. of ISE, Jain Institute of Technology, Davangere, Karnataka, India |
| VOLUME | 180 |
| DOI | DOI: 10.15680/IJIRCCE.2026.1401006 |
| pdf/6_Android Malware Detection using Machine Learning.pdf | |
| KEYWORDS | |
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