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 | Malware Detection using Machine Learning |
|---|---|
| ABSTRACT | Malware continues to pose a significant threat to digital systems, especially Android environments where APK files are commonly installed. Traditional antivirus solutions rely heavily on signature-based detection, which often fails to identify newly emerging or obfuscated malware variants. To overcome these limitations, this project presents a Machine Learning–based Malware Detection System capable of analysing multiple file formats—including APK, EXE, CSV, TXT, and JSON—to accurately detect malicious patterns. The system extracts static features from uploaded files and uses a trained classification model to determine whether a file is benign or malicious. Additionally, for malicious samples, the system identifies the specific malware family to provide deeper insight into its behavioural characteristics. A redesigned, interactive web interface with ten interconnected pages enhances usability and navigation. Beyond detection, the platform provides users with prevention guidelines, recommended antivirus tools, and suggested usage durations tailored to the identified malware family. It also displays the model’s accuracy and classification results through a clear, user-friendly dashboard. Overall, this project delivers an efficient, scalable, and interactive approach to malware analysis, serving as a practical cybersecurity tool for both end-users and researchers. |
| AUTHOR | PROF. POORNIMA R D, C S MANOJ, HRUSHIKESHT K, MARUTI, MANOJ M |
| VOLUME | 176 |
| DOI | DOI: 10.15680/IJIRCCE.2025.1311109 |
| pdf/109_Malware Detection using Machine Learning.pdf | |
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