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 | Machine Learning based Hybrid Approach for Prediction Model of Android Malware |
|---|---|
| ABSTRACT | Android malware continues to evolve in sophistication, rendering traditional signature-based detection methods increasingly inadequate for identifying novel and obfuscated threats. This study proposes a hybrid machine learning approach that combines Support Vector Machines (SVM) and Multi-Layer Perceptrons (MLP) to construct a robust prediction model for Android malware detection. The outputs of both classifiers are integrated using an ensemble or stacking strategy, where predictions are fused to improve overall classification accuracy, reduce false positive rates, and enhance detection of previously unseen malware variants. Experimental evaluation on benchmark Android malware datasets demonstrates that the proposed SVM-MLP hybrid model achieves superior performance in terms of accuracy, precision, recall, and F1-score compared to standalone SVM or MLP classifiers, underscoring the potential of hybrid machine learning architectures for building more resilient and adaptive Android malware prediction systems. |
| AUTHOR | RAJ SAGAR KUMAR, DR. RITESH KUMAR YADAV M. Tech Scholar, Department of CSE, Sarvepalli Radhakrishnan University, Bhopal, India Professor, Department of CSE, Sarvepalli Radhakrishnan University, Bhopal, India |
| VOLUME | 187 |
| DOI | DOI: 10.15680/IJIRCCE.2026.1408015 |
| pdf/15_Machine Learning based Hybrid Approach for Prediction Model of Android Malware.pdf | |
| KEYWORDS | |
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