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 | Ensemble Learning for Enhanced Malware Detection |
|---|---|
| ABSTRACT | Malware has become increasingly sophisticated, making traditional signature-based detection insufficient for identifying new and obfuscated threats. This paper presents an ensemble learning-based malware detection system using Bagging (Random Forest), Boosting (AdaBoost and Gradient Boosting), and Stacking techniques. The dataset contains 100,000 Android application samples equally divided into malware and benign classes. Data preprocessing includes label encoding, feature scaling, and removal of irrelevant attributes. Experimental results demonstrate that ensemble learning improves accuracy, precision, recall, and F1-score, providing a robust solution for modern cybersecurity applications. |
| AUTHOR | C SHIVA RAMA KRISHNA, DR. K. SANTHI SREE Post-Graduate Student, Department of Computer Science Engineering, Computer Networks and Information Security, Jawaharlal Nehru Technological University, Hyderabad, India Professor and Head, Department of Computer Science Engineering, Jawaharlal Nehru Technological University, Hyderabad, India |
| VOLUME | 185 |
| DOI | DOI: 10.15680/IJIRCCE.2026.1406042 |
| pdf/42_Ensemble Learning for Enhanced Malware Detection.pdf | |
| KEYWORDS | |
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