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 | Enhanced Real Time Detection of Polymorphic Malware Using Synthetic Data Augmentation |
|---|---|
| ABSTRACT | The evolution of malware led to the emergence of polymorphic variants capable of continuously modifying their code structure to evade traditional detection mechanisms. Such adaptability renders conventional signature-based and heuristic approaches largely ineffective, creating a need for intelligent and adaptive security solutions. This research presents a framework for real-time detection of polymorphic malware, integrating deep learning and synthetic data augmentation to enhance robustness and generalization. The proposed system leverages Convolutional Neural Networks (CNNs), Recurrent Neural Networks (RNNs), Attention LSTM along with other algorithms like Random Forest, Voting Classifier, Stacking Classifier and Naive Bayes to jointly analyze both static and dynamic features of malware, enabling the detection of complex behavioral patterns. To address the challenge of limited labeled datasets, a Generative Adversarial Network (GAN) is employed to generate synthetic data, thereby enriching the training samples and improving model diversity. The framework incorporates a data processing pipeline, including feature engineering, balancing, and real-time traffic analysis. To enhance transparency and trust, Explainable AI techniques such as LIME and SHAP are employed to interpret feature contributions and model decisions. Furthermore, a Flask-based web application is developed to enable real-time malware prediction through a user-friendly interface |
| AUTHOR | ANISETTI AMITHA, DR. M. NAGARATNA Post-Graduate Student, Cyber Forensics and Information Security, Department of Computer Science Engineering, Jawaharlal Nehru Technological University, Hyderabad, India Professor, Department of Computer Science Engineering, Jawaharlal Nehru Technological University, Hyderabad, India |
| VOLUME | 186 |
| DOI | DOI: 10.15680/IJIRCCE.2026.1407018 |
| pdf/18_Enhanced Real Time Detection of Polymorphic Malware Using Synthetic Data Augmentation.pdf | |
| KEYWORDS | |
| References | 1. M. Gazzan, B. Alobaywi, M. Almutairi and F. T. Sheldon, 'A Deep Learning Framework for Enhanced Detection of Polymorphic Ransomware', Future Internet, Vol. 17, Issue 7, Article 311, 2025. 2. M. Anderson, S. Rahman, H. Tanaka, C. Dominguez and L. Yue, 'AI-Based Detection of Polymorphic Malware', 2025. 3. M. S. Avhankar, J. Pawar and V. Kumbhar, 'A Comprehensive Survey on Polymorphic Malware Analysis: Challenges, Techniques, and Future Directions', 2025. 4. J. Whitman, A. El-Karim, P. Nandakumar, F. Ortega and L. Zheng, 'Real-Time Malware Forensics Using AI', 2025. 5. H. C. B. Hamadjida, A. T. Kouanou, C. T. Tchito and C. T. Kouadjo, 'Malware's Polymorphism Analysis Using a Hybrid Machine Learning Algorithm Approach', Proceedings of the Data Science Workshop: From Theory to Practice, 2025. 6. K. R. Ahmed, M. M. A. Semi, S. Akther, M. M. K. Rabbi, M. R. Raja, U. Chakraborty and M. I. H. Rial, 'Blockchain-Integrated Malware Detection Systems: Enhancing Accuracy and Trust in Cybersecurity', Proceedings of the 4th OPJU International Technology Conference (OTCON) on Smart Computing for Innovation and Advancement in Industry 5.0, IEEE, pp. 1–6, 2025. 7. L. Mauri and E. Damiani, 'Hardening Behavioral Classifiers Against Polymorphic Malware: An Ensemble Approach Based on Minority Report', Information Sciences, Vol. 689, Article 121499, 2025. 8. S. Sultana, M. Uddin, M. A. R. Chy, S. N. Hasan, E. Hossain, H. Kaur and J. Kaur, 'AI-Augmented Big Data Analytics for Real-Time Cyber Attack Detection and Proactive Threat Mitigation', International Journal of Computational and Experimental Science and Engineering, Vol. 11, Issue 3, pp. 5639–5647, 2025. |
