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