The article describes a new method for malware classification, based on a Machine Learning (ML) model architecture specifically designed for malware detection, enabling real-time and accurate malware identification. Using an innovative feature dimensionality reduction technique called the Interpolation-based Feature Dimensionality Reduction Technique (IFDRT), the authors have significantly reduced the feature space while retaining critical information necessary for malware classification. This technique optimizes the model’s performance and reduces computational requirements. The proposed method is demonstrated by applying it to the BODMAS malware dataset, which contains 57,293 malware samples and 77,142 benign samples, each with a 2381-feature vector. Through the IFDRT method, the dataset is transformed, reducing the number of features while maintaining essential data for accurate classification. The evaluation results show outstanding performance, with an F1 score of 0.984 and a high accuracy of 98.5% using only two reduced features. This demonstrates the method’s ability to classify malware samples accurately while minimizing processing time. The method allows for improving computational efficiency by reducing the feature space, which decreases the memory and time requirements for training and prediction. The new method’s effectiveness is confirmed by the calculations, which indicate significant improvements in malware classification accuracy and efficiency. The research results enhance existing malware detection techniques and can be applied in various cybersecurity applications, including real-time malware detection on resource-constrained devices. Novelty and scientific contribution lie in the development of the IFDRT method, which provides a robust and efficient solution for feature reduction in ML-based malware classification, paving the way for more effective and scalable cybersecurity measures.
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