Next-Gen Malware Detection using AI AI-Powered Malware Threat Detection Automated Malware Classification Through Machine Learning

Malware poses a growing cybersecurity threat, rendering traditional signature-based detection methods insufficient against evolving attack strategies. Many existing machine learning (ML) approaches prioritize dataset reduction to optimize training, but this often results in the loss of critical feature information, reducing their ability to detect sophisticated malware variants. This study presents a computationally efficient malware detection framework leveraging multiple ML models, including Decision Tree, Random Forest, K-Nearest Neighbors (KNN), Support Vector Machine (SVM), Gradient Boosting (GBDT), Convolutional Neural Network (CNN), and Incremental Stochastic Gradient Descent (ISGD). The models are evaluated on a dataset comprising 100,000 samples with 35 system-level behavioral features. Experimental results indicate that Decision Tree, Random Forest, and KNN achieve nearly 100% accuracy, significantly outperforming conventional compact data-based methods. CNN achieves 97.79% accuracy, showcasing deep learning’s potential, whereas SVM attains 86% accuracy but demands higher computational resources. To assess effectiveness, we analyze model complexity, execution time, and classification performance. Unlike dataset reduction techniques, our approach focuses on feature engineering and optimized model selection, ensuring high detection accuracy while maintaining low computational overhead. This makes our framework well-suited for real-time malware classification in cybersecurity applications.