A SECURE MACHINE LEARNING MODEL INTEGRATING CRYPTOGRAPHIC TECHNIQUES FOR REAL-TIME MALWARE DETECTION AND DEFENSE FOR SECURE INFORMATION SYSTEMS

Abstract

Malware and computer viruses pose serious threats to information systems, compromising data confidentiality, integrity, and availability, while also resulting in significant financial losses. Conventional malware detection techniques, such as signature-based approaches, face major limitations including manual signature updates, reactive threat response, and poor scalability against evolving attacks. To address these challenges, this research explores the integration of machine learning and cryptographic mechanisms for intelligent malware detection and prevention. Machine learning algorithms possess the ability to identify emerging and previously unseen malware variants by learning from behavioral and encrypted data patterns. Using three core feature metrics, this study evaluates the performance of Random Forest, Gradient Boosting, and Support Vector Machine (SVM) classifiers in identifying malicious activities. The experimental results demonstrate that these models not only improve detection accuracy but also strengthen cryptographic security and data protection frameworks. Overall, this research contributes to the advancement of information security by proposing adaptive, scalable, and secure methodologies for next-generation malware detection systems.