INTELLIGENT ADAPTIVE MACHINE LEARNING SCALABLE FRAMEWORK FOR DYNAMIC MALWARE IDENTIFICATION AND PROACTIVE THREAT PREVENTION

Abstract

Malware and computer viruses continue to pose serious threats to modern digital systems, often compromising security and leading to significant financial losses. As technology evolves, traditional malware detection methods—particularly signature-based approaches—are becoming less effective. These methods rely heavily on manual updates, respond only after threats are identified, and struggle to keep up with the growing volume and complexity of cyberattacks. In this study, we explore the potential of machine learning as a more advanced and proactive solution for malware detection. Unlike conventional techniques, machine learning models can learn from historical data and identify patterns that indicate malicious behavior. This capability allows them to detect previously unknown or emerging malware variants without requiring explicit signatures. The research focuses on evaluating the performance of three widely used machine learning algorithms: Random Forest, Gradient Boosting, and Support Vector Machine. These models are analyzed based on key feature sets and performance metrics to determine their effectiveness in identifying malicious software. Experimental findings demonstrate that these algorithms significantly improve detection accuracy while reducing false positives.Furthermore, the adaptability of machine learning models enables continuous improvement as new data becomes available, making them highly suitable for dynamic threat environments. The proposed approach also supports real-time detection, which is critical for minimizing damage caused by fast-spreading malware. In addition, the scalability of these techniques allows them to be implemented across large and complex networks without major performance degradation. This study also highlights the importance of feature selection and data preprocessing in improving model efficiency and accuracy. By integrating intelligent detection mechanisms, organizations can strengthen their cybersecurity infrastructure and respond more effectively to evolving threats. Overall, this research contributes to the development of robust, flexible, and sustainable malware detection systems, offering a promising direction for future advancements in cybersecurity.