Abstract

Behavior-driven malware detection frameworks are increasingly essential for securing modern operating systems against sophisticated and evolving cyber threats. Unlike signature-based detection methods, which rely on known malware patterns, behavior-driven frameworks analyze runtime system activities and application behaviors to identify malicious actions, even in previously unseen malware variants. This research explores the architecture, methodologies, and applications of behavior-driven malware detection frameworks, focusing on their deployment in modern operating systems such as Windows, Linux, and macOS. The study highlights how these frameworks leverage techniques such as system call monitoring, API tracing, and behavioral profiling to detect anomalous activities indicative of malware. It also examines the integration of machine learning algorithms for enhanced predictive capabilities and adaptive threat detection. By reviewing current literature, experimental methodologies, and real-world implementations, this study evaluates the effectiveness, scalability, and limitations of behavior-driven approaches compared to traditional detection methods. The findings underscore the importance of behavioral analysis in achieving proactive cybersecurity and provide recommendations for designing robust, adaptive malware detection frameworks capable of safeguarding modern computing environments