A Comprehensive Study Robust Statistical Methods for Detection of Malware

Abstract

This paper explores the application of robust statistical methods for detecting malware, addressing the challenges posed by noisy data and evolving threats in cybersecurity. Traditional malware detection techniques often rely on fixed signatures, making them vulnerable to new variants. In contrast, robust statistical methods, such as outlier detection and robust regression, effectively identify strange patterns in network traffic and system behavior, enabling the recognition of previously unseen malware. We discuss integrating these methods with machine learning algorithms to enhance detection accuracy and resilience. Techniques like kernel density estimation help establish baseline behavior, facilitating the identification of deviations indicative of malicious activity.

Additionally, Bayesian approaches allow for dynamic model updates, providing real-time adaptability to new data. Our findings demonstrate that robust statistical methods significantly improve the reliability of malware detection systems, particularly against sophisticated attacks and zero-day exploits. By leveraging these advanced techniques, organizations can enhance their cybersecurity posture, effectively mitigating risks associated with evolving malware threats. This paper highlights the importance of incorporating robust statistical approaches into malware detection frameworks to achieve greater accuracy and resilience in an increasingly complex cyber landscape.