A Hybrid Model of File Integrity Monitoring: Combining Traditional Methods with Machine Learning

Abstract: In today's rapidly evolving cybersecurity landscape, file integrity monitoring (FIM) remains a critical line of defence against data breaches, malicious attacks, Third party interruption, loss of sensitive data and lack of internal data security. Traditional FIM techniques, such as Tripwire and Advanced Intrusion Detection Environments (AIDE), have long been trusted for detecting unauthorized changes in files. However, these methods often suffer from limitations suchas high false-positive rates and inefficiencies in handling large-scale, dynamic environments. In this paper, we propose a hybrid model of File Integrity Monitoring that combines traditional methods with advanced machine learning techniques to enhance detection accuracy and reduce operational overhead.Also, we try to an improve the that limitations of a traditional techniques by an making a hybrid of Traditional FIM techniques & advanced machine learning techniques to improve and make a secure environment. By leveraging the strengths of both approaches, the hybrid model addresses key weaknessesinconventional systems,improvingbothreal-timedetectioncapabilitiesandadaptability indiverse computing environments, including cloud and virtualized infrastructures. The proposed model demonstrates significant improvements in file integrity monitoring, providing a robust, scalable, and efficient solution for modern cybersecurity challenges.