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.

Introduction

The integration of traditional file integrity monitoring (FIM) methods with machine learning (ML) offers a powerful hybrid approach to enhance cybersecurity, especially in Internet of Things (IoT) and industrial control systems (ICS). Traditional FIM relies on cryptographic hashes to detect file changes but cannot assess the context or risk of modifications, resulting in many false positives and limited threat insight.

Recent research highlights the use of ML techniques—such as Random Forest classifiers and federated learning—to improve detection accuracy, reduce false alarms, and adapt to evolving threats by analyzing file attributes and behavior patterns. Hybrid systems combine conventional hash-based checks with ML-driven risk assessment to not only detect file changes but also evaluate their malicious potential, enabling proactive defense.

The proposed system integrates a traditional FIM module with an AI-driven risk assessment module that scores detected file modifications based on features like file type, size, and modification frequency. Tested on real-world and synthetic data, this hybrid model showed improved accuracy (92% vs. 75% for traditional FIM), lower false positives, faster response times, and better scalability, especially in complex cloud and virtual environments.

Advantages of AI integration include contextual threat analysis, reduced alert fatigue, adaptability, and proactive threat identification. However, challenges remain in terms of training data quality, computational resources, and implementation complexity.

Conclusion

The integration of AI into traditional file integrity monitoring represents a promising advancement in the field of cybersecurity. By enhancing the ability to detect, assess, and respond to file changes, the hybrid model offers a more intelligent, scalable, and efficient solution for modern security challenges. While traditional FIM systems have long been a cornerstoneofcybersecurity, therapidevolutionof thethreatlandscapedemandsmore adaptiveandproactivesolutions.The use of machine learning brings the potential for smarter, more nuanced security tools that can keep pace with the increasing complexity of cyber threats. Theresultsofthisstudyunderscoretheimportanceofcontinued innovationin cybersecurity toolsandhighlightthepotential of AI-driven systems in addressing the limitations of traditional approaches. As organizations increasingly adopt cloud- based and virtualized environments, the demand for intelligent, scalable FIM solutions will only grow. The hybridmodeldevelopedinthis researchlaysthefoundationforfutureadvancementsinfileintegritymonitoringandcybersecurityatlarge, pushing thefield towardsmoreadaptive, contextawaresystemsthatcan anticipateand mitigatethreatswith greater accuracy and efficiency.

References

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Copyright

Copyright © 2025 Dnyaneshwar Navnath Vaykar, Hemant Kailas Dakhore, Yash Chavan, Shantanu Tathe . This is an open access article distributed under the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.