International Journal of Science, Engineering and TechnologyAuthors: Sneha Bhatt, Kiran Patil, Ashwini Desai, Mohan Raj
Abstract: In today’s complex and hybrid IT environments, ensuring configuration integrity is fundamental to maintaining security and compliance. Tripwire, a widely used file integrity monitoring and configuration assessment tool, plays a crucial role in detecting unauthorized or unexpected changes across enterprise systems. However, the sheer volume of alerts generated by Tripwire can quickly overwhelm security analysts, leading to alert fatigue, slow response times, and missed critical incidents. This challenge is especially pronounced in regulated industries such as healthcare, finance, and government, where each missed or misprioritized alert can result in compliance violations or security breaches. This review explores the application of machine learning (ML) techniques to enhance the prioritization of Tripwire-generated security alerts. By leveraging structured logs, metadata, asset sensitivity, user behavior, and historical incident outcomes, ML models can assign contextual risk scores to each alert, allowing security teams to triage more effectively. The article details the architectural design of such intelligent alerting systems—covering data preprocessing pipelines, supervised and unsupervised learning models, anomaly detection, and integration with SIEMs and ITSM workflows. It also highlights real-world implementation challenges, including model drift, data noise, and the need for explainability to gain analyst trust. Future directions are discussed, such as federated learning for privacy-preserving training, NLP for semantic log analysis, and zero-trust context enrichment for deeper threat insights. By fusing Tripwire’s robust detection capabilities with AI-driven prioritization, organizations can achieve a more adaptive, efficient, and risk-aware security posture. This synergy empowers security operations centers to respond faster, reduce noise, and strengthen configuration compliance in dynamic enterprise ecosystems.
DOI: http://doi.org/10.5281/zenodo.15846668
