INTEGRATING PREDICTIVE ANALYTICSWITH SIEM FOR ENHANCED THREATDETECTION
Keywords:
Predictive Analytics, Security Information, Event Management, Cybersecurity, Threat Detection, Machine Learning, Incident Response, Data Integration, Anomaly Detection, Advanced Threat Detection, Compliance ReportingAbstract
The integration of predictive analytics with Security Information and Event Management (SIEM) systems represents a significant advancement in the field of cybersecurity, enhancing the ability to detect, respond to, and mitigate threats proactively. This research paper explores the foundational concepts of SIEM and predictive analytics, detailing their core functionalities and the benefits of their convergence. It examines various methods of integration, including direct integration, external analytics engines, and hybrid approaches, and provides practical strategies for implementation. Key benefits of integrating predictive analytics with SIEM systems include enhanced threat detection, improved incident response, optimized resource allocation, and better compliance reporting. However, the paper also addresses the challenges associated with this integration, such as ensuring data quality, maintaining model accuracy, managing integration complexity, and addressing performance and scalability concerns. By leveraging advanced machine learning and data processing techniques, organizations can achieve a more robust and resilient cybersecurity posture. The paper concludes with a discussion on future directions, emphasizing the potential for continued innovation in integrating predictive analytics with SIEM systems to address the ever-evolving landscape of cyber threats
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