Database Resilience in the Era of Persistent Threats: Integrating Breach Forensics, Anomaly Detection, and Predictive Models
Confidence N. Oguebu, Chinedu Jude Nzekwe · International Journal of Research Publication and Reviews · 2024
In an era marked by persistent cyber threats, database resilience has become a critical priority for organizations seeking to protect sensitive information and maintain operational continuity.The increasing sophistication of cyberattacks, including ransomware, data breaches, and insider threats, necessitates a multifaceted approach to database security.This paper explores the integration of breach forensics, anomaly detection, and predictive modelling as key components of a comprehensive strategy to enhance database resilience.Breach forensics plays a pivotal role in understanding the scope and root causes of security incidents, enabling organizations to implement targeted corrective measures.Anomaly detection systems, powered by machine learning algorithms, provide real-time identification of unusual patterns and behaviours that may indicate emerging threats.Predictive models further complement these efforts by leveraging historical data to forecast potential vulnerabilities and proactively address them before they are exploited.This study examines the interplay between these technologies, emphasizing their collective value in creating a robust defense mechanism against persistent threats.It also discusses implementation challenges, including computational overhead, false-positive rates, and the need for continuous model training.By analysing case studies and industry best practices, the paper offers actionable insights for integrating these technologies into existing database security frameworks.The findings underscore the importance of a proactive, datadriven approach to achieving database resilience, safeguarding organizational assets, and maintaining stakeholder trust in an increasingly threat-prone digital landscape.