Title: Machine Learning Applications in Secure Systems Administration: Current Trends and Future Directions
Authors: Chisom Elizabeth Alozie, Oluwasanmi Segun Adanigbo, Olumese Anthony Abieba
Volume: 9
Issue: 4
Pages: 46-55
Publication Date: 2025/04/28
Abstract:
This review paper explores the transformative impact of machine learning (ML) on secure systems administration, highlighting current trends, challenges, and future opportunities. As cybersecurity threats become more sophisticated, ML has become a crucial tool for enhancing threat detection, automating responses, and predicting potential risks. This paper discusses the application of ML in areas such as anomaly detection, user behavior analytics, and predictive analytics while also examining the challenges related to data quality, adversarial attacks, model interpretability, and integration with existing security systems. The review underscores the practical implications for cybersecurity professionals, emphasizing the need for continuous learning and adaptation as ML technologies evolve. Additionally, the paper calls for further research to address existing challenges and explore future directions, including advances in adversarial machine learning and the development of AI-driven cybersecurity operations centers. The findings of this review provide valuable insights for practitioners and researchers, offering a roadmap for the continued integration of ML in secure systems administration.