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Buczak, A.L. and Guven, E. (2016) A Survey of Data Mining and Machine Learning Methods for Cyber Security Intrusion Detection. IEEE Communications Surveys & Tutorials, 18, 1153-1176.
https://doi.org/10.1109/comst.2015.2494502

has been cited by the following article:

• TITLE: Artificial Intelligence as a Security Mediation Layer in Enterprise Environments

  AUTHORS: Ustinovich Viktor Mikhailovich

  KEYWORDS: Artificial Intelligence, Enterprise Cybersecurity, Behavioral Analytics, Anomaly Detection, Zero-Trust Architecture, Machine Learning, Security Operations Center, Autonomous Threat Response, Distributed Systems Security

  JOURNAL NAME: Journal of Computer and Communications, Vol.14 No.6, June 18, 2026

  ABSTRACT: Traditional perimeter-based security models have proven structurally inadequate against the adaptive, multi-vector attack strategies that characterize contemporary enterprise threats. This article examines the theoretical and architectural foundations of artificial intelligence as a security mediation layer in large organizational environments, focusing on behavioral analytics, anomaly detection, automated policy enforcement, and zero-trust integration. Drawing on peer-reviewed scholarship and practitioner-research contributions, the study traces how recent architectural work engages problems that academic research has identified but rarely resolved at the deployment level, among them temporal threat modeling, distributed policy consistency, and the governance of autonomous decision systems. The analysis reveals persistent tensions between detection sensitivity and operational manageability, between model sophistication and institutional accountability, and between autonomous response and the interpretive role of human analysts. Resolving these tensions requires coherent architectural and governance frameworks sustained over the operational lifecycle of deployed systems.