Fundamental Principles for Building an Effective Detection Engineering System

Citation: Alina Gaifulina, "Fundamental Principles for Building an Effective Detection Engineering System", Universal Library of Innovative Research and Studies, Special Issue.

Copyright: This is an open access article distributed under the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.

Abstract

The article presents a comprehensive analysis of the principles and architectures of detection engineering as a methodological foundation for building modern systems for identifying and preventing cyberattacks. The study employs an interdisciplinary approach that integrates machine learning, provenance data graph analysis, and the MITRE ATT&CK taxonomy. The analysis is based on recent international publications reflecting the shift from signature-based methods to context-oriented models capable of adaptive self-learning and feedback with monitoring centers. The key components of the detection engineering cycle are examined, including data standardization under the STIX 2.1 format, correlation of system calls with MITRE tactics, the use of directed graphs for behavioral modeling, and the implementation of adaptive thresholds in classification algorithms. Particular attention is given to identifying implementation barriers related to log heterogeneity, lack of realistic datasets, and the dependence of models on fixed metrics. The novelty of the study lies in formulating the principles of building a detection engineering pipeline that unites machine learning, behavioral analytics, and organizational mechanisms of SOC within a single adaptive security framework. The practical significance of the research consists in justifying approaches that reduce false positives, improve the interpretability of detections, and enhance system resilience to APT-class attacks. The article will be useful for researchers and practitioners in cybersecurity, developers of analytical platforms, and specialists involved in the design of monitoring and incident response centers.

Keywords: Detection System, Data Analysis, Information Standardization, Behavior Modeling, Automation, Security, Monitoring.

https://doi.org/10.70315/uloap.ulirs.2023.001