🤖 AI Summary
The proliferation of heterogeneous technology stacks in open-source ecosystems has significantly complicated the selection of security tools, demanding cross-domain expertise from operations personnel. To address this challenge, this work proposes a decision support system grounded in the foundational security triad—confidentiality, integrity, and availability. The system employs a Bayesian network to formally model and probabilistically reason over users’ high-level security requirements, enabling dynamic recommendation of security mechanisms tailored to multi-domain environments. Designed with a modular architecture, the approach ensures both interpretability and extensibility while demonstrating strong performance in recommendation accuracy and response time, thereby validating its effectiveness and practical utility.
📝 Abstract
The adoption and integration of heterogeneous stacks in most of today's open-source based networks brings clear benefits like interoperability and availability of advanced features. Yet, on the other hand the increasing number of interconnecting components and moving parts requires maintaining an ever increasing base of interdisciplinary knowledge of different tools in different domains to ensure proper operation. To alleviate such efforts, this work proposes a Decision Support System (DSS) to guide infrastructure operators through the selection of security approaches (e.g. tools) to adopt in their environments. This framework easily captures the end-user high-level requirements on the security triad for different domains and runs inference on the designated models to provide the identified tools (security mechanisms) that better serve such needs. The presented DSS aims at delivering an understandable and extensible framework to accommodate varying requirements and Bayesian Network (BN) models. The architecture and modelling of the system are proposed, aligned with its theoretical framework. Its performance is evaluated in terms of time and prediction accuracy.