aiqbench analysis
This research proposes a Bayesian Network-based Decision Support System to streamline security tool selection for heterogeneous open-source networks, addressing the growing complexity of maintaining interdisciplinary knowledge. By inferring optimal security mechanisms based on high-level requirements, the framework aims to reduce operator cognitive load while ensuring alignment with the security triad. The study evaluates the system's architecture and performance in terms of inference time and prediction accuracy, offering a practical approach for managing diverse security stacks.
A Bayesian Network Approach for Enhancing Security-Focused Decision Support Systems
arXiv:2606.10782v1 Announce Type: cross 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.
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.
Cryptography and Security (cs.CR); Artificial Intelligence (cs.AI); Machine Learning (cs.LG)
arXiv:2606.10782 [cs.CR]
(or arXiv:2606.10782v1 [cs.CR] for this version)
https://doi.org/10.48550/arXiv.2606.10782 Focus to learn more arXiv-issued DOI via DataCite (pending registration)
Proc. 2025 IEEE 50th Conference on Local Computer Networks (LCN), 2025
https://doi.org/10.1109/LCN65610.2025.11146363 Focus to learn more DOI(s) linking to related resources
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Full text under the publisher's open licence · 287 words