Core Logic and Algorithmic Performance Enhancements for a System Vulnerability Analysis Technique for Complex Mission Critical Systems Implementation

2026-04-25

AI Summary

This work addresses the limitations of existing systems constrained by Boolean logic, which struggle to accurately model complex network structures in mission-critical applications. The authors reconstruct the logical core of the SONARR system by extending its rule engine to support arbitrary .NET data types, introducing a generic-based universal logic expression mechanism, and integrating digital twin technology for flexible system modeling. Furthermore, a parallel architecture leveraging multiple compute nodes is employed to significantly enhance processing efficiency for large-scale tasks. This approach not only substantially expands modeling expressiveness but also enables efficient analysis of diverse data types—including integers and strings—thereby markedly improving both the performance and applicability of complex network model analysis.

Technology Category

• Knowledge Representation and Reasoning: Computational Complexity of Reasoning
• Planning, Routing, and Scheduling: Model-Based Reasoning
• Cognitive Modeling & Cognitive Systems: Conceptual Inference and Reasoning

Application Category

• Systems and Infrastructure for Web, Mobile and WoT: Applied ML and AI for Web-based mobile applications
• Graph Algorithms and Modeling for the Web: Representation, reconstruction, and subgraph or motif discovery in Web-related graphs
• Social Networks and Social Media: Generative AI / large language models and their impact on social systems

Abstract

Core logic and processing improvements were made to the software for operations and network attack results review (SONARR) and are presented, herein. Previous SONARR versions' Boolean-only logic, derived from the Blackboard Architecture, was replaced with generic logic that allows any .NET type (e.g., integers, decimals, strings) to be utilized within facts. This allows calculations and equality operations with all data types to drive the algorithm's processing of network models. Additionally, multi-compute capabilities were implemented to increase the processing power for larger workloads. In this paper, the new logic objects are described, examples are presented to illustrate the efficacy of creating digital-twin systems using the new generic logic, and performance test results are presented that illustrate the expanded processing capability from the multi-compute functionality.

Problem

• system vulnerability analysis
• mission critical systems
• Boolean logic limitation
• multi-compute capability
• network modeling

Innovation

• generic logic
• multi-compute
• digital twin
• vulnerability analysis
• SONARR