Architecture Comparison and Security Assessment Tool for Network Infrastructure

Ensuring robust security and effective architectural design is vital in network infrastructure across various domains. This paper presents the Architecture Comparison and Security Assessment Tool (ACSAT), a novel solution that integrates architectural analysis with cybersecurity expertise to provide comprehensive gap assessments. ACSAT utilizes advanced machine learning techniques, including Optical Character Recognition (OCR) for text extraction, component bounding box detection models, and sophisticated algorithms for mapping interconnections. The tool is implemented as an intuitive desktop Electron application, interfacing with a Flask API orchestrated by Docker containers for scalability and robustness. ACSAT autonomously identifies and analyzes network components—such as servers, switches, compartments, and equipment—within architectural maps. It effectively maps out connections and data flows, generating detailed gap assessment reports that address critical security aspects, including on-premises to cloud transitions and firewall placements. The key contribution of this paper is the innovative synthesis of machine learning, architectural analysis, and cybersecurity expertise within ACSAT, enabling efficient and actionable security evaluations across diverse network infrastructures.