Project AFKUI

From data analysis to the implementation of cloud-based architectures and secure interface endpoints, the project follows a precise development plan. The subsequent milestones document the essential phases of this research work.

The AFKUI project focuses on real-time analysis, assessment, and filtering of maritime movement and sensor data. Using advanced measurement techniques, distributed acoustic sensing (DAS), satellite fusion, and artificial intelligence, the platform automatically detects data anomalies, time-stamp corruptions, loitering patterns, and intentional spoofing events in near-real-time.

• Requirement Analysis: Developing stakeholder-driven engineering specifications for technical infrastructure, interfaces, and the data model.
• Technical Translation: Formulating architectural plans for cloud processing modules and mapping cross-component infrastructure needs.
• IT Structure Setup: Setting up isolated testing structures, centralized access tokens, secure credential vaults, and continuous integration pipelines.
• Server Procurement: Calculating and pre-allocating necessary computing nodes, persistent volumes, and CPU times within optimized service brackets.

• Data Procurement: Acquiring deep-sea satellite feeds, global historical datasets, and commercial Shipborne AIS data to calibrate core behavioral models.
• Raw Storage Management: Architecting block storage vaults and defining message header parameters to log precision server arrival times.
• Vessel Database Building: Developing multidimensional searchable master record structures capable of tracking lifecycles and registration changes.
• Weather Data Fusion: Integrating environmental models including wind vector paths and sea currents to analyze speed variations.
• Station Management: Programming database logic to track provider behaviors and establish safety blocklists for malicious ingress channels.
• System Stream Monitoring: Creating custom telemetry dashboards to monitor overall dataset health, latency KPIs (< 1,000 ms), and operational thresholds.
• AIS Data Decoding: Creating tailored ingestion layers to evaluate sentence structures, catch corrupt entries, and purge duplicate traffic.
• High-Availability Partitioning: Deploying robust cloud tables indexed via high-performance geohashes and unique identifiers.
• Security Auditing: Performing threat evaluations, assessing infrastructure vulnerabilities, and generating compliance registers for KRITIS environments.

• Vesselstate Core Construction: Compiling microservices to stitch master static characteristics with streaming geographic coordinates.
• Secure Protocol Gateways: Engineering WebSocket, TCP-Pull, and MQTT transfer layers incorporating dynamic reconnect handlers and flow control.
• Multivariable Live Streams: Developing data piping connections across multiple protocol targets to pass structural data feeds securely.
• Virtual API Gateways & WFS: Building elastic application and spatial feature layers protecting backend systems from high-volume automated requests.
• Uncertainty Quantification: Executing numeric models to establish reliable confidence scores for unverified spatial entries.
• Cloud Microservices Adaptation: Migrating abstract logic definitions into concurrent software processes across internal software services.
• Stream Assessment Audits: Running joint code reviews to ensure live processing paths accurately categorize metadata.
• Shipborne AIS Validation: Evaluating coverage boundaries, satellite channel congestion, and signal quality to close coverage gaps in the German EEZ (AWZ).
• Field Correction Logic: Writing criteria protocols determining when individual attributes should be logically filled or rejected.

• Real-time MMSI-Twin Detection: Implementing streaming mathematical algorithms to detect identical MMSI usage across multiple vessels within sub-second windows.
• NMEA Header Tagging: Enriching streaming message headers (t-tag) with confidence flags ("MMSI questionable") for downstream threat awareness.
• Demonstrator Proof of Concept: Formulating swift spatial indexing engines and spatial WFS endpoints to support concurrent web visualizations.
• Middleware Architecture: Establishing secure operational session tracking layers and anti-bot defenses for web assets.
• Frontend Integration: Supporting IBM's situational picture layout and map tiles to display validated and cleaned maritime data layers.
• Intellectual Property & Exploitation Review: Tracking work achievements to determine registerable engineering rights, academic publications, and commercial service rollouts.

Provide civil defense authorities, port management entities, and maritime researchers with cost-effective access to secure, low-latency, and forensically validated AIS data streams—ensuring robust protection for critical subsea infrastructure.