Poseidon’s Shield: Secure Marine Data Acquisition System
Bibliographic record
Abstract
The increasing digitization of the marine industry challenges data acquisition systems capable of withstanding cybersecurity threats. Poseidon’s Shield is a prototype system designed to securely collect, transmit, and visualize sensor data from marine vessels in real time. The prototype addresses security concerns, including data confidentiality, integrity, and availability, through a combination of symmetric and asymmetric cryptographic techniques layered over Ethernet and User Diagram Protocol communication. The system is composed of modular data acquisition, relay, and aggregation nodes that facilitate secure data flow, enhanced with metadata tracking and provenance visualization. Poseidon’s Shield supports legacy marine protocols such as NMEA 2000 and Modbus, enabling backward compatibility while providing a user-friendly interface and real-time security alerts. The prototype developed demonstrates successful integration of secure communication, data provenance, and interactive visualization using Google’s Protocol Buffers framework, MongoDB, and modern cryptographic libraries. While current limitations restrict protection to in-transit data tampering, the system is designed for extensibility to address broader threats. This open-source solution proves that secure, interoperable, and practical marine data systems are feasible and can serve as a foundation for future marine cybersecurity advancements.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.011 | 0.005 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".