Security Vulnerability Analysis and Recommendations for Open Media Vault Cloud Server on Raspberry Pi
Bibliographic record
Abstract
The Raspberry Pi has been increasingly utilized as a network-attached storage (NAS) server, with Open Media Vault (OMV) software handling file and data storage. Access to the NAS server is provided through a Local Area Network (LAN), where open ports can pose potential security risks, enabling unauthorized intrusion. In this study, the network design method incorporating the PPDIOO model was employed to conduct a vulnerability assessment and to offer security recommendations for the OMV Cloud server running on Raspberry Pi. The analysis was executed using two prominent security tools, Nmap and Nessus. Upon employing Nmap and Nessus in the evaluation, several security vulnerabilities were identified on the OMV Cloud server utilizing Raspberry Pi. Through continuous monitoring and analysis, open ports were detected, including: port 22 (SSH), port 80 (WEB), port 111 (rcpbind), port 139 (netbios-ssn), port 445 (netbios-ssn), port 2049 (NFS), port 3389 (ms-wbt-server), and port 5357 (WSDAPI). Based on the assessment, seven solutions were proposed, addressing three vulnerability categories: high (2%), medium (2%), and informational (96%). This comprehensive examination provides valuable insight into securing the OMV Cloud server, enhancing the overall security of Raspberry Pi-based NAS implementations.
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 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 0.000 |
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".