Disk Forensics of VxWorks File Systems for Aircraft Security
Bibliographic record
Abstract
Modern avionics systems exhibit numerous networked electronic components ranging from sensors and actuators to dedicated subsystems, resulting in aircraft capable of processing and responding to information accurately, reliably, and in a timely fashion. Assuring the cyber security of these systems is a continual challenge and an active area of research; in the case where an aircraft has been compromised by a malicious actor, digital forensics can be utilized to investigate what and how the incident occurred. This research answers a simple, yet fundamental question on the security of aircraft: whether useful digital forensic artifacts be obtained from embedded real-time systems on aircraft. The highly reliable file system (HRFS) utilized by VxWorks was analyzed and described to align with the generalized descriptions of file system formats accepted in academia. The Sleuth Kit (TSK), an open-source forensic toolkit, was analyzed and extended to include functionality to support this file system, and a proof-of-concept implementation to obtain digital forensic artifacts from real-time operating systems on aircraft was developed. This research finds that the proposed implementation can perform file analysis and recovery from a VxWorks generated HRFS-formatted file system and can be generalized to show that embedded real-time systems can provide useful digital forensic artifacts.
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 | 0.002 | 0.009 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.003 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 0.001 |
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".