Anomaly Detection for the MIL-STD-1553B Multiplex Data Bus Using an LSTM Autoencoder
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
Due to the modernization of commercial and military aircraft, real-time systems and their connectivity to ground based networks, including the Internet, that were thought to be “air-gapped”, are becoming more susceptible to cyber-attack. Most real-time systems that communicate using the Military Standard 1553B Multiplex data bus (MIL-STD-1553B) protocol do not have the ability to detect cyber-attacks. These systems were originally developed with safety and redundancy in mind, not security. These two factors introduce attack vectors to MIL-STD-1553B communication buses and expose associated avionics systems to exploitation. Recent approaches to anomaly detection for the MIL-STD-1553B data bus have leveraged statistical analysis, Markov Chain modelling, remote terminal fingerprinting and signature-based detection. However, their comparative effectiveness is unknown. Regarding the statistical analysis technique, the lack of accuracy and precision in detecting the start and stop time of anomalous events are not ideal for conducting investigations due to the sheer volume of messages still required to be manually analysed. Deep learning techniques offer an effective means of anomaly detection and applying these techniques to the MIL-STD-1553B data bus could provide more accurate and precise detection times when anomalies or attacks are present, when compared to known statistical analysis, leading to more efficient forensic investigations of anomalous events.
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Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 it