AdaptIDS: Adaptive Intrusion Detection for Mission-Critical Aerospace Vehicles
Bibliographic record
Abstract
Aerospace and defense industries are particularly vulnerable to cyber threats given their sensitive nature, significantly extending the consequences of security breaches to the national level. Aerospace vehicles are augmented by cooperative control, intelligent, connected, and autonomous systems. The risk against such systems is further amplified due to commonly relying on the MIL-STD-1553 communication bus developed with a high focus on reliability and fault tolerance, albeit with security as a second priority. MIL-STD-1553 (a.k.a., STANAG 3838 by NATO) is a standard that describes a serial data communication bus primarily used in aerospace vehicles for military and civilian applications, including avionics, aircraft, and spacecraft data handling. In the absence of core security measures such as authentication, authorization, and encryption, the bus connecting sensitive functions, including autopilot, GPS, fuel valve switches, and other avionics equipment, is easily vulnerable to a wide range of attacks. This paper proposes, AdaptIDS, a novel adaptive intrusion detection system as a security analytics framework for the MIL-STD-1553 communication bus. AdaptIDS mainly adopts data science principles and leverages advanced deep learning techniques (i.e., the stacking ensemble) to boost its generalization capabilities for detecting unseen patterns of attacks in the dynamic changing environment of aerospace vehicles. Extensive experiments are conducted using two datasets generated from an open-source simulation system, reflecting dynamic real-life scenarios. The evaluation results demonstrate that our solution outperforms existing solutions with high detection effectiveness of 0.99 F1-measure and computational time efficiency.
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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) | 0.002 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| 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".