IoTDL<sup>2</sup>AIDS: Toward IoT-Based System Architecture Supporting Distributed LSTM Learning for Adaptive IDS on UAS
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Bibliographic record
Abstract
The rapid proliferation of Unmanned Aircraft Systems (UAS) introduces new threats to national security. UAS technologies have dramatically revolutionized legitimate business operations while providing powerful weaponizing systems to malicious actors and criminals. Due to their inherited wireless capabilities, they are an easy target for cyber threats. In response to this challenge, the implementation of many Intrusion Detection Systems (IDS), which support anomaly detection on UAS, have been proposed in the past. However, such systems often require offline training with heavy processing, making them unsuitable for UAS deployment. This is pertinent for drone systems that support dynamic changes in mission operational tasks. This paper presents a novel system architecture that utilizes sensing systems capabilities available on existing IoT infrastructure for supporting rapid infield adaptive models’ training and parameters estimation services for UAS. We have devised a cluster-oriented distributed training algorithm based on LSTM with mini-batch gradient descent, with hundreds of IoT platforms per cluster collaboratively performing model parameters estimation tasks. The proposed architecture is based on deploying a multilayer system that facilitates secure dissemination of power consumption behavioral patterns for the flight sensing system between the UAS layer and the IoT layer. The model was implemented and deployed on a real IoT-enabled platform based on NXP-Kinetis K64–120 MHz. Furthermore, model training and validation were performed by applying various datasets contaminated with different percentages of malicious data. Our anomaly detection model achieved high prediction accuracy with an ROC-AUC score of 0.9332. The model maintains minimal power consumption overheads and low training time during the processing of a data batch.
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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 | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| 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