Replay Cyberattack Detection in an IoT-based Healthcare System using RFID Sensors
Bibliographic record
Abstract
The use of automated insulin injection systems has become increasingly common for management of diabetes. However, these systems are vulnerable to cyberattacks, which can compromise the safety and effectiveness of the treatment. One type of cyberattack that has been identified in these systems is the replay attack, in which the hacker intercepts and replays legitimate data in order to manipulate the insulin delivery. In this paper, we propose a replay cyberattack detection system based on a feasibility condition that is developed and investigated. Towards this end, we add a virtual auxiliary system and detection filters to the automated control system. The key advantage of our proposed methodology is that the proposed scheme makes the system resilient to undetectable replay cyberattacks where the filters are capable of isolating replay cyberattacks from other types of adversary cyberattacks. The simulation results are provided to illustrate the effectiveness of the proposed approach.
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How this classification was reachedexpand
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 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 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 itClassification
machine, unvalidatedMachine predicted; a candidate call from one teacher head, 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".