Spoofing prevention using received signal strength for ZigBee-based home area networks
Bibliographic record
Abstract
In this paper we present a novel spoofing prevention system (SPS) for ZigBee based home area networks (HANs) within smart grids. The proposed SPS uses the spatial correlation of received signal strength (RSS) in order to detect attacks and filter malicious frames. The SPS consists of a spoofing detection module which is installed on the security center in the HAN, as well as spoofing prevention agents installed on network nodes. Once an attack is detected, the agents differentiate and filter malicious frames by analyzing the RSS values of received frames. Two methods are introduced and investigated for attack prevention, static threshold and dynamic threshold. The former has very low computational requirements, yet due to high false positive rate introduces some network overhead during the attack. The latter needs more computations; however, it has a higher performance and a very low network overhead. The soundness of the proposed method is proved through both theoretical analysis, as well as experiments.
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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 | 0.000 |
| 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".