Self Organizing Feature Map-Integrated Knowledge-Based Deep Network Against Fake Crowdsensing Tasks
Bibliographic record
Abstract
Mobile Crowdsensing (MCS) builds on the Sensing as a Service model, and is considered to be an integral component of the Internet of Things systems. Since MCS does not build on a thoroughly assessed and established trust mechanism between all parties various threats including data poisoning, fake sensing tasks and clogging task attacks remain challenges. Fake task submissions are the least investigated although they have the potential to drain significant amount of resources (e.g. battery, sensors, processing, storage) and clog the MCS servers. This paper proposes a knowledge-based technique alongside sequential feature selection methodology to detect fake sensing tasks submitted to the MCS servers so that the tasks do not get assigned to the participants but filtered at the MCS servers. The proposed methodology is compared to fake task detection under Knowledge Based Deep Neural Network which is also enhanced by feature selection, and the simulation results show that the proposed methodology, by utilizing Deep Prior Knowledge Input with Self-Organizing Feature Map can outperform the deep neural network-based detection by 9.7% in terms of accuracy.
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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 | 0.002 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| 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".