Region-Aware Bagging and Deep Learning-Based Fake Task Detection in Mobile Crowdsensing Platforms
Bibliographic record
Abstract
Mobile crowdsensing (MCS) is a distributed sensing concept that enables ubiquitous sensing services via various builtin sensors in smart devices. However, MCS systems are vulnerable because of being non-dedicated. Especially, submission of fake tasks with the aim of clogging participants device resources as well as MCS servers is a crucial threat to MCS platforms. In this paper, we propose an ensemble learning-based solution for MCS platforms to mitigate illegitimate tasks. Furthermore, we also integrate k-means-based classification with the proposed method to extract region-specific features as input to the machine learningbased fake task detection. Through simulations, we compare the ensemble method to a previously proposed Deep Belief Network (DBN)-based fake task detection, which is also shown to improve performance in terms of accuracy, F1 score, recall, precision and geometric mean score (G-mean) with the integration of regionawareness. Our validation results show that the ensemble machine learning-based detection can eliminate majority of the fake tasks, with up to 0.995 precision, 0.997 recall, 0.996 F1, 0.993 accuracy and 0.982 G-Mean. Furthermore, the proposed solution introduces savings up to 12.18% battery of mobile devices while reducing the impacted recruits to 0.25% and protecting up to 10.59% participants against malicious sensing tasks.
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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".