Detecting Fake Mobile Crowdsensing Tasks: Ensemble Methods Under Limited Data
Bibliographic record
Abstract
The nondedicated sensing capabilities of smart mobile devices contribute to Internet of Things (IoT) ecosystems with integral building blocks called mobile crowdsensing (MCS) systems. The distributed and nontrusted nature of MCS systems leads to various threats for devices and MCS platforms as well as for end users. Out of the many threats, fake tasks may lead to drained resources at the participating devices and clogged resources at the MCS platforms. Furthermore, when limited data are available, it becomes a further challenge to identify maliciously submitted fake tasks. In this article, we introduce possible solutions that leverage ensemble learning against fake tasks submitted to MCS platforms. More specifically, boosting-based solutions, namely adaptive boosting for binary classification (AdaBoost), gentle adaptive boosting (GentleBoost), and random under-sampling boosting (RUSBoost), form the basis for learning the legitimacy of tasks submitted to MCS platforms. Over a six-day observation window, one day was used for training while the remaining five days were used for testing to evaluate the performance under limited data in terms of training the machine learning (ML) models. Through extensive simulations, we have shown that GentleBoostbased ensemble learning can achieve promising performance in detecting fake/illegitimate tasks submitted to an MCS platform.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.004 | 0.009 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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 source (direct Gemma or distilled Codex), 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".