Deep Learning-Based Detection of Fake Task Injection in Mobile Crowdsensing
Bibliographic record
Abstract
Mobile crowdsensing (MCS) is a ubiquitous sensing paradigm where built-in sensors of smart mobile devices are empowered to acquire sensory data in lieu of dedicating large scale sensing infrastructures. One of the most crucial problems in mobile crowdsensing is the injection of fake sensing tasks to clog the energy, computing, storage and sensing resources of participating devices. In this paper, we present solutions that leverage deep networks to analyze the tasks submitted to MCS platforms. To this end, we model off-the-shelf deep learning models, namely Deep Autoencoder (Deep-AE), Restricted Boltzmann Machine (RBM) and Deep Belief Network (DBN) in order to detect and filter out illegitimate tasks submitted to MCS campaigns. For the same purpose, we also utilize a Deep Multi-layer Perceptron (Deep-MLP) network instead of the well known Multi-layer Perceptron. Through numerical results on MCS data, we show that Deep-MLP outperforms its counterparts with 0.963 precision and 0.964 recall in the detection of fake sensing tasks.
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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 | 0.002 | 0.008 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 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 | 0.001 |
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".