Ensemble Learning Against Adversarial AI-driven Fake Task Submission in Mobile Crowdsensing
Bibliographic record
Abstract
Non-dedicated nature of mobile crowdsensing (MCS) systems introduces vulnerabilities for MCS platforms in terms of sensing, computing, storage, and battery resources. The advent of adversarial artificial intelligence (AI) leads to high impact malicious behavior when adversaries aim to clog the resources of such a non-dedicated and ubiquitous system. This paper proposes an ensemble learning-based methodology for MCS platforms in order to mitigate the impacts of adversarial AI-driven fake task submission attacks, which are intelligently designed so to clog resources such as batteries, sensing, or memory resources. We validate our proposal through realistic simulations to generate crowdsensing data under two different cities, and intelligent fake task submissions under adversarial self-organizing maps. The experimental results show that when the submitted tasks undergo a Gradient Boosting-based classifier prior to being assigned to participants, the proposed solution can introduce battery savings at the participant devices up to 23%, and the impacted recruit population can be reduced from 24% to 6% whereas the defense mechanism can achieve an overall accuracy level above 98% concerning the legitimacy of the submitted 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 | 0.005 | 0.012 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.002 | 0.002 |
| 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".