Adversarial Machine Learning-Driven Fake Task Anticipation in Mobile Crowdsensing Systems
Bibliographic record
Abstract
Mobile Crowdsensing (MCS) enables access to distributed sensing sources of personalized devices so to support various smart services. Services and applications that can benefit from MCS are various such as transportation, health care, public safety, smart mobility and many others. Distributed nature and lack of a pre-established trust mechanism in MCS systems make them vulnerable in the presence of various threats that can be initiated by either sensing data providers or service requesters. While it is relatively easier to detect and eliminate false sensing data submissions via outlier detection, tasks that are submitted to keep the MCS servers and participating devices occupied and clogged are challenging since these attacks can be planned intelligently. In this paper, we investigate the potential of adversarial machine learning to anticipate fake / illegitimate task submissions to MCS systems. To this end, we empower a threat anticipation mechanism that leverages a Generative Adversarial Network (GAN) to inject adversarial samples of fake / illegitimate tasks to an MCS system. We evaluate the impact of GAN-driven attacks in terms of Adversarial Attack Success Rate (AASR) and Attack Severity (AS). Our numerical results show that the potential risk and severity of the offensive use of Machine Learning is significantly higher than an adversarial baseline that injects fake tasks solely based on random noise samples.
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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 | 0.003 | 0.010 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.002 | 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".