Knowledge-Based Machine Learning Boosting for Adversarial Task Detection in Mobile Crowdsensing
Bibliographic record
Abstract
Mobile Crowdsensing (MCS) leverages Sensing as a Service paradigm to contribute to the Internet of Things ecosystems through non-dedicated sensing capabilities of smart mobile devices. Distributed and non-trusted nature of MCS systems are vulnerable against various threats for the devices, MCS platforms, as well as the participating devices that provide sensory data services. Out of the many threats, submission of fake tasks may lead to drained resources at the participating devices, and clogged sensing server resources at MCS platforms. In this paper, classical machine learning (ML) performance is boosted by knowledge-based methods and sequential feature selection which is proposed for the first time against fake tasks submission to MCS platforms. Prior Knowledge Input and Prior Knowledge Input with Difference exploit AdaBoost and Decision Tree methods as initial accuracy to improve the accuracy of learning the legitimacy of submitted tasks to MCS platforms. Moreover, Sequential Feature Selection is implemented to investigate further improvements for the detection of task legitimacy in MCS campaigns. Intelligently selected 5 features amongst 10 possible features and implementation of knowledge-based methods boost the accuracy of machine learning performance from 93.67% to 97.37% for AdaBoost, and from 92.28% to 97.58% for Decision Trees.
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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 | 0.010 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| 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 | 0.002 | 0.002 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.002 | 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".