Securing Online Job Platforms: A Distributed Framework for Combating Employment Fraud in the Digital Landscape
Bibliographic record
Abstract
Job scams have existed for a while; advancements in technology have made them more accessible and profitable.By creating fake company websites and posting spurious job listings on well-known online job forums, cybercriminals impersonate actual employers and deceptively interview application victims.They then proceed to request personal information or money from their victims.To effectively address this issue, this work proposes an efficient framework that combines distributed processing, big data analytics, and machine learning to detect such attacks and combat emerging cyber threats.The proposed model employs mining rules to identify and prevent cyber threats in real-time, thereby enhancing user safety and fostering trust in online platforms.The proposed model accurately and efficiently detects fraud by leveraging the power of distributed processing and machine learning.The proposed framework, which provides a reliable approach for identifying fraudulent activity in job postings, is a hybrid approach that incorporates two different analyzing methods based on the data volume and dimension.The first method utilizes Conventional Machine Learning (CML) algorithms, while the second leverages Distributed Machine Learning (DML) algorithms on a distributed platform.The decisionmaking process regarding CML or DML ultimately depends on the specific application requirements, including the data characteristics, the need for distributed computing, and the operating environment.While CML models are a suitable choice for small datasets, DML models are beneficial when working with big datasets.For CML algorithms, the results demonstrate the effectiveness of the Random Forest (RF) algorithm in detection tasks.On the other hand, when the DML algorithms are implemented and evaluated to assess their performance, the experiments confirm that the Distributed Random Forest (DRF) algorithm exhibits high performance in this context.This research contributes to ongoing efforts to strengthen cybersecurity measures on digital platforms, thereby creating a safer online environment for both users and organizations.
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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 | 0.004 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.004 |
| Open science | 0.002 | 0.002 |
| 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".