A hybrid mobile call fraud detection model using optimized fuzzy C-means clustering and group method of data handling-based network
Bibliographic record
Abstract
A novel two-stage fraud detection system in mobile telecom networks has been presented in this paper that identifies the malicious calls among the normal ones in two stages. Initially, a genetic algorithm-based optimized fuzzy c-means clustering is applied to the user’s historical call records for constructing the calling profile. Thereafter, the identification of the fraudulent calls occurs in two stages. In the first stage, each incoming call is passed to the clustering module that identifies the call as genuine, malicious or suspicious. This is done by comparing the distance value of the new calling instance from the profile cluster centers against two predefined threshold values. The calls detected as genuine or malicious are not further processed. However, the call records that are found to be suspicious are additionally scrutinized in the second stage by a previously trained group method of data handling model for final decision making. The legitimate and forged labeled call records generated out of the clustering module are utilized for training the supervised classifier. Experimentation is done on a real-world call dataset to exhibit the effectiveness of the proposed model. A comparative analysis of the current approach with one of our earlier propositions and another recent fraud detection system clearly illustrates the efficacy of the developed model.
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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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.002 | 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".