Multivariate Bounded Support Kotz Mixture Model: Addressing Financial Fraud and Network Security Challenges
Bibliographic record
Abstract
With the rise of online shopping in recent years, the number of security threats has increased significantly, posing serious risks to the internet and computer networks. Due to the rapid development of electronic commerce and the Internet of Things, the number of credit card transactions has also grown rapidly, leading to a corresponding increase in security threats. Therefore, an effective fraud detection method is essential, as it can promptly identify fraudulent activity when a stolen card used. Intrusion Detection System (IDS) also plays a crucial role in identifying various types of cyberattacks. However, developing adaptive and flexible IDSs remains a challenging task due to the continual emergence of new attack types and evasion techniques. In this paper, we extend our recently proposed Multivariate Bounded Kotz Mixture Model (BKMM) and the model selection approach based on Minimum Message Length (MML) to two key security domains: credit card fraud detection and network intrusion detection. These applications not only demonstrate the practicality of our methods, but also validate the robustness and versatility of the BKMM and MML framework across diverse types of cybersecurity data. In BKMM, parameter estimation is performed by maximizing the loglikelihood using the Expectation-Maximization (EM) algorithm. To evaluate performance, we apply the models in both credit card fraud and network intrusion detection scenarios. Experimental results demonstrate that BKMM and MML effectively identify fraudulent transactions and cyber intrusions.
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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 | 0.011 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.002 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.003 |
| Open science | 0.003 | 0.002 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.001 | 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".