Applying Xfuzzy for the Development of Fuzzy Logic-Based Anomaly Detection Systems in Network Security: Moroccan Agribusiness SME
Bibliographic record
Abstract
In this article, we present the development and implementation of a fuzzy logic-based anomaly detection system specifically tailored for a Moroccan agribusiness SME.The primary objective is to enhance network security by accurately identifying anomalous network activities that could indicate potential cyber threats.The study's practical case involves a detailed analysis of the SME's network, focusing on key segments such as the Office LAN, Production Network, External WAN, and ERP System.Our methodology includes collecting and preprocessing network traffic data, designing the Fuzzy Inference System (FIS) using Xfuzzy, constructing a comprehensive rule base, and validating the system through simulation.The results indicate the system's effectiveness in detecting network anomalies.This study underscores the potential of fuzzy logic systems in enhancing network security for agribusiness SMEs, providing a robust framework for anomaly detection.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".