Designing A Multi-Agent System for Malicious PDF Detection Using Machine Learning and Generative Artificial Intelligence
Year: 2025
Type: article
Abstract: The growing sophistication of cyber threats exploiting PDF files represents a significant challenge for modern cybersecurity systems. To address this issue, this paper introduces a modular Multi Agent System (MAS) designed for the detection of malicious PDF files. Within this architecture, one agent employs a Conditional Tabular GAN (CTGAN) to expand the training dataset and reduce class imbalance, while another agent integrates supervised machine learning models for classification. Six supervised learning models, namely Decision Tree, Random Forest, XGBoost, Support Vector Machine, Naïve Bayes, and Neural Network, are evaluated on the enriched dataset. Among them, XGBoost achieves the best performance. The MAS coordinates autonomous agents dedicated to dataset management, data augmentation, learning, decision making, and user interaction, ensuring flexibility and scalability under both standard and adversarial conditions. To support interpretability, SHAP analysis is applied during the evaluation phase to identify the features that most strongly influence model decisions. Taken together, the proposed system demonstrates a comprehensive, explainable, and adaptable framework that contributes to strengthening PDF malware detection in sensitive digital infrastructures.
DOI: 10.14419/s6dere11
Authors: Dr. Amadou Diabagate, Dr. Hambally Yacouba Yazid, Prof. Abdellah Azmani, Prof. Adama Coulibaly
Subfield: Signal Processing
Field: Computer Science
Domain: Physical Sciences
Open Access status: diamond
OpenAlex ID: W4416107482
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