TY  - EJOU
AU  - Ganapathiyappan, Kirubavathi 
AU  - Eswaramoorthy, Kiruba Marimuthu 
AU  - Shanthamani, Abi Thangamuthu 
AU  - Venugopal, Aksaya 
AU  - Iyyappan, Asita Pon Bhavya 
AU  - Manickam, Thilaga 
AU  - Rehman, Ateeq Ur 
AU  - Hamam, Habib 

TI  - Advanced AI-Driven Cybersecurity Solutions: Intelligent Threat Detection, Explainability, and Adversarial Resilience
T2  - Computers, Materials \& Continua

PY  - 2026
VL  - 86
IS  - 2
SN  - 1546-2226

AB  - The growing use of Portable Document Format (PDF) files across various sectors such as education, government, and business has inadvertently turned them into a major target for cyberattacks. Cybercriminals take advantage of the inherent flexibility and layered structure of PDFs to inject malicious content, often employing advanced obfuscation techniques to evade detection by traditional signature-based security systems. These conventional methods are no longer adequate, especially against sophisticated threats like zero-day exploits and polymorphic malware. In response to these challenges, this study introduces a machine learning-based detection framework specifically designed to combat such threats. Central to the proposed solution is a stacked ensemble learning model that combines the strengths of four high-performing classifiers: Random Forest (RF), Extreme Gradient Boosting (XGB), LightGBM (LGBM), and CatBoost (CB). These models operate in parallel as base learners, each capturing different aspects of the data. Their outputs are then refined by a Gradient Boosting Classifier (GBC), which serves as a meta-learner to enhance prediction accuracy. To ensure the model remains both efficient and effective, Principal Component Analysis (PCA) is applied to reduce feature dimensionality while preserving critical information necessary for malware classification. The model is trained and validated using the CIC-Evasive PDFMalware2022 dataset, which includes a wide range of both malicious and benign PDF samples. The results demonstrate that the framework achieves impressive performance, with 97.10% accuracy and a 97.39% F1-score, surpassing several existing techniques. To enhance trust and interpretability, the system incorporates Local Interpretable Model-agnostic Explanations (LIME), which provides user-friendly insights into the rationale behind each prediction. This research emphasizes how the integration of ensemble learning, feature reduction, and explainable AI can lead to a practical and scalable solution for detecting complex PDF-based threats. The proposed framework lays the foundation for the next generation of intelligent, resilient cybersecurity systems that can address ever-evolving attack strategies.
KW  - PDF malware; ensemble learning; stacking model; cybersecurity; adversarial assaults

DO  - 10.32604/cmc.2025.070067