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.

The exponential expansion of the Internet of Things (IoT), Industrial Internet of Things (IIoT), and Transportation Management of Things (TMoT) produces vast amounts of real-time streaming data. Ensuring system dependability, operational efficiency, and security depends on the identification of anomalies in these dynamic and resource-constrained systems. Due to their high computational requirements and inability to efficiently process continuous data streams, traditional anomaly detection techniques often fail in IoT systems. This work presents a resource-efficient adaptive anomaly detection model for real-time streaming data in IoT systems. Extensive experiments were carried out on multiple real-world datasets, achieving an average accuracy score of 96.06% with an execution time close to 7.5 milliseconds for each individual streaming data point, demonstrating its potential for real-time, resource-constrained applications. The model uses Principal Component Analysis (PCA) for dimensionality reduction and a Z-score technique for anomaly detection. It maintains a low computational footprint with a sliding window mechanism, enabling incremental data processing and identification of both transient and sustained anomalies without storing historical data. The system uses a Multivariate Linear Regression (MLR) based imputation technique that estimates missing or corrupted sensor values, preserving data integrity prior to anomaly detection. The suggested solution is appropriate for many uses in smart cities, industrial automation, environmental monitoring, IoT security, and intelligent transportation systems, and is particularly well-suited for resource-constrained edge devices.