Abstract: Machine learning (ML) technologies have become foundational in critical domains such as cybersecurity, healthcare, and autonomous systems. However, their increasing reliance on large-scale training data exposes them to data poisoning attacks—where adversaries manipulate training inputs to degrade model performance. Such attacks can cause misclassifications with severe consequences, particularly in high-stakes environments. This study introduces a robust stacking ensemble framework for the detection and mitigation of data poisoning attacks. The proposed model integrates four supervised classifiers—K-Nearest Neighbors (KNN), Random Forest (RF), Decision Tree (DT), Gradient Boosting (GB), and Logistic Regression (LR)—within a meta-learning architecture to enhance detection accuracy and resilience. We evaluate the model on four benchmark cybersecurity datasets: UNSW-NB15, BotDroid, CTU-13, and CICIDS-2017, representing diverse attack scenarios and feature distributions. Poisoning is simulated at six intensities (0% to 25%) by injecting mislabeled or adversarial perturbed data. The ensemble model consistently outperforms individual classifiers across all datasets and poisoning levels. Notably, it achieves 99.56% accuracy and a 99.69% F1-score on CICIDS-2017 with 25% poisoning, and maintains 95.58% accuracy on the BotDroid dataset, where baseline models degrade significantly.

Keywords: Data Poisoning Attacks, Stacking Ensemble, Cybersecurity Datasets, Adversarial Robustness, Supervised Learning, Model Evaluation Metrics

Pages: 1-11