Review on Multi-Level Ensemble and Transfer Learning Approaches for Cybersecurity Anomaly Detection

Abstract

This review paper critically analyzes and synthesizes existing research to evaluate the performance, limitations, and practical applicability of multi-level ensemble and transfer learning architectures for anomaly detection in cybersecurity systems. The study follows a systematic review approach, focusing on recent and relevant studies related to machine learning-based anomaly detection, ensemble learning methods, and transfer learning techniques applied to various cybersecurity datasets and environments. The selected literature is analyzed based on architectural design, learning models, performance metrics, and implementation challenges rather than proposing or developing a new model.The paper selection criteria include studies published in reputable journals and conferences that focus on cybersecurity anomaly detection using ensemble learning, transfer learning, or hybrid approaches, particularly those addressing real-world datasets and practical deployment scenarios.The findings from the reviewed literature consistently show that multi-level ensemble methods provide higher robustness and better detection accuracy than single-model and single-layer ensemble approaches, especially when dealing with noisy, high-dimensional, and imbalanced cybersecurity data. Transfer learning is found to significantly improve anomaly detection performance in data-scarce environments by enabling knowledge transfer from related domains, reducing training time, and enhancing generalization to unseen and novel attacks, the combined use of ensemble learning and transfer learning demonstrates notable improvements in reducing false positives and enhancing the detection of zero-day attacks and advanced persistent threats (Mukherji et al., 2004). Overall, the review highlights that integrating multi-level ensemble learning with transfer learning is a promising approach for developing adaptive, scalable, and resilient cybersecurity anomaly detection systems. However, challenges such as computational complexity, interpretability, and real-time deployment remain key areas for future research.