Zero-Day Exploit Detection using Machine Learning

Uncovering and protecting against zero-day exploits has become one of the most critical challenges in modern cybersecurity, as such attacks exploit previously unknown software vulnerabilities for which no patches or signatures exist. This makes traditional security mechanisms and supervised machine learning models largely ineffective due to their limited ability to generalize across unseen attack patterns. Although deep learning–based solutions have demonstrated improved detection accuracy, they often introduce high computational overhead and lack interpretability, restricting their practical adoption. This work presents a unified methodology for zero-day exploit detection that leverages recent advancements in machine learning to achieve a balance between accuracy, efficiency, transparency, and cost-effectiveness. The proposed framework integrates unsupervised and semi-supervised learning techniques to model normal system behavior and identify anomalous deviations indicative of zero-day exploits, enabling effective detection without heavy reliance on labeled attack data. To further enhance robustness and adaptability, models trained on diverse data sources are combined using ensemble classifiers, whose outputs are subsequently fused within a deep learning architecture. This hybrid learning approach capitalizes on the complementary strengths of multiple learning paradigms, resulting in a comprehensive and scalable zero-day exploit detection system suitable for real-world cybersecurity environments.