AI-Enhanced Machine Learning Models for Intrusion Detection: A Sustainable Defense Against Zero-Day Threats

Abstract

The increased rate and complexity of cyberattacks, especially 0-days require a change in intrusion detection approaches. Some of the difficulties that traditional Intrusion Detection Systems (IDS) face in identifying new attack vectors is based on the fact that most signatures or learning models tend to be rigid and hence are unable to effectively detect the new attacks. The study is on an AI-augmented machine learning architecture, to provide sustainable and mutable defense mechanisms that can respond to zero-day intrusion. Combining the ideas of complex AI methods such as deep learning, as well as hybrid ensembles, into the life application of an IDS, we will present a model that will become concerned with a cross reference against time sensitive anomalies in real time and in a scalable way, as well, with a high level of accuracy.

The extensive assessment was carried out with benchmark sets of data like NSL-KDD and CICIDS2017. Our approach included a great deal of feature engineering, data normalization, and training convolutional neural networks (CNN), recurrent neural networks (RNN), and gradient boosting (XGBoost). The evaluation of the models was presented in terms of precision, recall, F1-score and false positive rates along with the emphasis on zero-day exploit detection. Findings showed that AI-augmented models are more accurate and higher-generalized than customary ML-founded IDSs, and the CNN-based construction performance created the best detection rates on unseen threats.

Besides technical performance, continuous learning and low computational overhead will enable sustainability in cybersecurity in the suggested model. Visualizations, of confusion matrices, performance bar charts and threat distribution pie charts, also confirm that our system works. This study presents opportunities of AI in revolutionizing the management of digital ecosystems and provides a practical model to deploy intelligent and real-time security infrastructure in contemporary network settings.