Zero-Day Threat Detection A Machine Learning Paradigm for Intrusion Prevention

In today's constantly changing cybersecurity world, the demand for sophisticated intrusion prevention systems (IPS) capable of detecting zero-day threats is greater than ever. Traditional intrusion prevention systems (IPS) that rely on signature-based detection approaches frequently fail to detect fresh assaults. To solve these problems, the study presents a new machine learning (ML) methodology for detecting zero-day threats within IPS. The proposed system uses powerful ML methods, including supervised and unsupervised learning techniques, to monitor network traffic patterns and detect anomalous activity that may indicate a danger. The system outperforms existing systems in terms of precision (0.92), recall (0.88), and false positive rates (0.05), due to a hybrid method that combines supervised and unsupervised learning. Performance research revealed considerable improvements in detection accuracy (98%), throughput capacity (100 Gbps), and resource efficiency. The proposed system is a proactive defensive mechanism against developing cyber threats, providing scalable and adaptable security for important assets in dynamic network environments.