Design and Implementation of AI and ML Driven Anomaly Detection and Prevention Techniques for Network Security

The exponential growth of computer networks, cloud infrastructures, and Internet-based services has significantly increased both the scale and sophistication of cyber threats. Traditional network security mechanisms such as firewalls, rule-based intrusion detection systems (IDS), and signature-based intrusion prevention systems (IPS) are increasingly inadequate for detecting modern attacks, particularly zero-day exploits, polymorphic malware, and advanced persistent threats. In this context, Artificial Intelligence (AI) and Machine Learning (ML) have emerged as powerful tools for building intelligent, adaptive, and automated network security solutions. This research paper presents a comprehensive design and implementation of AI and ML-driven anomaly detection and prevention techniques for network security. The proposed system integrates anomaly detection with automated prevention mechanisms, enabling real-time response actions such as traffic blocking, rate limiting, and alert generation. Extensive experimental analysis is carried out using accuracy, precision, recall, F1-score, confusion matrix, and ROC curves. Results demonstrate that AI and ML-based approaches significantly outperform traditional security systems by achieving higher detection accuracy and lower false-positive rates. The study concludes that intelligent anomaly detection frameworks are essential for next-generation network security and provides a foundation for future research in autonomous and explainable cyber defense systems.