AI Enhanced Intrusion Detection and Prevention Systems (IDS/IPS)

The increasing sophistication of cyber threats necessitates a move beyond traditional signature-based intrusion detection systems (IDS) toward more dynamic, data-driven approaches. This paper provides a comprehensive review of machine learning (ML) techniques for real-time network anomaly detection, a critical capability for responding to fast-moving attacks. We analyzed key ML paradigms, including supervised, unsupervised and semi-supervised learning, highlighting their trade-offs, such as the need for labeled data versus the ability to detect zero-day threats. A comparative analysis of traditional ML models (e.g., Random Forest, SVM) and deep learning (DL) architectures (e.g., CNN, LSTM, Autoencoder) reveals that DL models consistently offer superior performance in handling the high-dimensional, complex nature of modern network traffic, albeit with greater computational demands. Finally, we discuss advanced architectures and future research directions, including federated learning for its privacy-preserving and scalable nature and Explainable AI (XAI) for fostering trust and providing actionable insights to human security analysts. The paper concludes that the future of network security lies in the development of hybrid, continuously adaptive systems that balance performance, privacy and interpretability to effectively counter evolving cyber threats.