AI-Powered Network Intrusion Detection Systems

This study aims at analyzing and outlining an AI-based NIDS design and comparing different implementation models to improve the current state of network protection. Current NIDS do not assist organizations against modern cyber threats hence relies on machine learning and deep learning for real-time protections. The detailed plan of work includes the use of signature and anomaly detection techniques in parallel and the use of ensemble technique for increasing the detectors capabilities and decrease the false positive rates. The study employs open datasets for training and benchmarking and establishes that deep learning models, including CNNs and RNNs, elicit improved results compared to more conventional machine learning models. The results show that the ensemble learning model, outperformed the other and thus emphasizes future work should consider the use of this model for detection of network intrusions. This study shows that netsec based on AI-powered NIDS can increase the opportunity to detect threats and effectively respond to incidents in realistic network contexts. Further research by designs will involve handling issues like model interpretability and updates to sustain reliability and scalability.