The rapid growth of smart network environments, cloud computing, and Internet of Things (IoT) devices has significantly increased the risk of cyber attacks. Traditional intrusion detection systems rely mainly on signature-based techniques, which are ineffective in detecting unknown or evolving threats. Artificial Intelligence (AI) and Machine Learning (ML) techniques provide advanced capabilities for analyzing large-scale network traffic and detecting malicious behavior.

This paper proposes an AI-enabled intrusion detection framework designed to enhance the security of smart network infrastructures. The proposed system utilizes machine learning algorithms to monitor network traffic, identify abnormal patterns, and classify potential intrusions. The framework consists of multiple stages including data collection, preprocessing, feature extraction, model training, and intrusion detection.

Experimental evaluation using benchmark datasets demonstrates that the AI-based model achieves improved detection accuracy and reduced false positive rates compared to conventional intrusion detection systems. The proposed framework provides an efficient and intelligent approach to securing modern smart network environments.