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Machine learning for network intrusion detection in Internet of Things systems

This study develops a machine learning approach for network intrusion detection in Internet of Things systems. Supervised classification models analyze network traffic to identify malicious activity while limiting false alarms. Evaluation compares detection accuracy, computational cost, and robustness across heterogeneous devices. Findings demonstrate the potential of efficient artificial intelligence methods to strengthen cybersecurity in resource constrained networks.