Artificial Intelligence-Based Intrusion Detection for Internet of Things Networks

Abstract

The rapid expansion of Internet of Things (IoT) networks has created new opportunities for intelligent automation while simultaneously increasing cybersecurity risks. This study proposes an artificial intelligence-based intrusion detection framework for identifying malicious activities in IoT environments. The proposed approach utilizes machine learning techniques to analyze network traffic, device behavior, connection patterns, and communication anomalies. The system is designed to distinguish normal network activity from potential attacks such as denial-of-service, unauthorized access, and anomalous communication. The research evaluates the potential of intelligent detection models for improving the accuracy and responsiveness of IoT security systems. The proposed framework can support real-time monitoring while reducing dependence on conventional signature-based security mechanisms. The study highlights the importance of lightweight and adaptive security solutions for resource-constrained IoT devices and provides a foundation for developing more resilient intelligent network infrastructures.