The Internet of Things (IoT) has witnessed unprecedented growth in recent years, connecting various devices and systems to the Internet, thus making them susceptible to security threats and cyber-attacks. To mitigate these threats, the development of robust and efficient security systems is paramount. This paper proposes a graph-based artificial intelligence approach for IoT network security and attack detection systems. We explore the application of graph theory and machine learning techniques to analyze network traffic patterns and detect potential security breaches. This approach leverages the power of graphs to represent and analyze complex relationships within IoT networks, enabling more accurate and timely threat detection.