This paper proposes an AI-based intrusion detection method for the ITRI AI BOX information security application. The packets captured by AI BOX are analyzed to determine whether there are network attacks or abnormal traffic according to AI algorithms. Adjust or isolate some unnatural or harmful network data transmission behaviors if detected as abnormal. AI models are used to detect anomalies and allow or restrict data transmission to ensure the information security of devices. In future versions, it will also be able to intercept packets in the field of information technology (IT) and operational technology (OT). It can be applied to the free movement between heterogeneous networks to assist in data computation and transformation. This paper uses the experimental test to realize the intrusion detection method, hoping to add value to the AI BOX information security application. When IT and OT fields use AI BOX to detect intrusion accurately, it will protect the smart factory or hospital from abnormal traffic attacks and avoid causing system paralysis, extortion, and other dangers. We have built the machine learning model, packet sniffing functionality, and the operating system setting of the AI BOX environment. A public dataset has been used to test the model, and the accuracy has achieved 99\%, and the Yocto Project environment has been available in the AI Box and tested successfully.