Research on Abnormal Behavior Detection Algorithm of Internet of Things Devices with Computer Vision Assistance

Convolutional Random Forest Vision (CRFV) is an innovative technology that cleverly incorporates Convolutional Neural networks from the field of Computer Vision (CV). The Random Forest (RF) algorithm in machine learning aims to provide a powerful and efficient solution for abnormal behavior detection of Internet of Things (IoT) devices. The CNN layer is responsible for front-end feature extraction and uses its depth structure to accurately capture complex patterns in image and video data, while RF integrates multiple decision trees at the back-end and uses its powerful classification and regression capabilities to classify and identify anomalies in high-dimensional features extracted by CNN. This not only makes full use of the advantages of CNN in processing visual data, but also plays into the advantages of RF in processing high-dimensional feature Spaces and reducing overfitting. It is found that CRFV not only enhances the recognition accuracy of complex and hidden abnormal behavior, but also improves the generalization ability of the model and reduces the false positive rate. It can not only effectively identify and warn the abnormal operation of IoT devices, but also provide advanced intelligent tools for maintaining network security, preventing failures and optimizing equipment management, and is an important step towards autonomous and intelligent IoT monitoring.