Abstract: Advanced Persistent Threat (APT) attacks in the Wide-Area Internet of Things (IoT) are characterized by their covert nature, often targeting vulnerabilities lacking publicly available historical data. This makes it difficult to perform feature detection based on existing vulnerability signature databases, thereby reducing recognition accuracy. To address this issue, this paper proposes an intrinsic stereo recognition algorithm for wide-area IoT APT attacks based on latent feature inference. First, cluster analysis is performed on real-time call sequences in the wide-area IoT to extract the vulnerability feature distribution matrix of unlabeled attack data from the feature database. Combined with linear transformation, APT attack features are detected. Next, based on the extracted APT attack features, a latent attack recognition model for APT is constructed using a latent feature inference function. Finally, the intrinsic entity variation coefficient of the APT attack is calculated from the multi-stage contextual information of the attack data and integrated with the recognition model to achieve intrinsic stereo recognition of APT attacks. Experimental results demonstrate that the proposed method maintains the false detection rate of wide-area IoT APT attack feature detection below 20%, while achieving high recognition accuracy.