As an Internet infrastructure, DNS is rarely subjected to deep monitoring by firewalls, allowing hackers and Asia-Pacific Telecommunity (APT) organizations to exploit DNS covert tunnels for data theft or network control and posing a significant threat to network security. In response to the easily bypassed nature of existing detection methods and their weak generalization capabilities, this study enhances the characterization method of DNS traffic and introduces the pcap features extraction CNN-Transformer (PFEC-Transformer) model. This model uses characterized decimal numerical sequences as input, conducts local feature extraction through CNN modules, and then analyzes long-distance dependency patterns between local features by using the Transformer for classification. The research builds datasets by collecting internet traffic and data packets generated by various DNS covert tunnel tools and conducts generalization testing with publicly available datasets containing traffic from unknown tunneling tools. Experimental results demonstrate that the model achieves an accuracy of 99.97% on the testing dataset and 92.12% on the generalization testing dataset, effectively showcasing its exceptional performance in detecting unknown DNS covert tunnels.