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Computer networks are facing serious threats from the emergence of malware with sophisticated DGAs (Domain Generation Algorithms). This type of DGA malware dynamically generates domain names by concatenating words from dictionaries for evading detection. In this paper, we propose an approach for identifying the callback communications of such dictionary-based DGA malware by analyzing their domain names at the word level. This approach is based on the following observations: These malware families use their own dictionaries and algorithms to generate domain names, and accordingly, the word usages of malware-generated domains are distinctly different from those of human-generated domains. Our evaluation indicates that the proposed approach is capable of achieving accuracy, recall, and precision as high as 0.9989, 0.9977, and 0.9869, respectively, when used with labeled datasets. We also clarify the functional differences between our approach and other published methods via qualitative comparisons. Taken together, these results suggest that malware-infected machines can be identified and removed from networks using DNS queries for detected malicious domain names as triggers. Our approach contributes to dramatically improving network security by providing a technique to address various types of malware encroachment.