This study provides a comprehensive bibliometric analysis of research trends in malware detection and analysis within communication networks, with particular emphasis on the application of machine learning and federated learning techniques. Using Bibexcel and VOSviewer, a total of 2,915 research documents indexed in the Scopus database between 2008 and 2024 were systematically examined. The analysis explores publication trends, key contributing countries, frequently cited works, and core thematic areas in the field. Statistical findings reveal that concepts such as malware, machine learning, and malware detection dominate scholarly discussions, highlighting their central role in advancing detection frameworks. Moreover, the study identifies India, the United States, and China as the top three leading contributors in terms of research output, reflecting their growing academic and industrial engagement in cybersecurity innovation. Emerging trends such as federated learning indicate a strong research orientation toward privacy-preserving and decentralized approaches, which are becoming increasingly critical in large-scale and distributed communication systems. Overall, the study provides valuable insights into the intellectual structure and global research landscape of malware detection, offering guidance for future studies and the development of more robust, intelligent, and collaborative defense mechanisms against evolving cyber threats.