It has been nearly 20 years since the launch of the Leurré.com project, which deployed honeypots in over 30 countries to collect anomalous traffic data. Honeypots are a cybersecurity technology designed to attract attackers by intentionally exposing systems that appear vulnerable, enabling the observation and analysis of various malicious activities, such as unauthorized access or malware execution. Although the project is no longer active, the landscape of Internet usage has drastically changed since that time, with the emergence of a wide variety of applications. As a result, there remains a pressing need to continuously monitor and investigate security threats through large-scale honeypot deployments. In this research, in close collaboration with international research institutions such as Inria and DFKI, we aim to realize more secure network operations by addressing two key research questions: what the characteristics of newly observed anomalous traffic captured by large-scale honeypot deployments are, and what kinds of countermeasures can be developed to address these new forms of anomalous traffic. To achieve this goal, we observe the evolving security threats posed by the growing diversity of applications and build effective countermeasures using advanced machine learning and deep learning models that were not available at the time of the earlier project.