PACMNET, V2, CoNEXT4, December 2024 Editorial

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The Proceedings of the ACM on Networking (PACMNET) series showcases top-tier research in emerging computer networks and their applications. We welcome submissions introducing new technologies, innovative experiments, creative applications of networking technologies, and fresh insights gained through analysis. Supported by the ACM Special Interest Group on Communications and Computer Networks (SIGCOMM), the journal is backed by a distinguished Editorial Board composed of leading researchers in the field. This issue concludes the second volume of PACMNET which in total published 32 articles, with a growth of 25% compared to the first volume. This issue features 18 articles, all submitted by the June 2024 deadline when a total of 121 submissions were received. Each submission underwent a thorough review process involving over 80 Editors, coordinated by two Associate Editors. In the initial phase, every article received a minimum of three reviews. Following an online discussion, roughly half of the submissions were rejected, while the other half advanced to a second review phase. In this phase, Editors produced at least two additional reviews per article. After a further discussion and remote Editors' meeting, 18 articles were chosen for publication and appear in this issue. Topics include network measurement, security, and privacy of both traditional and novel networking technologies. From a methodological perspective, machine learning and artificial intelligence-based solutions are predominant. All papers include a thorough set of experiments to validate the proposed solutions. We would like to express our gratitude to all those who contributed to this issue of PACMNET, especially the Authors for submitting their finest work and the Associate Editors for offering valuable feedback in their reviews and engaging in the discussions. Our thanks also go to the SIGCOMM Executive Committee Chair and the CoNEXT Steering Committee members for their continued support and guidance, providing essential suggestions and insights throughout the article selection process.