Resilience, as an emerging research field, is strongly required as a core property of all kinds of critical systems. It describes the ability of a system to absorb shocks, recover promptly, adapt to maintain comparable functionality, and continuously improve by learning from such incidents. With the increasing complexity and heterogeneity of critical networks and systems, the scope of resilience is going beyond traditional fault tolerance and security paradigms and encompasses emerging requirements such as ultra-reliability, deterministic performance, trustworthiness, and privacy. This also necessitates advanced resilience measures, incorporating artificial intelligence (AI) and machine learning (ML) models, quantum security, real-time and distributed reconfiguration methods etc.

Accordingly, the Workshop on Resilient Networks and Systems targets new approaches for design, analysis, and evaluation of next-generation resilient networks and interconnected systems. It invites contributions for advanced techniques to overcome complex resilience challenges and to achieve various resilience objectives like dependability, security, robustness etc. The workshop spans diverse domains, including wired and wireless communication, Internet of Things (IoT), vehicular networks, cyber-physical systems, mobile edge computing, distributed operating systems, software-defined systems and beyond.

Topics of interest include, but are not limited to:

• Theoretical resilience frameworks and models
• Complex failure and threat models, and emerging resilience challenges
• Resilient hardware design (e.g., microprocessors, SoCs, sensors, quantum computer)
• Resilient software design (e.g., applications, middlewares, operating systems)
• Resilient virtualized systems (e.g., cloud/edge/fog computing paradigms, SOA)
• Resilient network technologies and paradigms (e.g., 5G/6G, TSN, network coding)
• Anomaly detection, prevention, prediction, and mitigation techniques
• System and network orchestration and (re)configuration schemes for resilience
• AI for resilience and resilience for AI (e.g., federated learning, explainable AI)
• Metrics and evaluation methods for qualitative and quantitative resilience analysis
• Case studies, testbeds, simulation frameworks, and prototypes