https://safeworkshop.github.io/ [1]

Deadline for submissions: September 9, 2023 (AoE)

Machine learning techniques are becoming increasingly popular in the
field of networking. It offers promising solutions for network
optimization, security, and management. However, the lack of
transparency and interpretability in machine learning models poses
challenges for understanding and trusting their decisions in critical
networking scenarios. Moreover, ensuring safety and reliability is of
utmost importance when deploying machine learning in real-world network
environments.

Control and decision-making algorithms are critical for the operation of
networks, hence we believe that the solutions should be safety bounded
and interpretable. Understanding the decisions and behaviors of machine
learning models is crucial for optimizing network performance, enhancing
security, and ensuring reliable network operations. This is a very
crucial topic which needs to be addressed, as network operators,
managers or administrators are reluctant to use ML in production
networks because of their critical and sensitive nature, e.g., as
outages and performance degradations can be very costly.

We invite original research contributions as well as position papers
addressing, but not limited to, the following topics:

– Explainable machine learning models for network performance
optimization
– Interpretable anomaly detection and intrusion detection in networking
systems
– Safety considerations and techniques for robust and reliable machine
learning in networking
– Fairness, accountability, and transparency in machine learning for
networking
– Hybrid models which combine formal methods and AI for explainability
– Explainable reinforcement learning for networking
– Explainable deep reinforcement learning for networking
– Safety bounded reinforcement learning for networking
– Explainable Graph neural networks for networking
– Explainable sequential decision-making
– Constraints-based explanations for networking
– Visualizations and tools for understanding and interpreting machine
learning models in networking
– Case studies and real-world applications of explainable and safety
bounded machine learning in networking
– Evaluation methods for explainable machine learning
– Fidelity of explainable machine learning methods

Submission procedure:

Papers should be submitted via https://conext23-safe.hotcrp.com [2]
for more details please see the webpage
https://safeworkshop.github.io/posts/submission/ [3]

Organising committee:

– Kamal Singh, University St-Etienne, France
– Abbas Bradai, University of Poitiers, France
– Pham Tran Anh Quang, Huawei Technologies, France
– Antonio Pescapè, University of Napoli Federico II, Italy
– Claudio Fiandrino, IMDEA Networks Institute, Madrid, Spain

Links: