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- research-articleDecember 2023
Explainability-based Metrics to Help Cyber Operators Find and Correct Misclassified Cyberattacks
SAFE '23: Proceedings of the 2023 on Explainable and Safety Bounded, Fidelitous, Machine Learning for NetworkingPages 9–15https://doi.org/10.1145/3630050.3630177Machine Learning (ML)-based Intrusion Detection Systems (IDS) have shown promising performance. However, in a human-centered context where they are used alongside human operators, there is often a need to understand the reasons of a particular decision. ...
- research-articleDecember 2023
DataZoo: Streamlining Traffic Classification Experiments
SAFE '23: Proceedings of the 2023 on Explainable and Safety Bounded, Fidelitous, Machine Learning for NetworkingPages 3–7https://doi.org/10.1145/3630050.3630176The machine learning communities, such as those around computer vision or natural language processing, have developed numerous supportive tools and benchmark datasets to accelerate the development. In contrast, the network traffic classification field ...
- proceedingDecember 2023
SAFE '23: Proceedings of the 2023 on Explainable and Safety Bounded, Fidelitous, Machine Learning for Networking
It is with great pleasure that we welcome you to the 2023 ACM CoNEXT Workshop on 'Explainable and Safety Bounded, Fidelitous, Machine Learning for Networking' - SAFE'23. We are excited to be hosting the first edition of this workshop, and it brings us ...