Topics of interest include but are not limited to:
- Data mining & visualization, statistical modeling, and big data analytics for networking data
- Frameworks or tools for data analytics or visualization for networking data
- Time series predictions for networking data such as traffic demands, failures, etc.
- AI/ML algorithms for anomaly detection and attack step prediction in network security
- Protocol design and optimization using ML/AI
- Deep learning and reinforcement learning in network control & management
- Resource allocation for virtualized networks using machine learning
- Machine learning & transfer learning for prediction of networking data & control decisions
- Practical implementations or experience with ML/AI in networking
- Self-learning and adaptive networking protocols and algorithms
- Self-X networks: Self-learning, self-driving, self-repairing, etc.
- New concepts like empowerment for quantifying and improving ML/AI-based concepts
Two forms of contribution are possible:
- Title, abstract and reference of previously published work to present and discuss it in the KuVS community.
- Title and abstract for a visionary talk, a project or teaching report, or presentation of original work.
- All contributions should be submitted as PDF documents. Submissions may be up to 2 pages long and should be formatted according to the IEEE conference layout.
- Link to submission system: https://easychair.org/conferences/?conf=kuvsfgmln2020
- Submission deadline: 14.08.2020
- Notification of acceptance: 21.08.2020
- Final submission/Camera-ready version and registration: 28.08.2020
- Workshop date: 08./09.10.2020