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Machine Learning Computer Networks@ESANN 2020 : Machine Learning applied to Computer Networks - Special Session @ ESANN 2020


When Apr 22, 2020 - Apr 24, 2020
Where Bruges, Belgium
Submission Deadline Nov 25, 2019
Notification Due Jan 31, 2020
Categories    machine learning   computer networks   DNN   network management

Call For Papers


European Symposium on Artificial Neural Networks, Computational Intelligence and Machine Learning (ESANN)

Special Session: Machine Learning applied to Computer Networks



The 28th European Symposium on Artificial Neural Networks, Computational Intelligence and Machine Learning (ESANN) will take place in Bruges, Belgium from 22 to 24 April 2020. This event builds upon a very successful series of conferences organized each year since 1993. ESANN has become a major scientific event in the machine learning, computational intelligence and artificial neural networks fields over the years.

Special Session: Machine Learning applied to Computer Networks

The application of machine learning techniques in the area of computer networks is a very promising area of study. Steadily growing network traffic, especially in the Internet or in huge data centers, as well as the continuously increasing number of end points due to mobile devices and IoT applications, implies an enormous management and monitoring effort when designing and operating these networks. Network automation mechanisms based on, e.g., Software-Defined Networking (SDN), can leverage advanced telemetry solutions to allow fine-grained traffic management. Large scale data transfer and ever-growing bandwidths raise the demand for open-loop network management assistance or sustained closed-loop auto-remediation, self-healing or -optimization techniques.

On the machine learning side, challenges arise due to the inherent "big data" aspect of network traffic. Because of this, learning has to be conducted "on the fly" on streaming data, without storing any data at all. Learning is further complicated by the non-stationarity of the data which can produce the catastrophic forgetting effect to which DNNs are particularly vulnerable. Last but not least, the acquisition of sufficient amounts of good-quality training data is often difficult due to privacy protection issues, and the results of machine learning are often not generalizable because all networks and their users have strongly individual characteristics.

Topics of Interest:

We encourage submission of papers on novel methods or applications of machine learning for computer network management and monitoring, including but not limited to:

- machine learning for network automation and programmability in the data, control, management or knowledge plane cognitive/autonomic network management and monitoring
- machine learning in network fault, configuration, accounting, performance or security management
- big data and deep Learning Approaches for network traffic engineering and routing
- in-network computing using machine learning
- streaming (network) data processing by deep neural networks
- continual learning on network traffic data
- acquisition of training data from computer networks and generalization of results

Special Session Organizers:

Alexander Gepperth (University of Applied Sciences Fulda, Germany), Sebastian Rieger (University of Applied Sciences Fulda, Germany)

Instructions for Authors:

Papers must not exceed 6 pages, including figures and references.
Special session "Machine Learning applied to Computer Networks" must be identified on the paper submission form.
LaTeX and Word style files are available. Further information and submission requirements are provided on the conference page:

Important Dates:

Prospective authors are invited to submit their contributions before 18 November 2019.
Notification of acceptance: 31 January 2020
ESANN 2020 Conference dates: 22-24 April 2020, Bruges, Belgium

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