Novel machine learning techniques such as convolutional neural networks or deep learning allow to tackle realworld problems. At the same time local computer networks and the Internet face threats unseen before. With emerging machine learning paradigms we can detect and counteract them. We can also approach identifying software defects and hardware faults in networks as well as seasonality of the traffic. The Session will provide an opportunity for researchers working with machine learning to showcase their developments in the field computer network traffic analysis and will be a forum to bring these two areas of research together and elicit fruitful discussions.
The scope of the MLCN 2017 includes, but is not limited to the following topics:
anomaly detection,
intrusion detection,
traffic classification,
web services security,
software defects and hardware faults in networks,
traffic prediction (seasonality),
user profiling,
traffic log analysis,
securing the Internet of Things.
Jul 03
2017
Jul 05
2017
Draft paper submission deadline
Draft Paper Acceptance Notification
Registration deadline
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