Toward effective network traffic analytics of mobile apps via deep learning
In recent years operators have experienced the tremendous growth of the traffic to be managed in their networks, whose heterogeneous composition (e.g. mobile/IoT devices, anonymity tools), dynamicity and increasing encryption is posing new challenges toward actionable network traffic analytics.
In this talk, the topics of network traffic classification and prediction generated by mobile applications will be covered, due to their beneficial use in network management, user-tailored experience and privacy. First, the reasoned use of Deep Learning umbrella will be introduced and explained in such context. Hence, lessons learned and common pitfalls will be highlighted. Subsequently, the adoption of sophisticated multi-modal multi-task architectures will be put forward. The objective is to devise AI-based traffic analysis tools able to capitalize the structured nature of traffic and able to support different objectives. The talk will also cover the current research done in the area of AI-based network traffic analysis at TRAFFIC group of University of Naples Federico II, Italy.