Accurate identification and categorization of network traffic is a crucial task in many network management and engineering activities, such as QoS evaluation, capacity planning and attack detection. Terabytes of data may be transferred through the core network of a typical ISP every day, and an exponential growth in traffic is expected in the short-term, with more than 50 billions of Internet-connected devices. This scenario represents a tough challenge for network data capture and analysis. An accurate and massively scalable platform for both online and offline characterization of network traffic pattern evolution is a key element in facing the challenge. Top applications benefitting from such a platform include proactive congestion control mechanisms and intrusion detection systems.\n\nTo this end, the ONTIC project proposes to design, develop and evaluate:

1) A novel architecture of massively-scalable online techniques able to (a) characterize network traffic data streams, identifying traffic pattern evolutions, and (b) proactively detect anomalies in real time at very high network speeds, i.e., hundreds of thousands of packets per second.

2) An innovative set of massively-scalable offline data mining techniques to characterize network traffic, exploiting big-data analytic approaches and cloud-based distributed computation paradigms on extremely large network traffic datasets.

ONTIC will integrate these techniques into an autonomous network traffic characterization platform, to be used as cornerstone of a new generation of scalable and proactive network management and analysis applications. Dissemination and adoption of project outcomes in other application domains (e.g. bioinformatics, medicine, physics, social sciences, and finances) will be fostered by releasing the code and documentation of the ONTIC framework to the public by means of popular open-source repositories.