Albert Bifet

Ubiquitous Artificial Intelligence and Dynamic Data Streams

Artificial Intelligence is leading to ubiquitous sources of Big Data arriving at high-velocity and in real-time. To effectively deal with it, we need to be able to adapt to changes in the distribution of the data being produced, and we need to do it using a minimum amount of time and memory. In this paper, we detail modern applications falling into this context, and discuss some state-of-the-art methodologies in mining data streams in real-time, and the open source tools that are available to do machine learning/data mining in real-time for this challenging setting.

Telemetry-based stream-learning of BGP anomalies

Recent technology evolution allows network equipments to continuously stream a wealth of telemetry information, which pertains to multiple protocols and layers of the stack, at a very fine spatialgrain and at high-frequency. Processing this deluge of telemetry data in real-time clearly offers new opportunities for network control and troubleshooting, but also poses serious challenges. We tackle this challenge by applying streaming machine-learning techniques to the continuous flow of control and data-plane telemetry data, with the purpose of real-time detection of BGP anomalies. In particular, we implement an anomaly detection engine that leverages DenStream, an unsupervised clustering technique, and apply it to features collected from a large-scale testbed comprising tens of routers traversed by 1 Terabit/sec worth of real application traffic. In spirit with the recent trend toward reproducibility of research results, we make our code, datasets and demo available as open source to the scientific community.