The last two decades witnessed tremendous advances in the Information and
Communications Technologies. Beside improvements in computational power and
storage capacity, communication networks carry nowadays an amount of data which
was not envisaged only few years ago. Together with their pervasiveness,
network complexity increased at the same pace, leaving operators and
researchers with few instruments to understand what happens in the networks,
and, on the global scale, on the Internet. Fortunately, recent advances in data
science and machine learning come to the rescue of network analysts, and allow
analyses with a level of complexity and spatial/temporal scope not possible
only 10 years ago. In my thesis, I take the perspective of an Internet Service
Provider (ISP), and illustrate challenges and possibilities of analyzing the
traffic coming from modern operational networks. I make use of big data and
machine learning algorithms, and apply them to datasets coming from passive
measurements of ISP and University Campus networks. The marriage between data
science and network measurements is complicated by the complexity of machine
learning algorithms, and by the intrinsic multi-dimensionality and variability
of this kind of data. As such, my work proposes and evaluates novel techniques,
inspired from popular machine learning approaches, but carefully tailored to
operate with network traffic