  The Internet is a complex network of interconnected routers and the existence
of collective behavior such as congestion suggests that the correlations
between different connections play a crucial role. It is thus critical to
measure and quantify these correlations. We use methods of random matrix theory
(RMT) to analyze the cross-correlation matrix C of information flow changes of
650 connections between 26 routers of the French scientific network `Renater'.
We find that C has the universal properties of the Gaussian orthogonal ensemble
of random matrices: The distribution of eigenvalues--up to a rescaling which
exhibits a typical correlation time of the order 10 minutes--and the spacing
distribution follow the predictions of RMT. There are some deviations for large
eigenvalues which contain network-specific information and which identify
genuine correlations between connections. The study of the most correlated
connections reveals the existence of `active centers' which are exchanging
information with a large number of routers thereby inducing correlations
between the corresponding connections. These strong correlations could be a
reason for the observed self-similarity in the WWW traffic.
