Abstract :
Summary form only given. Many key tasks in computer network monitoring, such as load balancing and the detection of anomalies (e.g., DoS attacks, link failures, and routing loops), fundamentally require a whole-network perspective - that is, methods of ´spatial´ traffic analysis. A key challenge to advances in this area is posed by the fact that the ´space´ in question, deriving from a network, is not some Euclidean sub-space but rather a graph. We focus on the problem of developing analyses sensitive to ´spatial´ scale and introduce a framework for graph-based wavelets. In conjunction, we also consider the important issue of characterizing the underlying ´spatial´ auto-correlation structure.
Keywords :
computer networks; graph theory; monitoring; telecommunication traffic; wavelet transforms; anomaly detection; computer network monitoring; computer network traffic data; graph-based wavelets; load balancing; spatial auto-correlation structure; spatial traffic analysis; Autocorrelation; Computer crime; Computer networks; Computerized monitoring; Condition monitoring; Failure analysis; Load management; Routing; Telecommunication traffic; Wavelet analysis;
