In the management of large enterprise communication networks, it becomes difficult to detect and identify causes of abnormal change in traffic distributions when the underlying logical topology is dynamic. This paper describes a novel approach to abnormal network change detection by representing periodic observations of logical communications within a network as a time series of graphs. A number of graph distance measures are proposed to assess the difference between successive graphs and identify abnormal behaviour. Localisation techniques have also been described to show where in the network most change occurred.

Abstract. Consider a network of unreliable links, modelling for example a communication network. Estimating the reliability of the network -expressed as the probability that certain nodes in the network are connected -is a computationally difficult task. In this paper we study how the Cross-Entropy method can be used to obtain more efficient network reliability estimation procedures. Three techniques of estimation are considered: Crude Monte Carlo and the more sophisticated Permutation Monte Carlo and Merge Process. We show that the Cross-Entropy method yields a speed-up over all three techniques.

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This article presents Monte Carlo techniques for estimating network reliability+ For highly reliable networks, techniques based on graph evolution models provide very good performance+ However, they are known to have significant simulation cost+ An existing hybrid scheme~based on partitioning the time space! is available to speed up the simulations; however, there are difficulties with optimizing the important parameter associated with this scheme+ To overcome these difficulties, a new hybrid scheme~based on partitioning the edge set! is proposed in this article+ The proposed scheme shows orders of magnitude improvement of performance over the existing techniques in certain classes of network+ It also provides reliability bounds with little overhead+

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