Neural approach to detecting communication network events
The Internet and associated network technologies are an increasingly integral part of modern day working practices. With this increase in use comes an increase in dependence. For some time, commentators have noted that given the level of reliance on data networks, there is a paucity of monitoring tools and techniques to support them. As this area is addressed, more data regarding network performance becomes available. However, a need to automatically analyse and interpret this performance data now becomes imperative. The paper takes one-way latency as an example performance metric. The term ‘data exception’ is employed to describe delay data that is unusual or unexpected. Data exceptions can be used by network operators to assess the effect of network modifications, failures and usage, and can also help in the diagnosis of intermittent network faults. Automating the detection of data exceptions is a non-trivial process that is not well suited to a rule-based solution. The paper shows that data exceptions can be identified by the use of a two-stage approach. The Kolmogorov–Smirnov test can initially be applied to detect general changes in the delay distribution, a neural network can then be used to categorise the change. The approach is evaluated using a network simulation.
