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Intrusion detection is a problem that has attracted a great deal of attention from computer scientists recently, due to the exponential increase in computer attacks in recent years. DARPA KDD Cup 99 is a standard dataset for classifying computer attacks, to which several machine learning techniques have been applied. In this paper, we describe the results obtained using functional networks–a paradigm that extends feedforward neural networks–and compare these to the results obtained for other techniques applied to the same dataset. Of particular interest is the capacity for generalization of the approach used.