Cluster computing for neural network based anomaly detection

Network intrusion-detection systems are now being identified as a mandatory component in multilayered security architecture. Intrusion detection systems have traditionally been based on the characterization of a user and tracking of activity of the user to see if it matches that characterization. Artificial neural networks provide a feasible approach to model complex engineering systems such as intrusion detection. Applications of artificial neural networks to characterize the behavior of users have been well studied in the recent past, without considering the enormous time they take to get modeled. In this paper we present an implementation of a parallel version of the back propagation training algorithm for feed-forward neural networks that are used for detecting intruders based on the MPI standard on Linux PC clusters. The experiments show a considerable increase in speedup during training and testing of the neural network, which in turn increases the speed of detecting intruders.