A distributed hebb neural network for network anomaly detection

One of the most challenging problems in anomaly detection is to develop scalable algorithms which are capable of dealing with large audit data, network traffic data, or alter data. In this paper a distributed neural network based on Hebb rule is presented to improve the speed and scalability of inductive learning. The speed is improved by randomly splitting a large data set into disjoint subsets and each subset data is presented to an independent neural network, these networks can be trained in distributed and each one in parallel. The analysis of completeness and risk bounds of competitive Hebb learning proof that the distributed Hebb neural network can avoid the accuracy being degraded as compared to running a single algorithm with the entire data. The experiments are performed on the KDD'99 Data set, which is a standard intrusion detection benchmark. Comparisons with other approaches on the same benchmark demonstrate the effectiveness and applicability of the proposed method.