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Botnet attacks are a major threat to networked systems because of theirability to turn the network nodes that they compromise into additionalattackers, leading to the spread of high volume attacks over long periods. Thedetection of such Botnets is complicated by the fact that multiple network IPaddresses will be simultaneously compromised, so that Collective Classificationof compromised nodes, in addition to the already available traditional methodsthat focus on individual nodes, can be useful. Thus this work introduces acollective Botnet attack classification technique that operates on traffic froman n-node IP network with a novel Associated Random Neural Network (ARNN) thatidentifies the nodes which are compromised. The ARNN is a recurrentarchitecture that incorporates two mutually associated, interconnected andarchitecturally identical n-neuron random neural networks, that actsimultneously as mutual critics to reach the decision regarding which of nnodes have been compromised. A novel gradient learning descent algorithm ispresented for the ARNN, and is shown to operate effectively both withconventional off-line training from prior data, and with on-line incrementaltraining without prior off-line learning. Real data from a 107 node packetnetwork is used with over 700,000 packets to evaluate the ARNN, showing that itprovides accurate predictions. Comparisons with other well-known state of theart methods using the same learning and testing datasets, show that the ARNNoffers significantly better performance.

Associated Random Neural Networks for Collective Classification of Nodes in Botnet Attacks