Resource awareness in computational intelligence

Roman V. Yampolskiy, University of Louisville
Leon Reznik, Rochester Institute of Technology
Mike Adams, Acme Packet, Inc.
Joshua Harlow, Yahoo Inc.
Dima Novikov, VirtualScopics, LLC


High learning and adaptation ability of intelligent agents based on artificial neural networks (ANNs) has made them a popular tool in design and implementation of intrusion detection systems (IDS). However, ANN might consume significant resources during their retraining because of network changes. The paper investigates the design of ANN structures that may reduce the resource consumption without a substantial performance degradation. It describes the results of empirical studies examining a variety of design solutions, such as the choice of the ANN architecture and its parameters, the choice of an ANN fully connected topology versus a partial connectivity and the IDS design in a form of a hierarchical system of heterogeneous ANN-based agents. The results are analysed and design recommendations are provided. The fully connected ANN structure optimised with genetic algorithms has been found to achieve the best performance, while partial connectivity might save resources without a significant sacrifice of possible accomplishments. © 2011 Inderscience Enterprises Ltd.