Neural Network Approximation of Simulation-based IDS Fitness Evaluation

Abstract

Configuring intrusion detection systems (IDSs) in large networks may involve balancing multiple criteria, e.g. detection rate, number of probes, and power consumption at each node. The tradeoffs become particularly acute when the nodes are resource-constrained, as is often the case in the Internet of Things (IoT) networks. A genetic algorithm based optimisation approach is outlined to address this task. However, the fitness function is evaluated in part via a computationally expensive simulation. We show how a neural network, trained over a set of IDS configurations, can be used as a surrogate fitness function, providing better results more cheaply.