Weighted feature extraction using a genetic algorithm for intrusion detection
Melanie Middlemiss, Grant Dick · 2004
The objective of this paper is to investigate the use of a genetic algorithm for weighted feature extraction with specific application to intrusion detection data. In order to achieve this, we have implemented a simple genetic algorithm which evolves weights for the features of the data set. A k-nearest neighbour classifier was used for the fitness function of the GA as well as to evaluate the performance of the new weighted feature set. The results shown in this paper indicate that evolving a weighted set of features for a particular class of data can provide an increase in intrusion detection accuracy.