Binary PSO and random forests algorithm for PROBE attacks detection in a network

During the past few years, huge amount of network attacks have increased the requirement of efficient network intrusion detection techniques. Different classification techniques for identifying various attacks have been proposed in the literature. In this paper we propose and implement a hybrid classifier based on binary particle swarm optimization (BPSO) and random forests (RF) algorithm for the classification of PROBE attacks in a network. PSO is an optimization method which has a strong global search capability and is used for fine-tuning of the features whereas RF, a highly accurate classifier, is used here for classification. We demonstrate the performance of our technique using KDD99Cup dataset. We also compare the performance of our proposed classifier with eight other well-known classifiers and the results show that the performance achieved by the proposed classifier is much better than the other approaches.