Real-time intrusion detection with fuzzy genetic algorithm

Abstract

In this work, we consider network intrusion detection using fuzzy genetic algorithm to classify network attack data. Fuzzy rule is a machine learning algorithm that can classify network attack data, while a genetic algorithm is an optimization algorithm that can help finding appropriate fuzzy rule and give the best/optimal solution. In this paper, we consider both wellknown KDD99 dataset and our own network dataset. The KDD99 dataset is a benchmark dataset that is used in various researches while our network dataset is an online network data captured in actual network environment. We evaluate our IDS in terms of detection speed, detection rate and false alarm rate. From the experiment, we can detect network attack in real-time (or within 2-3 seconds) after the data arrives at the detection system. The detection rate of our algorithm is approximately over 97.5%.