A genetic-fuzzy classification approach to improve high-dimensional intrusion detection system

Abstract

With the increasing number of attacks and growing scalability of connected networks over the past few years, researchers are brought to find other alternatives to judge the relevance, severity and correlation of network attacks. The high-dimensional intrusion detection system seems a promising dynamic protection component in security fields. In this work we propose an optimized classification scheme that coordinates several techniques for generating fuzzy association rules based on a large data set. Our main task is to ameliorate the detection rate of attacks in a real-time environment by using the one-versus-one decomposition to minimize as much as possible the false alarm rate. In addition, we aim to reduce the loss of knowledge through a suitable n-dimensional overlap function in order to model the conjunction in fuzzy rules to provide enough classification accuracy. We can also opt for the aggregation method to obtain the final decision. To evaluate the performance of our approach, an experimental study is performed so as to achieve relevant results. The final outcome shows that our approach outperforms other classifiers by providing the highest detection accuracy, a low false alarm rate and time consumption.