Soft-computing-based false alarm reduction for hierarchical data of intrusion detection system

Abstract

A false alarm rate of online anomaly-based intrusion detection system is a crucial concern. It is challenging to implement in the real-world scenarios when these anomalies occur sporadically. The existing intrusion detection system has been developed to limit or decrease the false alarm rate. However, the state-of-the-art approaches are attack or algorithm specific, which is not generic. In this article, a soft-computing-based approach has been designed to reduce the false-positive rate for hierarchical data of anomaly-based intrusion detection system. The recurrent neural network model is applied to classify the data set of intrusion detection system and normal instances for various subclasses. The designed approach is more practical, reason being, it does not require any assumption or knowledge of the data set structure. Experimental evaluation is conducted on various attacks on KDDCup’99 and NSL-KDD data sets. The proposed method enhances the intrusion detection systems that can work with data with dependent and independent features. Furthermore, this approach is also beneficial for real-life scenarios with a low occurrence of attacks.