Ensemble Voting based Intrusion Detection Technique using Negative Selection Algorithm

Abstract

This paper proposes an Intrusion Detection Technique (IDT) using an Artificial Immune System (AIS) based on Negative Selection Algorithm (NSA) to distinguish the self and non-self (intrusion) in computer networks. The novelties of the work are 1) use of Stacked Autoencoders (SAEs) and random forest for dimensionality reduction of data, 2) use of AIS to exploit its feature like self-learning, distributed, self-adaption, self-regulation with self and non-self-distinguishing capability, 3) implementation of two algorithms i.e., NSA based on Cosine distance (NSA_CD) and NSA based on Pearson Distance (NSA_PD) to explore their intrusion detection capabilities, and iv) development of a new ensemble voting based Intrusion Detection Technique (IDT-NSAEV) to detect and test the anomalies in the system. The proposed IDT-NSAEV technique combines the power of NSA_CD, NSA_PD and NSA based on Euclidean distance (NSA_ED) algorithms to enhance the detection rate by reducing the false alarm rate. The performance of the proposed technique is tested on standard benchmark NSL-KDD dataset and the results are compared with the state-of-the-art techniques. The results are in the favour of the proposed technique.