A novel intrusion detection system based on an optimal hybrid kernel extreme learning machine

Abstract

Intrusion detection is a challenging technology in the area of cyberspace security for protecting a system from malicious attacks. A novel accurate and effective misuse intrusion detection system that relies on specific attack signatures to distinguish between normal and malicious activities is therefore presented to detect various attacks based on an extreme learning machine with a hybrid kernel function (HKELM). First, the derivation and proof of the proposed hybrid kernel are given. A combination of the gravitational search algorithm (GSA) and differential evolution (DE) algorithm is employed to optimize the parameters of HKELM, which improves its global and local optimization abilities during prediction attacks. In addition, the kernel principal component analysis (KPCA) algorithm is introduced for dimensionality reduction and feature extraction of the intrusion detection data. Then, a novel intrusion detection approach, KPCA-DEGSA-HKELM, is obtained. The proposed approach is eventually applied to the classic benchmark KDD99 dataset, the real modern UNSW-NB15 dataset and the industrial intrusion detection dataset from the Tennessee Eastman process. The numerical results validate both the high accuracy and the time-saving benefit of the proposed approach.