Intrusion Detection System Based on Gradient Corrected Online Sequential Extreme Learning Machine

Abstract

Nowadays, Intrusion Detection System (IDS) is an active research topic with machine learning nature. A single-hidden layer feedforward neural network (SLFN) trained on the approach of extreme learning machine (ELM) is used for (IDS). The encouraging factors for its usage are its fast learning and supportability of sequential learning in its online sequential extreme learning machine (OSELM) variant. An issue with OSELM that has been addressed by researchers is its random weights nature of the input-hidden layer. Most approaches use the concept of metaheuristic optimisation for determining the optimal weights of OSELM and resolve the random weight. However, metaheuristic approaches require many trials to determine the optimal one. Hence, there is concern about the convergence aspect and speed. This article proposes a novel approach for finding the optimal weights of the input-hidden layer. This article presents an approach for an integration between OSELM and back-propagation designated as (OSELM-BP). After integration, BP changes the random weights iteratively and uses an iterated evaluation of the generated error for feedback correction of the weights. The approach is evaluated based on various scenarios of activation functions for OSELM on the one hand and the number of iterations for BP on the other. An extensive evaluation of the approach and comparison with the original OSELM reveal a superiority of OSELM-BP in reaching optimal accuracy with a small number of iterations.

Keywords

• Online sequential extreme learning machine (OSELM)
• intrusion detection system (IDS)
• back-propagation (BP)
• activation function