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

Nedhal Ahmad Hamdi Qaiwmchi,Haleh Amintoosi,Amirhossein Mohajerzadeh

doi:10.1109/access.2020.3047933

Nedhal Ahmad Hamdi Qaiwmchi, Haleh Amintoosi + Show 1 more

Open Access

https://doi.org/10.1109/access.2020.3047933

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Journal: IEEE Access	Publication Date: Dec 29, 2020
Citations: 40	License type: CC BY 4.0

Affiliation: Ferdowsi University of Mashhad

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.

Highlights

Intrusion detection is the task of observing, analysing and identifying activities aiming to violate a network’s security policy
3- We evaluate the performance of the proposed method online sequential extreme learning machine (OSELM)-BP in terms of five activation functions, with and without relying on the characterisation model for setting the number of neurons and based on various numbers of iterations added to the BP, and we show its superiority in terms of reaching optimal performance more frequently than OSELM alone
This article has presented a novel variant of extreme learning machine to solve the problem of random weights in the input and hidden layer

Summary

Introduction

Intrusion detection is the task of observing, analysing and identifying activities aiming to violate a network’s security policy. The key success factor for identifying such activities relies on an appropriate monitoring of the network by diagnosing its usage chronically [1]. Organisations used specific authentication policies articulating various levels of accessing. The conventional approach used in the past to prevent suspicious activities depended on an authentication framework giving users restricted network access based on their role. Such approach does not guarantee full prevention of unauthorised activities, where violating a network’s privacy has become more advanced [2].

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