Advanced Feature Extraction and Selection Approach Using Deep Learning and Aquila Optimizer for IoT Intrusion Detection System.

Mohammed A A Al-Qaness,Mohamed Abd Abd Elaziz,Songfeng Lu,Abdulaziz Fatani,Abdelghani Dahou

doi:10.3390/s22010140

Abstract

Developing cyber security is very necessary and has attracted considerable attention from academy and industry organizations worldwide. It is also very necessary to provide sustainable computing for the the Internet of Things (IoT). Machine learning techniques play a vital role in the cybersecurity of the IoT for intrusion detection and malicious identification. Thus, in this study, we develop new feature extraction and selection methods and for the IDS system using the advantages of the swarm intelligence (SI) algorithms. We design a feature extraction mechanism depending on the conventional neural networks (CNN). After that, we present an alternative feature selection (FS) approach using the recently developed SI algorithm, Aquila optimizer (AQU). Moreover, to assess the quality of the developed IDS approach, four well-known public datasets, CIC2017, NSL-KDD, BoT-IoT, and KDD99, were used. We also considered extensive comparisons to other optimization methods to verify the competitive performance of the developed method. The results show the high performance of the developed approach using different evaluation indicators.

Highlights

Internet applications help people and society in many fields, including teaching, electronic commerce (EC), electronic learning, entertainment, electronic communication, and others [1]
A new approach was proposed for the internet of things (IoT) intrusion detection system (IDS)
We leveraged the advances of swarm intelligence (SI) and deep learning techniques

Summary

Introduction

Internet applications help people and society in many fields, including teaching, electronic commerce (EC), electronic learning, entertainment, electronic communication, and others [1]. Along with these applications, cybersecurity issues have been raised due to the vulnerability of the internet applications due to the wide expansion of the networks and the massive emergence of malicious intrusion [1]. AQU is a population-based optimization technique, similar to other metaheuristic (MH). In Equation (1), UBj and LBj represent limits of the search space. The AQU technique’s step is to do either exploration or exploitation until the best solution is found. There are two ways for exploration and exploitation, according to [23]

Methods

Results

Conclusion