Optimized Machine Learning-Based Intrusion Detection System for Fog and Edge Computing Environment

As a new paradigm, fog computing (FC) has several characteristics that set it apart from the cloud computing (CC) environment. Fog nodes and edge computing (EC) hosts have limited resources, exposing them to cyberattacks while processing large streams and sending them directly to the cloud. Intrusio...

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Bibliographic Details
Published in:Electronics (Basel) Vol. 11; no. 19; p. 3007
Main Authors: Alzubi, Omar A., Alzubi, Jafar A., Alazab, Moutaz, Alrabea, Adnan, Awajan, Albara, Qiqieh, Issa
Format: Journal Article
Language:English
Published: Basel MDPI AG 01.10.2022
Subjects:
Algorithms
Cloud computing
Cyberterrorism
Data security
Datasets
Edge computing
Feature selection
Internet of Things
Intrusion detection systems
Machine learning
Mathematical optimization
Methods
Optimization algorithms
Optimization models
Particle swarm optimization
Prevention
Unmanned aerial vehicles
Jordan
ISSN:2079-9292, 2079-9292
Online Access:Get full text
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Summary:As a new paradigm, fog computing (FC) has several characteristics that set it apart from the cloud computing (CC) environment. Fog nodes and edge computing (EC) hosts have limited resources, exposing them to cyberattacks while processing large streams and sending them directly to the cloud. Intrusion detection systems (IDS) can be used to protect against cyberattacks in FC and EC environments, while the large-dimensional features in networking data make processing the massive amount of data difficult, causing lower intrusion detection efficiency. Feature selection is typically used to alleviate the curse of dimensionality and has no discernible effect on classification outcomes. This is the first study to present an Effective Seeker Optimization model in conjunction with a Machine Learning-Enabled Intrusion Detection System (ESOML-IDS) model for the FC and EC environments. The ESOML-IDS model primarily designs a new ESO-based feature selection (FS) approach to choose an optimal subset of features to identify the occurrence of intrusions in the FC and EC environment. We also applied a comprehensive learning particle swarm optimization (CLPSO) with Denoising Autoencoder (DAE) for the detection of intrusions. The development of the ESO algorithm for feature subset selection and the DAE algorithm for parameter optimization results in improved detection efficiency and effectiveness. The experimental results demonstrated the improved outcomes of the ESOML-IDS model over recent approaches.
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ISSN:2079-9292
2079-9292
DOI:10.3390/electronics11193007