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

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...

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Vydáno v:Sensors (Basel, Switzerland) Ročník 22; číslo 1; s. 140
Hlavní autoři: Fatani, Abdulaziz, Dahou, Abdelghani, Al-qaness, Mohammed A. A., Lu, Songfeng, Abd Elaziz, Mohamed Abd
Médium: Journal Article
Jazyk:angličtina
Vydáno: Switzerland MDPI AG 26.12.2021
MDPI
Témata:
Accuracy
Aquila optimizer
Classification
Cybersecurity
Datasets
Deep learning
Exploitation
Feature selection
Internet of Things
intrusion detection system
Intrusion detection systems
Machine learning
Neural networks
Optimization algorithms
Optimization techniques
Support vector machines
sustainable computing
swarm Intelligence
intrusion detection system
swarm Intelligence
internet of things (IoT)
Aquila optimizer
sustainable computing
cybersecurity
feature selection
ISSN:1424-8220, 1424-8220
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Popis
Shrnutí: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.
Bibliografie:ObjectType-Article-1
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ISSN:1424-8220
1424-8220
DOI:10.3390/s22010140