Optimal Deep-Learning-Based Cyberattack Detection in a Blockchain-Assisted IoT Environment

The Internet of Things (IoT) is the most extensively utilized technology nowadays that is simple and has the advantage of replacing the data with other devices by employing cloud or wireless networks. However, cyber-threats and cyber-attacks significantly affect smart applications on these IoT platf...

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Bibliographic Details
Published in:Mathematics (Basel) 2023-10, Vol.11 (19), p.4080
Main Authors: Assiri, Fatmah Y., Ragab, Mahmoud
Format: Article
Language:English
Subjects:
Accuracy
Algorithms
Analysis
Artificial intelligence
Belief networks
Blockchain
Classification
Cryptography
Cybercrime
Cybersecurity
Cyberterrorism
Deep learning
Dung
Feature selection
Internet of Things
intrusion detection system
Intrusion detection systems
Machine learning
metaheuristics
Optimization
Security systems
Sensors
Telecommunication systems
Wireless networks
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Summary:The Internet of Things (IoT) is the most extensively utilized technology nowadays that is simple and has the advantage of replacing the data with other devices by employing cloud or wireless networks. However, cyber-threats and cyber-attacks significantly affect smart applications on these IoT platforms. The effects of these intrusions lead to economic and physical damage. The conventional IoT security approaches are unable to handle the current security problems since the threats and attacks are continuously evolving. In this background, employing Artificial Intelligence (AI) knowledge, particularly Machine Learning (ML) and Deep Learning (DL) solutions, remains the key to delivering a dynamically improved and modern security system for next-generation IoT systems. Therefore, the current manuscript designs the Honey Badger Algorithm with an Optimal Hybrid Deep Belief Network (HBA-OHDBN) technique for cyberattack detection in a blockchain (BC)-assisted IoT environment. The purpose of the proposed HBA-OHDBN algorithm lies in its accurate recognition and classification of cyberattacks in the BC-assisted IoT platform. In the proposed HBA-OHDBN technique, feature selection using the HBA is implemented to choose an optimal set of features. For intrusion detection, the HBA-OHDBN technique applies the HDBN model. In order to adjust the hyperparameter values of the HDBN model, the Dung Beetle Optimization (DBO) algorithm is utilized. Moreover, BC technology is also applied to improve network security. The performance of the HBA-OHDBN algorithm was validated using the benchmark NSLKDD dataset. The extensive results indicate that the HBA-OHDBN model outperforms recent models, with a maximum accuracy of 99.21%.
ISSN:2227-7390
2227-7390
DOI:10.3390/math11194080