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

被引:10
作者
Assiri, Fatmah Y. [1 ]
Ragab, Mahmoud [2 ]
机构
[1] Univ Jeddah, Coll Comp Sci & Engn, Dept Software Engn, Jeddah 21493, Saudi Arabia
[2] King Abdulaziz Univ, Fac Comp & Informat Technol, Informat Technol Dept, Jeddah 21589, Saudi Arabia
关键词
Internet of Things; blockchain; cybersecurity; intrusion detection system; deep learning; metaheuristics; 68-11; INDUSTRIAL INTERNET;
D O I
10.3390/math11194080
中图分类号
O1 [数学];
学科分类号
0701 ; 070101 ;
摘要
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%.
引用
收藏
页数:16
相关论文
共 28 条
[1]   An Efficient Deep-Learning-Based Detection and Classification System for Cyber-Attacks in IoT Communication Networks [J].
Abu Al-Haija, Qasem ;
Zein-Sabatto, Saleh .
ELECTRONICS, 2020, 9 (12) :1-26
[2]   DRaNN_PSO: A deep random neural network with particle swarm optimization for intrusion detection in the industrial internet of things [J].
Ahmad, Jawad ;
Shah, Syed Aziz ;
Latif, Shahid ;
Ahmed, Fawad ;
Zou, Zhuo ;
Pitropakis, Nikolaos .
JOURNAL OF KING SAUD UNIVERSITY-COMPUTER AND INFORMATION SCIENCES, 2022, 34 (10) :8112-8121
[3]   An Ensemble Deep Learning-Based Cyber-Attack Detection in Industrial Control System [J].
Al-Abassi, Abdulrahman ;
Karimipour, Hadis ;
Dehghantanha, Ali ;
Parizi, Reza M. .
IEEE ACCESS, 2020, 8 :83965-83973
[4]   CNN-CNN: Dual Convolutional Neural Network Approach for Feature Selection and Attack Detection on Internet of Things Networks [J].
Alabsi, Basim Ahmad ;
Anbar, Mohammed ;
Rihan, Shaza Dawood Ahmed .
SENSORS, 2023, 23 (14)
[5]  
Alalayah K. M., 2023, Comput. Syst. Sci. Eng., V46, P3121
[6]   Intrusion Detection System to Advance Internet of Things Infrastructure-Based Deep Learning Algorithms [J].
Alkahtani, Hasan ;
Aldhyani, Theyazn H. H. .
COMPLEXITY, 2021, 2021
[7]   Blockchain-Assisted Cybersecurity for the Internet of Medical Things in the Healthcare Industry [J].
Alkatheiri, Mohammed Saeed ;
Alghamdi, Ahmed S. .
ELECTRONICS, 2023, 12 (08)
[8]  
Alohali MA, 2023, Comput Syst Sci Eng, V46, P3583
[9]  
Arya Laxmi, 2023, 2023 9th International Conference on Advanced Computing and Communication Systems (ICACCS), P834, DOI 10.1109/ICACCS57279.2023.10112989
[10]  
Asiri M.M., 2023, Comput. Syst. Sci. Eng, V45, P1679, DOI [10.32604/csse.2023.031063, DOI 10.32604/CSSE.2023.031063]