A hybrid blockchain and machine learning approach for intrusion detection system in Industrial Internet of Things
The Industrial Internet of Things (IIoT) is a key component of Industry 4.0, which enables manufacturing to be automated and data collected in real-time. Edge IoT devices are subject to cybersecurity threats and unauthorised access. Decentralisation and resource limitations of IIoT often prevent tra...
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| Published in | Alexandria engineering journal Vol. 127; pp. 619 - 627 |
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| Main Authors | , , , , |
| Format | Journal Article |
| Language | English |
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Elsevier B.V
01.08.2025
Elsevier |
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| Abstract | The Industrial Internet of Things (IIoT) is a key component of Industry 4.0, which enables manufacturing to be automated and data collected in real-time. Edge IoT devices are subject to cybersecurity threats and unauthorised access. Decentralisation and resource limitations of IIoT often prevent traditional security mechanisms from addressing these threats. Intrusion detection systems (IDSs), which are used to detect intrusions in IIoT environments, are presented in this paper as hybrid machine learning-blockchain approaches. Blockchain technology ensures data integrity, secures communication, and prevents unauthorised modifications through the proposed system. To reduce false positives and improve threat detection accuracy, XGBoost is able to reduce the number of false positives. Using the BOT-IoT dataset, the model is demonstrated to be superior to conventional intrusion detection systems. This approach ensures enhanced security and trustworthiness of IIoT networks by offering a scalable, efficient, and secure solution. |
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| AbstractList | The Industrial Internet of Things (IIoT) is a key component of Industry 4.0, which enables manufacturing to be automated and data collected in real-time. Edge IoT devices are subject to cybersecurity threats and unauthorised access. Decentralisation and resource limitations of IIoT often prevent traditional security mechanisms from addressing these threats. Intrusion detection systems (IDSs), which are used to detect intrusions in IIoT environments, are presented in this paper as hybrid machine learning-blockchain approaches. Blockchain technology ensures data integrity, secures communication, and prevents unauthorised modifications through the proposed system. To reduce false positives and improve threat detection accuracy, XGBoost is able to reduce the number of false positives. Using the BOT-IoT dataset, the model is demonstrated to be superior to conventional intrusion detection systems. This approach ensures enhanced security and trustworthiness of IIoT networks by offering a scalable, efficient, and secure solution. |
| Author | Song, Wu Ren, Sheng Tan, Wenxue Peng, Yibo Zhu, Xiangyuan |
| Author_xml | – sequence: 1 givenname: Wu surname: Song fullname: Song, Wu email: songwu@huas.edu.cn organization: School of Computer and Electrical Engineering, Hunan University of Arts and Science, Changde, Hunan 415000, China – sequence: 2 givenname: Xiangyuan surname: Zhu fullname: Zhu, Xiangyuan email: swthesis@163.com organization: Hunan Provincial Key Laboratory of Network Investigational Technology, Hunan Police Academy, Changsha, Hunan 410138, China – sequence: 3 givenname: Sheng surname: Ren fullname: Ren, Sheng email: rensheng@huas.edu.cn organization: School of Computer and Electrical Engineering, Hunan University of Arts and Science, Changde, Hunan 415000, China – sequence: 4 givenname: Wenxue surname: Tan fullname: Tan, Wenxue email: twxpaper@huas.edu.cn organization: School of Computer and Electrical Engineering, Hunan University of Arts and Science, Changde, Hunan 415000, China – sequence: 5 givenname: Yibo surname: Peng fullname: Peng, Yibo email: hnwlpyb@huas.edu.cn organization: School of Computer and Electrical Engineering, Hunan University of Arts and Science, Changde, Hunan 415000, China |
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| Cites_doi | 10.1109/TIFS.2019.2936975 10.1109/TCE.2024.3351221 10.1109/ACCESS.2021.3095078 10.1109/ACCESS.2018.2799854 10.1109/CCGRID.2017.8 10.1016/j.compeleceng.2022.108379 10.1109/ACCESS.2019.2935142 10.1186/s42400-019-0038-7 10.1109/TEM.2019.2921736 10.3390/s22062112 10.1109/ACCESS.2020.3017891 10.3390/s22020572 10.3390/network3010006 10.1109/COMST.2015.2494502 10.3390/electronics9030521 10.34028/iajit/19/5/14 10.3390/su15119001 10.1186/s40854-016-0046-5 10.1109/TSMC.2020.3019272 10.1016/j.phycom.2020.101157 10.1016/j.jnca.2016.09.014 10.3390/en13153951 10.4018/JITR.2021070102 10.1016/j.future.2019.05.041 10.64179/3080-7549.1004 10.1016/j.future.2017.11.022 10.1016/j.inffus.2023.102002 10.1109/JIOT.2021.3125190 10.1016/j.jnca.2012.05.003 10.1109/TII.2020.3023430 10.1145/3291064.3291075 10.3390/s19143119 10.3390/electronics13040687 10.3390/s20164636 10.4018/IJDST.307900 |
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| Keywords | Industry 4.0 Intrusion Detection Systems Blockchain Industrial Internet of Things |
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| References | Gong, Lee (bib42) 2020; 9 Adhikari, Ramkumar (bib7) 2023; 3 Meng, Li, Zhu (bib29) 2020; 67 Ali (bib40) 2022; 22 AlKadi, Moustafa, Turnbull, Choo (bib22) 2019; 7 Khan (bib47) 2024; 101 Hu, Zhou, Tian, Hu, Junping (bib33) Apr. 2021; 51 Khraisat, Gondal, Vamplew, Kamruzzaman (bib17) 2019; 2 Almaiah, Ali, Hajjej, Pasha, Alohali (bib35) 2022; 22 Botello, Mesa, Rodríguez, Díaz-López, Nespoli, Mármol (bib39) 2020; 20 Khan, Salah (bib26) 2018; 82 Bobde, Narayanan, Jati, Raj, Cvitić, Peraković (bib5) 2024; 13 S. Axelsson, Intrusion detection systems: A survey and taxonomy, 2000, (Accessed: Nov. 29, 2024). [Online]. Available C.R. Meiners, J. Patel, E. Norige, E. Torng, and A.X. Liu, Fast regular expression matching using small {tcams} for network intrusion detection and prevention systems, in 19th USENIX Security Symposium (USENIX Security 10), 2010. (Accessed: Mar. 02, 2025). [Online]. Available Guezzaz, Azrour, Benkirane, Mohy-Eddine, Attou, Douiba (bib36) 2022; 19 S. Kim, B. Kim, and H.J. Kim, Intrusion Detection and Mitigation System Using Blockchain Analysis for Bitcoin Exchange, in Proceedings of the 2018 International Conference on Cloud Computing and Internet of Things, Singapore Singapore: ACM, 2018, pp. 40–44. Alkadi, Moustafa, Turnbull, Choo (bib18) 2020; 15 Buczak, Guven (bib13) 2016; 18 M. Sarhan, W.W. Lo, S. Layeghy, and M. Portmann, HBFL: A Hierarchical Blockchain-based Federated Learning Framework for a Collaborative IoT Intrusion Detection, 2022, arXiv. Alexopoulos, Vasilomanolakis, Ivánkó, Mühlhäuser (bib23) 2018; 10707 Singh (bib1) 2024 Bolat-Akça, Bozkaya (bib12) 2023 Xu (bib27) 2016; 2 Meng, Tischhauser, Wang, Wang, Han (bib24) 2018; 6 J.-H. Ahn, J.-Y. Kim, M. Im, J.-W. Han, and Y.-K. Choi, A nanogap-embedded nanowire field effect transistor for sensor applications: Immunosensor and humidity sensor, in Proceedings of the 14th International Conference on Miniaturized Systems for Chemistry and Life Sciences, 2010. (Accessed: Dec. 11, 2024). [Online]. Available Goel, Khatri, Gangu, Ayyagiri, Mokkapati, Hussien (bib34) 2024 Rani (bib10) 2023 Nguyen, Marchal, Miettinen, Fereidooni, Asokan, Sadeghi (bib46) 2019 M. Almehdhar et al., Deep learning in the fast lane: A survey on advanced intrusion detection systems for intelligent vehicle networks, in IEEE Open Journal of Vehicular Technology, 2024, (Accessed: Mar. 02, 2025). [Online]. Available Bhola (bib8) 2022 Zhang, Sun, Situ, Jiang, Xie (bib38) 2021; 9 Derhab (bib4) 2019; 19 M. Niedermaier, M. Striegel, F. Sauer, D. Merli, and G. Sigl, Efficient Intrusion Detection on Low-Performance Industrial IoT Edge Node Devices, 2019, arXiv. Li, Meng, Kwok, Ip (bib19) 2017; 77 Alshahrani, Khan, Rizwan, Reshan, Sulaiman, Shaikh (bib3) 2023; 15 Baraniak, Starzyński (bib32) 2020; 13 Bediya, Kumar (bib11) 2021; 14 Li, Wu, Song, Lu, Li, Zhao (bib44) 2021; 17 Kumar (bib9) 2020; 8 . Modi, Patel, Borisaniya, Patel, Patel, Rajarajan (bib21) 2013; 36 Gholami, Fakhari (bib31) 2017 Yu (bib41) 2022; 9 X. Liang, S. Shetty, D. Tosh, C. Kamhoua, K. Kwiat, and L. Njilla, ProvChain: A Blockchain-Based Data Provenance Architecture in Cloud Environment with Enhanced Privacy and Availability, in 2017 17th IEEE/ACM International Symposium on Cluster, Cloud and Grid Computing (CCGRID), Madrid, Spain: IEEE, May 2017, pp. 468–477. Rani, Garjola, Abbas (bib2) 2024; 1 Zhao, Yin, Shi, Xue (bib45) 2020; 42 Koroniotis, Moustafa, Sitnikova, Turnbull (bib43) 2019; 100 Hussain, Rani, Kumar, Chaudhary (bib20) 2022; 13 Gholami (10.1016/j.aej.2025.05.030_bib31) 2017 AlKadi (10.1016/j.aej.2025.05.030_bib22) 2019; 7 Alexopoulos (10.1016/j.aej.2025.05.030_bib23) 2018; 10707 Guezzaz (10.1016/j.aej.2025.05.030_bib36) 2022; 19 Rani (10.1016/j.aej.2025.05.030_bib2) 2024; 1 Bhola (10.1016/j.aej.2025.05.030_bib8) 2022 Alkadi (10.1016/j.aej.2025.05.030_bib18) 2020; 15 Meng (10.1016/j.aej.2025.05.030_bib24) 2018; 6 Alshahrani (10.1016/j.aej.2025.05.030_bib3) 2023; 15 Modi (10.1016/j.aej.2025.05.030_bib21) 2013; 36 Yu (10.1016/j.aej.2025.05.030_bib41) 2022; 9 Derhab (10.1016/j.aej.2025.05.030_bib4) 2019; 19 Li (10.1016/j.aej.2025.05.030_bib44) 2021; 17 Hussain (10.1016/j.aej.2025.05.030_bib20) 2022; 13 Zhao (10.1016/j.aej.2025.05.030_bib45) 2020; 42 Buczak (10.1016/j.aej.2025.05.030_bib13) 2016; 18 Bediya (10.1016/j.aej.2025.05.030_bib11) 2021; 14 10.1016/j.aej.2025.05.030_bib6 Kumar (10.1016/j.aej.2025.05.030_bib9) 2020; 8 Adhikari (10.1016/j.aej.2025.05.030_bib7) 2023; 3 Rani (10.1016/j.aej.2025.05.030_bib10) 2023 Gong (10.1016/j.aej.2025.05.030_bib42) 2020; 9 Goel (10.1016/j.aej.2025.05.030_bib34) 2024 10.1016/j.aej.2025.05.030_bib25 Khraisat (10.1016/j.aej.2025.05.030_bib17) 2019; 2 10.1016/j.aej.2025.05.030_bib28 Khan (10.1016/j.aej.2025.05.030_bib26) 2018; 82 Nguyen (10.1016/j.aej.2025.05.030_bib46) 2019 Botello (10.1016/j.aej.2025.05.030_bib39) 2020; 20 Singh (10.1016/j.aej.2025.05.030_bib1) 2024 Zhang (10.1016/j.aej.2025.05.030_bib38) 2021; 9 Meng (10.1016/j.aej.2025.05.030_bib29) 2020; 67 Baraniak (10.1016/j.aej.2025.05.030_bib32) 2020; 13 Khan (10.1016/j.aej.2025.05.030_bib47) 2024; 101 Li (10.1016/j.aej.2025.05.030_bib19) 2017; 77 Xu (10.1016/j.aej.2025.05.030_bib27) 2016; 2 Ali (10.1016/j.aej.2025.05.030_bib40) 2022; 22 Hu (10.1016/j.aej.2025.05.030_bib33) 2021; 51 10.1016/j.aej.2025.05.030_bib30 Bobde (10.1016/j.aej.2025.05.030_bib5) 2024; 13 Bolat-Akça (10.1016/j.aej.2025.05.030_bib12) 2023 Almaiah (10.1016/j.aej.2025.05.030_bib35) 2022; 22 10.1016/j.aej.2025.05.030_bib14 10.1016/j.aej.2025.05.030_bib15 10.1016/j.aej.2025.05.030_bib37 10.1016/j.aej.2025.05.030_bib16 Koroniotis (10.1016/j.aej.2025.05.030_bib43) 2019; 100 |
| References_xml | – start-page: 515 year: 2017 end-page: 535 ident: bib31 article-title: Support vector machine: principles, parameters, and applications publication-title: Handbook of Neural Computation – volume: 100 start-page: 779 year: 2019 end-page: 796 ident: bib43 article-title: Towards the development of realistic botnet dataset in the internet of things for network forensic analytics: Bot-iot dataset publication-title: Future Gener. Comput. Syst. – volume: 14 start-page: 20 year: 2021 end-page: 37 ident: bib11 article-title: A novel intrusion detection system for Internet of Things network security publication-title: J. Inf. Technol. Res. – volume: 9 start-page: 8154 year: 2022 end-page: 8167 ident: bib41 article-title: A blockchain-based Shamir’s threshold cryptography scheme for data protection in Industrial Internet of Things settings publication-title: IEEE Internet Things J. – volume: 9 start-page: 521 year: 2020 ident: bib42 article-title: BLOCIS: blockchain-based cyber threat intelligence sharing framework for sybil-resistance publication-title: Electronics – start-page: 756 year: 2019 end-page: 767 ident: bib46 article-title: DÏoT: A federated self-learning anomaly detection system for IoT publication-title: in 2019 IEEE 39th International Conference on Distributed Computing Systems (ICDCS), IEEE – volume: 20 start-page: 4636 year: 2020 ident: bib39 article-title: BlockSIEM: protecting smart city services through a blockchain-based and distributed SIEM publication-title: Sensors – volume: 19 start-page: 3119 year: 2019 ident: bib4 article-title: Blockchain and random subspace learning-based IDS for SDN-enabled industrial IoT security publication-title: Sensors – volume: 10707 start-page: 107 year: 2018 end-page: 118 ident: bib23 article-title: Towards Blockchain-Based Collaborative Intrusion Detection Systems, Critical Information Infrastructures Security publication-title: in Lecture Notes in Computer Science – reference: M. Sarhan, W.W. Lo, S. Layeghy, and M. Portmann, HBFL: A Hierarchical Blockchain-based Federated Learning Framework for a Collaborative IoT Intrusion Detection, 2022, arXiv. – reference: M. Almehdhar et al., Deep learning in the fast lane: A survey on advanced intrusion detection systems for intelligent vehicle networks, in IEEE Open Journal of Vehicular Technology, 2024, (Accessed: Mar. 02, 2025). [Online]. Available: – volume: 19 year: 2022 ident: bib36 article-title: A lightweight hybrid intrusion detection framework using machine learning for edge-based IIoT security publication-title: IAJIT – year: 2024 ident: bib1 article-title: Blockchain-based lightweight authentication protocol for next-generation trustworthy internet of vehicles communication publication-title: IEEE Trans. Consum. Electron. – reference: S. Axelsson, Intrusion detection systems: A survey and taxonomy, 2000, (Accessed: Nov. 29, 2024). [Online]. Available: – volume: 67 start-page: 1377 year: 2020 end-page: 1386 ident: bib29 article-title: Enhancing medical smartphone networks via blockchain-based trust management against insider attacks publication-title: IEEE Trans. Eng. Manag. – volume: 36 start-page: 42 year: 2013 end-page: 57 ident: bib21 article-title: A survey of intrusion detection techniques in Cloud publication-title: J. Netw. Comput. Appl. – start-page: 0672 year: 2023 end-page: 0677 ident: bib12 article-title: Software-Defined Intrusion Detection System for DDoS Attacks in IoT Edge Networks publication-title: 2023 IEEE Intl Conf on Dependable, Autonomic and Secure Computing, Intl Conf on Pervasive Intelligence and Computing, Intl Conf on Cloud and Big Data Computing, Intl Conf on Cyber Science and Technology Congress (DASC/PiCom/CBDCom/CyberSciTech), Abu Dhabi, United Arab Emirates – volume: 13 start-page: 3951 year: 2020 ident: bib32 article-title: Modeling the impact of electric vehicle charging systems on electric power quality publication-title: Energies – volume: 22 start-page: 572 year: 2022 ident: bib40 article-title: An industrial IoT-based blockchain-enabled secure searchable encryption approach for healthcare systems using neural network publication-title: Sensors – reference: M. Niedermaier, M. Striegel, F. Sauer, D. Merli, and G. Sigl, Efficient Intrusion Detection on Low-Performance Industrial IoT Edge Node Devices, 2019, arXiv. – volume: 8 start-page: 154166 year: 2020 end-page: 154185 ident: bib9 article-title: BlockEdge: blockchain-edge framework for industrial IoT networks publication-title: IEEE Access – volume: 9 start-page: 98630 year: 2021 end-page: 98638 ident: bib38 article-title: Federated transfer learning for IIoT devices with low computing power based on blockchain and edge computing publication-title: IEEE Access – volume: 101 year: 2024 ident: bib47 article-title: Fed-inforce-fusion: a federated reinforcement-based fusion model for security and privacy protection of IoMT networks against cyber-attacks publication-title: Inf. Fusion – reference: C.R. Meiners, J. Patel, E. Norige, E. Torng, and A.X. Liu, Fast regular expression matching using small {tcams} for network intrusion detection and prevention systems, in 19th USENIX Security Symposium (USENIX Security 10), 2010. (Accessed: Mar. 02, 2025). [Online]. Available: – volume: 15 start-page: 9001 year: 2023 ident: bib3 article-title: Intrusion detection framework for Industrial Internet of Things using software defined network publication-title: Sustainability – volume: 18 start-page: 1153 year: 2016 end-page: 1176 ident: bib13 article-title: A survey of data mining and machine learning methods for cyber security intrusion detection publication-title: IEEE Commun. Surv. Tutor. – volume: 7 start-page: 114607 year: 2019 end-page: 114618 ident: bib22 article-title: Mixture localization-based outliers models for securing data migration in cloud centers publication-title: IEEE Access – volume: 82 start-page: 395 year: 2018 end-page: 411 ident: bib26 article-title: IoT security: review, blockchain solutions, and open challenges publication-title: Future Gener. Comput. Syst. – start-page: 307 year: 2024 end-page: 313 ident: bib34 article-title: Secure Edge IoT Intrusion Detection Framework for Industrial IoT via Blockchain Integration publication-title: 2024 4th International Conference on Blockchain Technology and Information Security (ICBCTIS) – volume: 6 start-page: 10179 year: 2018 end-page: 10188 ident: bib24 article-title: When intrusion detection meets blockchain technology: a review publication-title: IEEE Access – volume: 2 start-page: 20 year: 2019 ident: bib17 article-title: Survey of intrusion detection systems: techniques, datasets and challenges publication-title: Cybersecur – volume: 2 start-page: 25 year: 2016 ident: bib27 article-title: Are blockchains immune to all malicious attacks? publication-title: Financ Innov. – year: 2023 ident: bib10 article-title: Federated learning-based misbehaviour detection for the 5G-enabled internet of vehicles publication-title: IEEE Trans. Consum. Electron. – volume: 77 start-page: 135 year: 2017 end-page: 145 ident: bib19 article-title: Enhancing collaborative intrusion detection networks against insider attacks using supervised intrusion sensitivity-based trust management model publication-title: J. Netw. Comput. Appl. – volume: 42 year: 2020 ident: bib45 article-title: Intelligent intrusion detection based on federated learning aided long short-term memory publication-title: Phys. Commun. – volume: 13 start-page: 687 year: 2024 ident: bib5 article-title: Enhancing Industrial IoT Network Security through Blockchain Integration publication-title: Electronics – reference: . – volume: 15 start-page: 1164 year: 2020 end-page: 1174 ident: bib18 article-title: An ontological graph identification method for improving localization of IP prefix hijacking in network systems publication-title: IEEE Trans. Inform. Forensic Secur – volume: 13 start-page: 1 year: 2022 end-page: 23 ident: bib20 article-title: A deep comprehensive research architecture, characteristics, challenges, issues, and benefits of routing protocol for vehicular ad-hoc networks publication-title: Int. J. Distrib. Syst. Technol. (IJDST) – reference: S. Kim, B. Kim, and H.J. Kim, Intrusion Detection and Mitigation System Using Blockchain Analysis for Bitcoin Exchange, in Proceedings of the 2018 International Conference on Cloud Computing and Internet of Things, Singapore Singapore: ACM, 2018, pp. 40–44. – volume: 51 start-page: 2187 year: Apr. 2021 end-page: 2198 ident: bib33 article-title: Decentralized consensus decision-making for cybersecurity protection in multimicrogrid systems publication-title: IEEE Trans. Syst. Man Cyber Syst. – volume: 17 start-page: 5615 year: 2021 end-page: 5624 ident: bib44 article-title: DeepFed: federated deep learning for intrusion detection in industrial cyber–physical systems publication-title: IEEE Trans. Ind. Inf. – volume: 3 start-page: 115 year: 2023 end-page: 141 ident: bib7 article-title: IoT and blockchain integration: applications, opportunities, and challenges publication-title: Network – reference: X. Liang, S. Shetty, D. Tosh, C. Kamhoua, K. Kwiat, and L. Njilla, ProvChain: A Blockchain-Based Data Provenance Architecture in Cloud Environment with Enhanced Privacy and Availability, in 2017 17th IEEE/ACM International Symposium on Cluster, Cloud and Grid Computing (CCGRID), Madrid, Spain: IEEE, May 2017, pp. 468–477. – year: 2022 ident: bib8 article-title: Quality-enabled decentralized dynamic IoT platform with scalable resources integration publication-title: IET Commun. – reference: J.-H. Ahn, J.-Y. Kim, M. Im, J.-W. Han, and Y.-K. Choi, A nanogap-embedded nanowire field effect transistor for sensor applications: Immunosensor and humidity sensor, in Proceedings of the 14th International Conference on Miniaturized Systems for Chemistry and Life Sciences, 2010. (Accessed: Dec. 11, 2024). [Online]. Available: – volume: 1 start-page: 53 year: 2024 end-page: 65 ident: bib2 article-title: A predictive IoT and cloud framework for smart healthcare monitoring using integrated deep learning model publication-title: NJF Intell. Eng. J. – volume: 22 start-page: 2112 year: 2022 ident: bib35 article-title: A lightweight hybrid deep learning privacy preserving model for FC-based Industrial Internet of medical things publication-title: Sensors – year: 2022 ident: 10.1016/j.aej.2025.05.030_bib8 article-title: Quality-enabled decentralized dynamic IoT platform with scalable resources integration publication-title: IET Commun. – ident: 10.1016/j.aej.2025.05.030_bib16 – volume: 15 start-page: 1164 year: 2020 ident: 10.1016/j.aej.2025.05.030_bib18 article-title: An ontological graph identification method for improving localization of IP prefix hijacking in network systems publication-title: IEEE Trans. Inform. Forensic Secur doi: 10.1109/TIFS.2019.2936975 – year: 2024 ident: 10.1016/j.aej.2025.05.030_bib1 article-title: Blockchain-based lightweight authentication protocol for next-generation trustworthy internet of vehicles communication publication-title: IEEE Trans. Consum. Electron. doi: 10.1109/TCE.2024.3351221 – ident: 10.1016/j.aej.2025.05.030_bib14 – volume: 9 start-page: 98630 year: 2021 ident: 10.1016/j.aej.2025.05.030_bib38 article-title: Federated transfer learning for IIoT devices with low computing power based on blockchain and edge computing publication-title: IEEE Access doi: 10.1109/ACCESS.2021.3095078 – start-page: 0672 year: 2023 ident: 10.1016/j.aej.2025.05.030_bib12 article-title: Software-Defined Intrusion Detection System for DDoS Attacks in IoT Edge Networks – volume: 6 start-page: 10179 year: 2018 ident: 10.1016/j.aej.2025.05.030_bib24 article-title: When intrusion detection meets blockchain technology: a review publication-title: IEEE Access doi: 10.1109/ACCESS.2018.2799854 – ident: 10.1016/j.aej.2025.05.030_bib25 doi: 10.1109/CCGRID.2017.8 – ident: 10.1016/j.aej.2025.05.030_bib37 doi: 10.1016/j.compeleceng.2022.108379 – start-page: 307 year: 2024 ident: 10.1016/j.aej.2025.05.030_bib34 article-title: Secure Edge IoT Intrusion Detection Framework for Industrial IoT via Blockchain Integration – volume: 10707 start-page: 107 year: 2018 ident: 10.1016/j.aej.2025.05.030_bib23 article-title: Towards Blockchain-Based Collaborative Intrusion Detection Systems, Critical Information Infrastructures Security – volume: 7 start-page: 114607 year: 2019 ident: 10.1016/j.aej.2025.05.030_bib22 article-title: Mixture localization-based outliers models for securing data migration in cloud centers publication-title: IEEE Access doi: 10.1109/ACCESS.2019.2935142 – year: 2023 ident: 10.1016/j.aej.2025.05.030_bib10 article-title: Federated learning-based misbehaviour detection for the 5G-enabled internet of vehicles publication-title: IEEE Trans. Consum. Electron. – volume: 2 start-page: 20 issue: 1 year: 2019 ident: 10.1016/j.aej.2025.05.030_bib17 article-title: Survey of intrusion detection systems: techniques, datasets and challenges publication-title: Cybersecur doi: 10.1186/s42400-019-0038-7 – volume: 67 start-page: 1377 issue: 4 year: 2020 ident: 10.1016/j.aej.2025.05.030_bib29 article-title: Enhancing medical smartphone networks via blockchain-based trust management against insider attacks publication-title: IEEE Trans. Eng. Manag. doi: 10.1109/TEM.2019.2921736 – volume: 22 start-page: 2112 issue: 6 year: 2022 ident: 10.1016/j.aej.2025.05.030_bib35 article-title: A lightweight hybrid deep learning privacy preserving model for FC-based Industrial Internet of medical things publication-title: Sensors doi: 10.3390/s22062112 – volume: 8 start-page: 154166 year: 2020 ident: 10.1016/j.aej.2025.05.030_bib9 article-title: BlockEdge: blockchain-edge framework for industrial IoT networks publication-title: IEEE Access doi: 10.1109/ACCESS.2020.3017891 – volume: 22 start-page: 572 issue: 2 year: 2022 ident: 10.1016/j.aej.2025.05.030_bib40 article-title: An industrial IoT-based blockchain-enabled secure searchable encryption approach for healthcare systems using neural network publication-title: Sensors doi: 10.3390/s22020572 – volume: 3 start-page: 115 issue: 1 year: 2023 ident: 10.1016/j.aej.2025.05.030_bib7 article-title: IoT and blockchain integration: applications, opportunities, and challenges publication-title: Network doi: 10.3390/network3010006 – volume: 18 start-page: 1153 issue: 2 year: 2016 ident: 10.1016/j.aej.2025.05.030_bib13 article-title: A survey of data mining and machine learning methods for cyber security intrusion detection publication-title: IEEE Commun. Surv. Tutor. doi: 10.1109/COMST.2015.2494502 – volume: 9 start-page: 521 issue: 3 year: 2020 ident: 10.1016/j.aej.2025.05.030_bib42 article-title: BLOCIS: blockchain-based cyber threat intelligence sharing framework for sybil-resistance publication-title: Electronics doi: 10.3390/electronics9030521 – start-page: 756 year: 2019 ident: 10.1016/j.aej.2025.05.030_bib46 article-title: DÏoT: A federated self-learning anomaly detection system for IoT – volume: 19 issue: 5 year: 2022 ident: 10.1016/j.aej.2025.05.030_bib36 article-title: A lightweight hybrid intrusion detection framework using machine learning for edge-based IIoT security publication-title: IAJIT doi: 10.34028/iajit/19/5/14 – volume: 15 start-page: 9001 issue: 11 year: 2023 ident: 10.1016/j.aej.2025.05.030_bib3 article-title: Intrusion detection framework for Industrial Internet of Things using software defined network publication-title: Sustainability doi: 10.3390/su15119001 – volume: 2 start-page: 25 issue: 1 year: 2016 ident: 10.1016/j.aej.2025.05.030_bib27 article-title: Are blockchains immune to all malicious attacks? publication-title: Financ Innov. doi: 10.1186/s40854-016-0046-5 – ident: 10.1016/j.aej.2025.05.030_bib6 – ident: 10.1016/j.aej.2025.05.030_bib15 – volume: 51 start-page: 2187 issue: 4 year: 2021 ident: 10.1016/j.aej.2025.05.030_bib33 article-title: Decentralized consensus decision-making for cybersecurity protection in multimicrogrid systems publication-title: IEEE Trans. Syst. Man Cyber Syst. doi: 10.1109/TSMC.2020.3019272 – volume: 42 year: 2020 ident: 10.1016/j.aej.2025.05.030_bib45 article-title: Intelligent intrusion detection based on federated learning aided long short-term memory publication-title: Phys. Commun. doi: 10.1016/j.phycom.2020.101157 – volume: 77 start-page: 135 year: 2017 ident: 10.1016/j.aej.2025.05.030_bib19 article-title: Enhancing collaborative intrusion detection networks against insider attacks using supervised intrusion sensitivity-based trust management model publication-title: J. Netw. Comput. Appl. doi: 10.1016/j.jnca.2016.09.014 – volume: 13 start-page: 3951 issue: 15 year: 2020 ident: 10.1016/j.aej.2025.05.030_bib32 article-title: Modeling the impact of electric vehicle charging systems on electric power quality publication-title: Energies doi: 10.3390/en13153951 – volume: 14 start-page: 20 issue: 3 year: 2021 ident: 10.1016/j.aej.2025.05.030_bib11 article-title: A novel intrusion detection system for Internet of Things network security publication-title: J. Inf. Technol. Res. doi: 10.4018/JITR.2021070102 – volume: 100 start-page: 779 year: 2019 ident: 10.1016/j.aej.2025.05.030_bib43 article-title: Towards the development of realistic botnet dataset in the internet of things for network forensic analytics: Bot-iot dataset publication-title: Future Gener. Comput. Syst. doi: 10.1016/j.future.2019.05.041 – ident: 10.1016/j.aej.2025.05.030_bib30 – volume: 1 start-page: 53 issue: 1 year: 2024 ident: 10.1016/j.aej.2025.05.030_bib2 article-title: A predictive IoT and cloud framework for smart healthcare monitoring using integrated deep learning model publication-title: NJF Intell. Eng. J. doi: 10.64179/3080-7549.1004 – volume: 82 start-page: 395 year: 2018 ident: 10.1016/j.aej.2025.05.030_bib26 article-title: IoT security: review, blockchain solutions, and open challenges publication-title: Future Gener. Comput. Syst. doi: 10.1016/j.future.2017.11.022 – volume: 101 year: 2024 ident: 10.1016/j.aej.2025.05.030_bib47 article-title: Fed-inforce-fusion: a federated reinforcement-based fusion model for security and privacy protection of IoMT networks against cyber-attacks publication-title: Inf. Fusion doi: 10.1016/j.inffus.2023.102002 – volume: 9 start-page: 8154 issue: 11 year: 2022 ident: 10.1016/j.aej.2025.05.030_bib41 article-title: A blockchain-based Shamir’s threshold cryptography scheme for data protection in Industrial Internet of Things settings publication-title: IEEE Internet Things J. doi: 10.1109/JIOT.2021.3125190 – volume: 36 start-page: 42 issue: 1 year: 2013 ident: 10.1016/j.aej.2025.05.030_bib21 article-title: A survey of intrusion detection techniques in Cloud publication-title: J. Netw. Comput. Appl. doi: 10.1016/j.jnca.2012.05.003 – volume: 17 start-page: 5615 issue: 8 year: 2021 ident: 10.1016/j.aej.2025.05.030_bib44 article-title: DeepFed: federated deep learning for intrusion detection in industrial cyber–physical systems publication-title: IEEE Trans. Ind. Inf. doi: 10.1109/TII.2020.3023430 – ident: 10.1016/j.aej.2025.05.030_bib28 doi: 10.1145/3291064.3291075 – volume: 19 start-page: 3119 issue: 14 year: 2019 ident: 10.1016/j.aej.2025.05.030_bib4 article-title: Blockchain and random subspace learning-based IDS for SDN-enabled industrial IoT security publication-title: Sensors doi: 10.3390/s19143119 – volume: 13 start-page: 687 issue: 4 year: 2024 ident: 10.1016/j.aej.2025.05.030_bib5 article-title: Enhancing Industrial IoT Network Security through Blockchain Integration publication-title: Electronics doi: 10.3390/electronics13040687 – volume: 20 start-page: 4636 issue: 16 year: 2020 ident: 10.1016/j.aej.2025.05.030_bib39 article-title: BlockSIEM: protecting smart city services through a blockchain-based and distributed SIEM publication-title: Sensors doi: 10.3390/s20164636 – volume: 13 start-page: 1 issue: 8 year: 2022 ident: 10.1016/j.aej.2025.05.030_bib20 article-title: A deep comprehensive research architecture, characteristics, challenges, issues, and benefits of routing protocol for vehicular ad-hoc networks publication-title: Int. J. Distrib. Syst. Technol. (IJDST) doi: 10.4018/IJDST.307900 – start-page: 515 year: 2017 ident: 10.1016/j.aej.2025.05.030_bib31 article-title: Support vector machine: principles, parameters, and applications |
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