HO-CER: Hybrid-optimization-based convolutional ensemble random forest for data security in healthcare applications using blockchain technology

Sahar Badri; Sahar Badri

doi:10.3934/era.2023278

Electronic Research Archive

2023, Volume 31, Issue 9: 5466-5484. doi: 10.3934/era.2023278

Previous Article Next Article

Research article Special Issues

HO-CER: Hybrid-optimization-based convolutional ensemble random forest for data security in healthcare applications using blockchain technology

Sahar Badri ^,

Department of Information Systems, College of Computer Sciences and Information Technology, King Abdulaziz University, Jeddah, Saudi Arabia

Received: 03 March 2023 Revised: 22 May 2023 Accepted: 12 July 2023 Published: 04 August 2023

The Internet of Things (IoT) plays a vital role in the rapid progression of healthcare diligence. In recent years, IoT has become one of the most significant sources in the medical domain, since physical devices collect essential patient information to share real-time data with medical practitioners via various sensors. Meanwhile, numerous existing intrusion detection techniques failed to meet the security needs to safeguard the patient data collected. If an attack or intrusion cannot be identified at a particular time, immeasurable damage will be developed, which will fail the system. Utilizing innovative and new technologies, namely Blockchain, edge computing, and machine learning, provides a powerful security solution to preserve the medical data of various patients. This paper proposes a modified convolutional ensemble random forest-based hybrid particle swarm (MCERF-HPS) approach to guarantee healthcare data security with the advancement of blockchain technology. The proposed MCERF-HPS-based intrusion detection system identifies and categorizes attacks and regular traffic in blockchain-based edge systems. In immediate response to the identification, the gateway devices in the network layer block the attack traffic within seconds, with fewer computing and processing abilities. Applying the detection mechanism at the edge layer close to the attack source provides a quick detection response and minimizes the workload of clouds. The proposed MCERF-HPS approach's ability to detect an intrusion is tested using the BoT-IoT database. The analytic result illustrates that the proposed MCERF-HPS approach achieves an improved attack detection accuracy of about 98.7% compared to other methods.
- blockchain,
- cybersecurity,
- modified convolutional neural network,
- ensemble random forest,
- hybrid particle swarm optimization,
- attack,
- intrusion detection
Citation: Sahar Badri. HO-CER: Hybrid-optimization-based convolutional ensemble random forest for data security in healthcare applications using blockchain technology[J]. Electronic Research Archive, 2023, 31(9): 5466-5484. doi: 10.3934/era.2023278

Related Papers:

Abstract

The Internet of Things (IoT) plays a vital role in the rapid progression of healthcare diligence. In recent years, IoT has become one of the most significant sources in the medical domain, since physical devices collect essential patient information to share real-time data with medical practitioners via various sensors. Meanwhile, numerous existing intrusion detection techniques failed to meet the security needs to safeguard the patient data collected. If an attack or intrusion cannot be identified at a particular time, immeasurable damage will be developed, which will fail the system. Utilizing innovative and new technologies, namely Blockchain, edge computing, and machine learning, provides a powerful security solution to preserve the medical data of various patients. This paper proposes a modified convolutional ensemble random forest-based hybrid particle swarm (MCERF-HPS) approach to guarantee healthcare data security with the advancement of blockchain technology. The proposed MCERF-HPS-based intrusion detection system identifies and categorizes attacks and regular traffic in blockchain-based edge systems. In immediate response to the identification, the gateway devices in the network layer block the attack traffic within seconds, with fewer computing and processing abilities. Applying the detection mechanism at the edge layer close to the attack source provides a quick detection response and minimizes the workload of clouds. The proposed MCERF-HPS approach's ability to detect an intrusion is tested using the BoT-IoT database. The analytic result illustrates that the proposed MCERF-HPS approach achieves an improved attack detection accuracy of about 98.7% compared to other methods.

References

[1]	A. Omran, M. Abouyoussef, M. Ismail, S. Bhatia, Sharded blockchain-based online diagnostic system for suspected patients during pandemics, in 2022 IEEE Wireless Communications and Networking Conference (WCNC), (2022), 2715−2720. https://doi.org/10.1109/WCNC51071.2022.9771790
[2]	G. N. Nguyen, N. H. Le Viet, M. Elhoseny, K. Shankar, B. B. Gupta, A. A. Abd El-Latif, Secure blockchain enabled cyber–physical systems in healthcare using deep belief networks with the ResNet model, J. Parallel Distrib. Comput., 153 (2021), 150−160. https://doi.org/10.1016/j.jpdc.2021.03.011 doi: 10.1016/j.jpdc.2021.03.011
[3]	A. Rehman, S. Abbas, M. A. Khan, T. M. Ghazal, K. M. Adnan, A. Mosavi, A secure healthcare 5.0 system based on blockchain technology entangled with federated learning technique, Comput. Biol. Med., 150 (2022), 106019. https://doi.org/10.1016/j.compbiomed.2022.106019 doi: 10.1016/j.compbiomed.2022.106019
[4]	A. Verma, V. Ranga, Machine learning-based intrusion detection systems for IoT applications, Wireless Pers. Commun., 111 (2020), 2287−2310. https://doi.org/10.1007/s11277-019-06986-8 doi: 10.1007/s11277-019-06986-8
[5]	A. Kore, S. Patil, IC-MADS: IoT-enabled cross-layer man-in-middle attack detection system for smart healthcare applications, Wireless Pers. Commun., 113 (2020), 727−746. https://doi.org/10.1007/s11277-020-07250-0 doi: 10.1007/s11277-020-07250-0
[6]	P. T. Nguyen, V. D. B. Huynh, K. D. Vo, P. T. Phan, M. Elhoseny, D. N. Le, Deep learning-based optimal multimodal fusion framework for intrusion detection systems for healthcare data, CMC-Comput. Mater. Continua, 66 (2021), 2555−2571. https://doi.org/10.32604/cmc.2021.012941 doi: 10.32604/cmc.2021.012941
[7]	M. Abouyoussef, S. Bhatia, P. Chaudhary, S. Sharma, M. Ismail, Blockchain-enabled online diagnostic platform of suspected Patients of COVID-19 like pandemics, IEEE Internet Things Mag., 4 (2021), 94−99. https://doi.org/10.1109/IOTM.1001.2100046 doi: 10.1109/IOTM.1001.2100046
[8]	S. Kumar, B. Bhushan, S. Bhatia, Blockchain-based big data solutions for internet of things (IoT) and smart cities, in New Trends and Applications in Internet of Things (IoT) and Big Data Analytics, Cham: Springer International Publishing, (2022), 225−253. https://doi.org/10.1007/978-3-030-99329-0_15
[9]	S. P. Ramu, P. K. R. Maddikunta, M. Parimala, S. Koppu, T. R. Gadekallu, C. L. Chowdhary, et al., An effective feature engineering for DNN using hybrid PCA-GWO for intrusion detection in IoMT architecture, Comput. Commun., 160 (2020), 139−149. https://doi.org/10.1016/j.comcom.2020.05.048 doi: 10.1016/j.comcom.2020.05.048
[10]	A. Singh, G. Sharma, R. Krishnamurthi, A. Kumar, S. Bhatia, A. Mashat, Cybersecurity for battlefield of things – a comprehensive review, J. Circuits, Syst. Comput., 31 (2020), 2230010. https://doi.org/10.1142/S0218126622300100 doi: 10.1142/S0218126622300100
[11]	M. A. Kumar, D. Samiayya, P. M. Vincent, K. Srinivasan, C. Y. Chang, H. Ganesh, A hybrid framework for intrusion detection in healthcare systems using deep learning, Front. Public Health, 9 (2022), 2295. https://doi.org/10.3389/fpubh.2021.824898 doi: 10.3389/fpubh.2021.824898
[12]	M. Rashid, J. Kamruzzaman, T. Imam, S. Wibowo, S. Gordon, A tree-based stacking ensemble technique with feature selection for network intrusion detection, Appl. Intell., 52 (2022), 1−14. https://doi.org/10.1007/s10489-021-02968-1 doi: 10.1007/s10489-021-02968-1
[13]	A. Abbas, M. A. Khan, S. Latif, M. Ajaz, A. A. Shah, J. Ahmad, A new ensemble-based intrusion detection system for the Internet of things, Arabian J. Sci. Eng., 47 (2022), 1805−1819. https://doi.org/10.1007/s13369-021-06086-5 doi: 10.1007/s13369-021-06086-5
[14]	E. Ashraf, N. F. Areed, H. Salem, E. H. Abdelhay, A. Farouk, Fidchain: Federated intrusion detection system for blockchain-enabled IoT healthcare applications, Healthcare, 10 (2022), 1110. https://doi.org/10.3390/healthcare10061110 doi: 10.3390/healthcare10061110
[15]	M. K. Chandol, M. K. Rao, Border collie cat optimization for intrusion detection system in healthcare IoT network using deep recurrent neural network, Comput. J., 65 (2022), 3181−3198. https://doi.org/10.1093/comjnl/bxab136 doi: 10.1093/comjnl/bxab136
[16]	M. Fouda, R. Ksantini, W. Elmedany, A novel intrusion detection system for internet of healthcare things based on deep subclasses dispersion information, IEEE Internet Things J., 10 (2023), 8395−8407. https://doi.org/10.1109/JIOT.2022.3230694 doi: 10.1109/JIOT.2022.3230694
[17]	S. Saif, P. Das, S. Biswas, M. Khari, V. Shanmuganathan, HIIDS: Hybrid intelligent intrusion detection system empowered with machine learning and metaheuristic algorithms for application in IoT-based healthcare, Microprocess. Microsyst., 2022 (2022), 104622. https://doi.org/10.1016/j.micpro.2022.104622 doi: 10.1016/j.micpro.2022.104622
[18]	S. Saif, K. Karmakar, S. Biswas, S. Neogy, MLIDS: Machine learning enabled intrusion detection system for health monitoring framework using BA-WSN, Int. J. Wireless Inf. Networks, 29 (2022), 491−502. https://doi.org/10.1007/s10776-022-00574-7 doi: 10.1007/s10776-022-00574-7
[19]	A. I. Taloba, A. Elhadad, A. Rayan, R. M. Abd El-Aziz, M. Salem, A. A. Alzahrani, et al., A blockchain-based hybrid platform for multimedia data processing in IoT-healthcare, Alexandria Eng. J., 65 (2023), 263−274. https://doi.org/10.1016/j.aej.2022.09.031 doi: 10.1016/j.aej.2022.09.031
[20]	P. Kanagala, Effective cyber security system to secure optical data based on deep learning approach for healthcare application, Optik, 272 (2023), 170315. https://doi.org/10.1016/j.ijleo.2022.170315 doi: 10.1016/j.ijleo.2022.170315
[21]	Y. Liu, W. Yu, Z. Ai, G. Xu, L. Zhao, Z. Tian, A blockchain-empowered federated learning in healthcare-based cyber physical systems, IEEE Trans. Network Sci. Eng., 2022 (2022). https://doi.org/10.1109/TNSE.2022.3168025 doi: 10.1109/TNSE.2022.3168025
[22]	Z. Hu, X. Zhu, Gesture detection from RGB hand image using modified convolutional neural network, in 2019 2nd International Conference on Information Systems and Computer Aided Education (ICISCAE), IEEE, (2019), 143−146. 10.1109/ICISCAE48440.2019.221606
[23]	W. Lin, Z. Wu, L. Lin, A. Wen, J. Li, An ensemble random forest algorithm for insurance big data analysis, IEEE Access, 5 (2017), 16568−16575. https://doi.org/10.1109/ACCESS.2017.2738069 doi: 10.1109/ACCESS.2017.2738069
[24]	W. Xing, Y. Bei, Medical health big data classification based on the KNN classification algorithm, IEEE Access, 8 (2019), 28808−28819. https://doi.org/10.1109/ACCESS.2019.2955754 doi: 10.1109/ACCESS.2019.2955754
[25]	X. Wang, B. Yu, A. Ma, C. Chen, B. Liu, Q. Ma, Protein–protein interaction site prediction by ensemble random forests with synthetic minority oversampling technique, Bioinformatics, 35 (2019), 2395−2402. https://doi.org/10.1093/bioinformatics/bty995 doi: 10.1093/bioinformatics/bty995
[26]	R. Wang, K. Hao, L. Chen, T. Wang, C. Jiang, A novel hybrid particle swarm optimization using adaptive strategy, Inf. Sci., 579 (2021), 231−250. https://doi.org/10.1016/j.ins.2021.07.093 doi: 10.1016/j.ins.2021.07.093
[27]	S. Lalwani, H. Sharma, S. C. Satapathy, K. Deep, J. C. Bansal, A survey on parallel particle swarm optimization algorithms, Arabian J. Sci. Eng., 44 (2019), 2899−2923. https://doi.org/10.1007/s13369-018-03713-6 doi: 10.1007/s13369-018-03713-6

Reader Comments

Your name:*

Email:*
© 2023 the Author(s), licensee AIMS Press. This is an open access article distributed under the terms of the Creative Commons Attribution License (http://creativecommons.org/licenses/by/4.0)