Research Article

Blockchain-based Intelligent Monitored Security System for Detection of Replication Attack in the Wireless Healthcare Network

Jatinder Warraich
Charles Sturt University, Australia
Chetanpal Singh
Charles Sturt University, Australia
* Corresponding author
Prem Thapa
Charles Sturt University, Australia
DOI icon 10.24018/ejeng.2021.6.6.2599

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Submitted 2021-09-16
Published 2021-10-31

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Article Main Content

Wireless sensor networks have revolutionized the way healthcare works replacing the traditional methods with sensor-enabled IoT devices that help in monitoring the data. The data is collected by these sensors that are there on the body of the user, the data is transmitted over the network to the healthcare monitoring systems. The transmission follows the route of the wireless channel that is not secure as it can be accessed by legitimate as well as illegitimate users. These pose security threats; one such attack is a replication attack. This makes the replicas of the original node, replaces the data with the malicious content for attacking the system, and deploys the node back to the network making it difficult to detect. The aim of the work is to review the Blockchain-based intelligent monitored security system for the detection of replication attacks in the wireless healthcare network. The method used for review is the secondary research method. The main focus of the work is kept on the literature review for obtaining insights and knowledge. The results show that blockchain provides the required security to the data carried by the sensor-enabled IoT. The result contributed to the understanding of the different blockchain techniques in securing data. The system component is farmed in the work and verified in the results.

Keywords: blockchain, data analysis, distributed systems, healthcare system, predictive analysis, replication attack, secure data sharing, smart grid, traceability

References

  1. Abuelkhail, U. Baroudi, M. Raad, and T. Sheltami, “Internet of things for healthcare monitoring applications based on RFID clustering scheme,” Wireless Networks, vol. 27, no. 1, pp. 747–763, Nov. 2020, doi: 10.1007/s11276-020-02482-1.
     Google Scholar
  2. Y. Al-Handarish et al., “A Survey of Tactile-Sensing Systems and Their Applications in Biomedical Engineering,” Advances in Materials Science & Engineering, pp. 1–18, Jan. 2020, doi: 10.1155/2020/4047937.
     Google Scholar
  3. M. M. Alhejazi and R. M. A. Mohammad, “Enhancing the blockchain voting process in IoT using a novel blockchain Weighted Majority Consensus Algorithm (WMCA),” Information Security Journal: A Global Perspective, pp. 1–19, Jan. 2021, doi: 10.1080/19393555.2020.1869356.
     Google Scholar
  4. Barredo Arrieta et al., “Explainable Artificial Intelligence (XAI): Concepts, taxonomies, opportunities and challenges toward responsible AI,” Information Fusion, vol. 58, pp. 82–115, Jun. 2020, doi: 10.1016/j.inffus.2019.12.012.
     Google Scholar
  5. S. Balasubramanian, V. Shukla, J. S. Sethi, N. Islam, and R. Saloum, “A readiness assessment framework for Blockchain adoption: A healthcare case study,” Technological Forecasting and Social Change, vol. 165, p. 120536, Apr. 2021, doi: 10.1016/j.techfore.2020.120536.
     Google Scholar
  6. E. Bandara, D. Tosh, P. Foytik, S. Shetty, N. Ranasinghe, and K. De Zoysa, “Tikiri—Towards a lightweight blockchain for IoT,” Future Generation Computer Systems, vol. 119, pp. 154–165, Jun. 2021, doi: 10.1016/j.future.2021.02.006.
     Google Scholar
  7. U. A. Bhatti et al., “Predictive Data Modeling Using sp-kNN for Risk Factor Evaluation in Urban Demographical Healthcare Data,” Journal of Medical Imaging and Health Informatics, vol. 11, no. 1, pp. 7–14, Jan. 2021, doi: 10.1166/jmihi.2021.3313.
     Google Scholar
  8. M. Bhavin, S. Tanwar, N. Sharma, S. Tyagi, and N. Kumar, “Blockchain and quantum blind signature-based hybrid scheme for healthcare 5.0 applications,” Journal of Information Security and Applications, vol. 56, p. 102673, Feb. 2021, doi: 10.1016/j.jisa.2020.102673.
     Google Scholar
  9. M. Chen et al., “Blockchain-Enabled healthcare system for detection of diabetes,” Journal of Information Security and Applications, vol. 58, p. 102771, May 2021, doi: 10.1016/j.jisa.2021.102771.
     Google Scholar
  10. M. Ciampi, A. Esposito, F. Marangio, M. Sicuranza, and G. Schmid, “Modernizing Healthcare by Using Blockchain,” Studies in Big Data, pp. 29–67, Dec. 2020, doi: 10.1007/978-981-15-9547-9_2.
     Google Scholar
  11. J. Deng, J. Cai, M. U. Aftab, M. S. Khokhar, and R. Kumar, “Visual Features with Spatio-Temporal-Based Fusion Model for Cross-Dataset Vehicle Re-Identification,” Electronics, vol. 9, no. 7, p. 1083, Jul. 2020, doi: 10.3390/electronics9071083.
     Google Scholar
  12. S. Deokar, M. Mangla, and R. Akhare, “A Secure Fog Computing Architecture for Continuous Health Monitoring,” Fog Computing for Healthcare 4.0 Environments, pp. 269–290, Aug. 2020, doi: 10.1007/978-3-030-46197-3_11.
     Google Scholar
  13. R. K. Dwivedi, R. Kumar, and R. Buyya, “Gaussian Distribution-Based Machine Learning Scheme for Anomaly Detection in Healthcare Sensor Cloud,” International Journal of Cloud Applications and Computing, vol. 11, no. 1, pp. 52–72, Jan. 2021, doi: 10.4018/ijcac.2021010103.
     Google Scholar
  14. H. B. Elhadj, F. Sallabi, A. Henaien, L. Chaari, K. Shuaib, and M. Al Thawadi, “Do-Care: A dynamic ontology reasoning based healthcare monitoring system,” Future Generation Computer Systems, vol. 118, pp. 417–431, May 2021, doi: 10.1016/j.future.2021.01.001.
     Google Scholar
  15. L. Erhan et al., “Smart anomaly detection in sensor systems: A multi-perspective review,” Information Fusion, vol. 67, pp. 64–79, Mar. 2021, doi: 10.1016/j.inffus.2020.10.001.
     Google Scholar
  16. L. Fang, Y. Li, Z. Liu, C. Yin, M. Li, and Z. J. Cao, “A Practical Model Based on Anomaly Detection for Protecting Medical IoT Control Services Against External Attacks,” IEEE Transactions on Industrial Informatics, vol. 17, no. 6, pp. 4260–4269, Jun. 2021, doi: 10.1109/tii.2020.3011444.
     Google Scholar
  17. X. Guo, H. Lin, Y. Wu, and M. Peng, “A new data clustering strategy for enhancing mutual privacy in healthcare IoT systems,” Future Generation Computer Systems, vol. 113, pp. 407–417, Dec. 2020, doi: 10.1016/j.future.2020.07.023.
     Google Scholar
  18. M. Gupta, R. Jain, M. Kumari, and G. Narula, “Securing Healthcare Data by Using Blockchain,” Studies in Big Data, pp. 93–114, Dec. 2020, doi: 10.1007/978-981-15-9547-9_4.
     Google Scholar
  19. M. Ul Hassan, M. H. Rehmani, and J. Chen, “Differential privacy in blockchain technology: A futuristic approach,” Journal of Parallel and Distributed Computing, vol. 145, pp. 50–74, Nov. 2020, doi: 10.1016/j.jpdc.2020.06.003.
     Google Scholar
  20. Hasselgren, K. Kralevska, D. Gligoroski, S. A. Pedersen, and A. Faxvaag, “Blockchain in healthcare and health sciences—A scoping review,” International Journal of Medical Informatics, vol. 134, Feb. 2020, doi: 10.1016/j.ijmedinf.2019.104040.
     Google Scholar
  21. H. Huang, P. Zhu, F. Xiao, X. Sun, and Q. Huang, “A blockchain-based scheme for privacy-preserving and secure sharing of medical data,” Computers & Security, vol. 99, pp. 1–13, Dec. 2020, doi: 10.1016/j.cose.2020.102010.
     Google Scholar
  22. H. M. Hussien, S. M. Yasin, N. I. Udzir, M. I. H. Ninggal, and S. Salman, “Blockchain technology in the healthcare industry: Trends and opportunities,” Journal of Industrial Information Integration, vol. 22, Jun. 2021, doi: 10.1016/j.jii.2021.100217.
     Google Scholar
  23. S. Jain et al., “Internet of medical things (IoMT)-integrated biosensors for point-of-care testing of infectious diseases,” Biosensors and Bioelectronics, vol. 179, p. 113074, May 2021, doi: 10.1016/j.bios.2021.113074.
     Google Scholar
  24. M. A. Jan et al., “Security and blockchain convergence with Internet of Multimedia Things: Current trends, research challenges and future directions,” Journal of Network and Computer Applications, vol. 175, pp. 1–22, Feb. 2021, doi: 10.1016/j.jnca.2020.102918.
     Google Scholar
  25. D. Jones et al., “Design and Evaluation of Magnetic Hall Effect Tactile Sensors for Use in Sensorized Splints,” Sensors, vol. 20, no. 4, p. 1123, Feb. 2020, doi: 10.3390/s20041123.
     Google Scholar
  26. D. Kim, D. Jeong, and Y. Seo, “Automated composition and execution of web-based simulation systems through knowledge designing and reasoning,” Advanced Engineering Informatics, vol. 48, Apr. 2021, doi: 10.1016/j.aei.2021.101263.
     Google Scholar
  27. M. Kumar and S. Chand, “MedHypChain: A patient-centered interoperability hyperledger-based medical healthcare system: Regulation in COVID-19 pandemic,” Journal of Network and Computer Applications, vol. 179, pp. 1–14, Apr. 2021, doi: 10.1016/j.jnca.2021.102975.
     Google Scholar
  28. R. Kumar et al., “An Integration of blockchain and AI for secure data sharing and detection of CT images for the hospitals,” Computerized Medical Imaging and Graphics, vol. 87, pp. 1–16, Jan. 2021, doi: 10.1016/j.compmedimag.2020.101812.
     Google Scholar
  29. V. Suresh Kumar and C. Krishnamoorthi, “Development of electrical transduction based wearable tactile sensors for human vital signs monitor: Fundamentals, methodologies and applications,” Sensors and Actuators A: Physical, vol. 321, Apr. 2021, doi: 10.1016/j.sna.2021.112582.
     Google Scholar
  30. G. Latif and J. Alghazo, “IoT Cloud Based Rx Healthcare Expert System,” Fog Computing for Healthcare 4.0 Environments, pp. 251–265, Aug. 2020, doi: 10.1007/978-3-030-46197-3_10.
     Google Scholar
  31. S. Latif, Z. Idrees, J. Ahmad, L. Zheng, and Z. Zou, “A blockchain-based architecture for secure and trustworthy operations in the industrial Internet of Things,” Journal of Industrial Information Integration, vol. 21, p. 100190, Mar. 2021, doi: 10.1016/j.jii.2020.100190.
     Google Scholar
  32. X. Li, Y. Lu, X. Fu, and Y. Qi, “Building the Internet of Things platform for smart maternal healthcare services with wearable devices and cloud computing,” Future Generation Computer Systems, vol. 118, pp. 282–296, May 2021, doi: 10.1016/j.future.2021.01.016.
     Google Scholar
  33. J. Liang, Z. Qin, S. Xiao, J. Zhang, H. Yin, and K. Li, “Privacy-preserving range query over multi-source electronic health records in public clouds,” Journal of Parallel and Distributed Computing, vol. 135, pp. 127–139, Jan. 2020, doi: 10.1016/j.jpdc.2019.08.011.
     Google Scholar
  34. H. Liu and Y. Liu, “Construction of a Medical Resource Sharing Mechanism Based on Blockchain Technology: Evidence from the Medical Resource Imbalance of China,” Healthcare, vol. 9, no. 1, p. 52, Jan. 2021, doi: 10.3390/healthcare9010052.
     Google Scholar
  35. Meshram, R. W. Ibrahim, A. J. Obaid, S. G. Meshram, A. Meshram, and A. M. A. El-Latif, “Fractional chaotic maps based short signature scheme under human-centered IoT environments,” Journal of Advanced Research, pp. 1–10, Sep. 2020, doi: 10.1016/j.jare.2020.08.015.
     Google Scholar
  36. Minoli, “Positioning of Blockchain Mechanisms in IoT-powered Smart Home Systems: A Gateway-based Approach,” Internet of Things, p. 10, Nov. 2019, doi: 10.1016/j.iot.2019.100147.
     Google Scholar
  37. Mitra, A. Paul, and S. Chatterjee, “Machine Learning in Healthcare,” AI Innovation in Medical Imaging Diagnostics, pp. 37–60, 2021, doi: 10.4018/978-1-7998-3092-4.ch002.
     Google Scholar
  38. Musamih et al., “A Blockchain-Based Approach for Drug Traceability in Healthcare Supply Chain,” IEEE Access, vol. 9, pp. 9728–9743, 2021, doi: 10.1109/access.2021.3049920.
     Google Scholar
  39. S. N. Mohanty et al., “An efficient Lightweight integrated Blockchain (ELIB) model for IoT security and privacy,” Future Generation Computer Systems, vol. 102, pp. 1027–1037, Jan. 2020, doi: 10.1016/j.future.2019.09.050.
     Google Scholar
  40. L. Nayak and V. Jayalakshmi, “A Study of Securing Healthcare Big Data using DNA Encoding based ECC,” 2021 6th International Conference on Inventive Computation Technologies (ICICT), pp. 348–352, Jan. 2021, doi: 10.1109/icict50816.2021.9358546.
     Google Scholar
  41. N. N. Nortey, R. Pometsey, L. Asiedu, S. Iddi, and F. O. Mettle, “Anomaly Detection in Health Insurance Claims Using Bayesian Quantile Regression,” International Journal of Mathematics and Mathematical Sciences, vol. 2021, pp. 1–11, Feb. 2021, doi: 10.1155/2021/6667671.
     Google Scholar
  42. A. Omar, R. Jayaraman, M. S. Debe, K. Salah, I. Yaqoob, and M. Omar, “Automating Procurement Contracts in the Healthcare Supply Chain using Blockchain Smart Contracts,” IEEE Access, vol. 9, pp. 1–1, 2021, doi: 10.1109/access.2021.3062471.
     Google Scholar
  43. A. Onesimu, J. Karthikeyan, and Y. Sei, “An efficient clustering-based anonymization scheme for privacy-preserving data collection in IoT based healthcare services,” Peer-to-Peer Networking and Applications, pp. 1–21, Feb. 2021, doi: 10.1007/s12083-021-01077-7.
     Google Scholar
  44. S. Sadri, A. Shahzad, and K. Zhang, “Blockchain Traceability in Healthcare: Blood Donation Supply Chain,” 2021 23rd International Conference on Advanced Communication Technology (ICACT), pp. 119–126, Feb. 2021, doi: 10.23919/icact51234.2021.9370704.
     Google Scholar
  45. K. Sahu, G. K. Panda, and S. K. Das, “Rough Set Classifications and Performance Analysis in Medical Health Care,” Advances in Intelligent Systems and Computing, pp. 411–422, Nov. 2020, doi: 10.1007/978-981-15-6353-9_37.
     Google Scholar
  46. K. Sahu, G. K. Panda, and S. K. Das, “Rough Set Classifications and Performance Analysis in Medical Health Care,” Advances in Intelligent Systems and Computing, pp. 411–422, Nov. 2020, doi: 10.1007/978-981-15-6353-9_37.
     Google Scholar
  47. M. A. G. Santos, R. Munoz, R. Olivares, P. P. R. Filho, J. D. Ser, and V. H. C. de Albuquerque, “Online heart monitoring systems on the internet of health things environments: A survey, a reference model and an outlook,” Information Fusion, vol. 53, pp. 222–239, Jan. 2020, doi: 10.1016/j.inffus.2019.06.004.
     Google Scholar
  48. P. M. Shakeel, S. Baskar, H. Fouad, G. Manogaran, V. Saravanan, and C. E. Montenegro-Marin, “Internet of things forensic data analysis using machine learning to identify roots of data scavenging,” Future Generation Computer Systems, vol. 115, pp. 756–768, Feb. 2021, doi: 10.1016/j.future.2020.10.001.
     Google Scholar
  49. Sharma and A. P. Bhatt, “Quantum Cryptography for Securing IoT-Based Healthcare Systems,” Limitations and Future Applications of Quantum Cryptography, pp. 124–147, 2021, doi: 10.4018/978-1-7998-6677-0.ch007.
     Google Scholar
  50. Sharma, P. Singh, and G. Dar, “Artificial Intelligence and Machine Learning for Healthcare Solutions,” Data Analytics in Bioinformatics, pp. 281–291, Jan. 2021, doi: 10.1002/9781119785620.ch11.
     Google Scholar
  51. Sharma, J. Olson, A. Guha, and L. McDougal, “HOW blockchain will transform the healthcare ecosystem,” Business Horizons, vol. 1, no. 1, Feb. 2021, doi: 10.1016/j.bushor.2021.02.019.
     Google Scholar
  52. Sharma and S. Joshi, “Barriers to blockchain adoption in health-care industry: an Indian perspective,” Journal of Global Operations and Strategic Sourcing, vol. 14, no. 1, pp. 1–1, Jan. 2021, doi: 10.1108/jgoss-06-2020-0026.
     Google Scholar
  53. P. Sharma, R. Jindal, and M. D. Borah, “Healthify: A Blockchain-Based Distributed Application for Health care,” Studies in Big Data, pp. 171–198, Dec. 2020, doi: 10.1007/978-981-15-9547-9_7.
     Google Scholar
  54. S. Sharma and Y. K. Gupta, “Predictive analysis and survey of COVID-19 using machine learning and big data,” Journal of Interdisciplinary Mathematics, vol. 24, no. 1, pp. 175–195, Jan. 2021, doi: 10.1080/09720502.2020.1833445.
     Google Scholar
  55. P. G. Shynu, V. G. Menon, R. L. Kumar, S. Kadry, and Y. Nam, “Blockchain-based Secure Healthcare Application for Diabetic-Cardio Disease Prediction in Fog Computing,” IEEE Access, pp. 1–1, 2021, doi: 10.1109/ACCESS.2021.3065440.
     Google Scholar
  56. Soni and D. K. Singh, “Blockchain-based security & privacy for biomedical and healthcare information exchange systems,” Materials Today: Proceedings, Feb. 2021, doi: 10.1016/j.matpr.2021.02.094.
     Google Scholar
  57. S. Stavrotheodoros, N. Kaklanis, K. Votis, D. Tzovaras, and A. Astell, “A hybrid matchmaking approach in the ambient assisted living domain,” Universal Access in the Information Society, pp. 1–18, Feb. 2021, doi: 10.1007/s10209-020-00756-1.
     Google Scholar
  58. L. Sujaya and R. S. Bhaskar, “A Modelling of Context-Aware Elderly Healthcare Eco-System-(CA-EHS) Using Signal Analysis and Machine Learning Approach,” Wireless Personal Communications, pp. 1–16, Mar. 2021, doi: 10.1007/s11277-021-08341-2.
     Google Scholar
  59. L. Salvador-Carulla, “Use of the new paradigm of healthcare ecosystem research in mental health planning,” International Journal of Integrated Care, vol. 20, no. 3, p. 156, Feb. 2021, doi: 10.5334/ijic.s4156.
     Google Scholar
  60. Surantha, T. F. Lesmana, and S. M. Isa, “Sleep stage classification using extreme learning machine and particle swarm optimization for healthcare big data,” Journal of Big Data, vol. 8, no. 1, pp. 1–17, Jan. 2021, doi: 10.1186/s40537-020-00406-6.
     Google Scholar
  61. Tandon, A. Dhir, N. Islam, and M. Mäntymäki, “Blockchain in healthcare: A systematic literature review, synthesizing framework and future research agenda,” Computers in Industry, vol. 122, pp. 1–22, Nov. 2020, doi: 10.1016/j.compind.2020.103290.
     Google Scholar
  62. M. Tanniru, J. Niu, C. Feng, C. G. Duque, C. Lu, and H. Krishnan, “Incentives to Engage Blockchain and Ecosystem Actors,” Building Decentralized Trust, pp. 35–61, 2021, doi: 10.1007/978-3-030-54414-0_3.
     Google Scholar
  63. Tolba and Z. Al-Makhadmeh, “Predictive data analysis approach for securing medical data in smart grid healthcare systems,” Future Generation Computer Systems, vol. 117, pp. 87–96, Apr. 2021, doi: 10.1016/j.future.2020.11.008.
     Google Scholar
  64. S. Tuli, N. Basumatary, and R. Buyya, “EdgeLens: Deep Learning based Object Detection in Integrated IoT, Fog and Cloud Computing Environments,” 2019 4th International Conference on Information Systems and Computer Networks (ISCON), pp. 496–502, Nov. 2019, doi: 10.1109/iscon47742.2019.9036216.
     Google Scholar
  65. S. Tuli et al., “HealthFog: An ensemble deep learning based Smart Healthcare System for Automatic Diagnosis of Heart Diseases in integrated IoT and fog computing environments,” Future Generation Computer Systems, vol. 104, pp. 187–200, Mar. 2020, doi: 10.1016/j.future.2019.10.043.
     Google Scholar
  66. S. Tuli, R. Mahmud, S. Tuli, and R. Buyya, “FogBus: A Blockchain-based Lightweight Framework for Edge and Fog Computing,” Journal of Systems and Software, vol. 154, pp. 22–36, Aug. 2019, doi: 10.1016/j.jss.2019.04.050.
     Google Scholar
  67. M. Uddin, “Blockchain Medledger: Hyperledger fabric enabled drug traceability system for counterfeit drugs in pharmaceutical industry,” International Journal of Pharmaceutics, vol. 597, Mar. 2021, doi: 10.1016/j.ijpharm.2021.120235.
     Google Scholar
  68. M. Vahdati, K. Gholizadeh HamlAbadi, and A. M. Saghiri, “IoT-Based Healthcare Monitoring Using Blockchain,” Studies in Big Data, pp. 141–170, Dec. 2020, doi: 10.1007/978-981-15-9547-9_6.
     Google Scholar
  69. V. Varadharajan, D. Bansal, S. J. Nair, and R. Leevinson J, “Blockchain Reinventing the Healthcare Industry,” Advances in Data Mining and Database Management, pp. 309–328, 2021, doi: 10.4018/978-1-7998-6650-3.ch013.
     Google Scholar
  70. A. Vazirani, O. O’Donoghue, D. Brindley, and E. Meinert, “Blockchain vehicles for efficient Medical Record management,” npj Digital Medicine, vol. 3, no. 1, pp. 1–5, Jan. 2020, doi: 10.1038/s41746-019-0211-0.
     Google Scholar
  71. Wang et al., “A blockchain-based eHealthcare system interoperating with WBANs,” Future Generation Computer Systems, vol. 110, pp. 675–685, Sep. 2020, doi: 10.1016/j.future.2019.09.049.
     Google Scholar
  72. Z. Wang, N. Luo, and P. Zhou, “GuardHealth: Blockchain empowered secure data management and Graph Convolutional Network enabled anomaly detection in smart healthcare,” Journal of Parallel and Distributed Computing, vol. 142, pp. 1–12, Aug. 2020, doi: 10.1016/j.jpdc.2020.03.004.
     Google Scholar
  73. Z. Wu et al., “MD-NDNet: a multi-dimensional convolutional neural network for false-positive reduction in pulmonary nodule detection,” Physics in Medicine & Biology, vol. 65, no. 23, Dec. 2020, doi: 10.1088/1361-6560/aba87c.
     Google Scholar
  74. Yaqoob, K. Salah, R. Jayaraman, and Y. Al-Hammadi, “Blockchain for healthcare data management: opportunities, challenges, and future recommendations,” Neural Computing and Applications, pp. 1–16, Jan. 2021, doi: 10.1007/s00521-020-05519-w.
     Google Scholar
  75. Zhu, J. Hu, Y. Zhang, and X. Li, “Enhancing Traceability of Infectious Diseases: A Blockchain-Based Approach,” Information Processing & Management, vol. 58, no. 4, pp. 1–20, Jul. 2021, doi: 10.1016/j.ipm.2021.102570.
     Google Scholar