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Security of Blockchain and AI-Empowered Smart Healthcare: Application-Based Analysis
Open accessAbstract
A smart device carries a great amount of sensitive patient data as it offers innovative and enhanced functionalities in the smart healthcare system. Moreover, the components of healthcare systems are interconnected via the Internet, bringing significant changes to the delivery of healthcare services to individuals. However, easy access to healthcare services and applications has given rise to severe risks and vulnerabilities that hamper the performance of a smart healthcare system. Moreover, a large number of heterogeneous devices accumulate data that vary in terms of size and formats, making it challenging to manage the data in the healthcare repository and secure it from attackers who seek to profit from the data. Thus, smart healthcare systems are susceptible to numerous security threats and risks, such as hardware and software-based attacks, system-level attacks, and network attacks that have the potential to place patients’ lives at risk. An analysis of the literature revealed a research gap in that most security surveys on the healthcare ecosystem examined only the security challenges and did not explore the possibility of integrating modern technologies to alleviate security issues in the smart healthcare system. Therefore, in this article, we conduct a comprehensive review of the various most recent security challenges and their countermeasures in the smart healthcare environment. In addition, an artificial intelligence (AI) and blockchain-based secure architecture is proposed as a case study to analyse malware and network attacks on wearable devices. The proposed architecture is evaluated using various performance metrics such as blockchain scalability, accuracy, and dynamic malware analysis. Lastly, we highlight different open issues and research challenges facing smart healthcare systems.
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In the citing paper
“The authors of [15] proposed an architecture combining AI and blockchain to protect smart healthcare systems from data integrity attacks, focusing on wearable devices.”
From Balancing Privacy and Progress: A Review of Privacy Challenges, Systemic Oversight, and Patient Perceptions in AI-Driven Healthcare (2024) · cites this paper as [15]
In the citing paper
“By leveraging blockchain, healthcare providers can offer a more transparent and patient-controlled exchange of medical data, empowering patients to have a say in who accesses their information.”
From Balancing Privacy and Progress: A Review of Privacy Challenges, Systemic Oversight, and Patient Perceptions in AI-Driven Healthcare (2024) · cites this paper as [15]
In the citing paper
“Homomorphic Encryption in healthcare AI systems enables secure data utilization, ensuring patient confidentiality is maintained even during complex data analysis processes [15].”
From Balancing Privacy and Progress: A Review of Privacy Challenges, Systemic Oversight, and Patient Perceptions in AI-Driven Healthcare (2024) · cites this paper as [15]
In the citing paper
“Integrating blockchain, FL, Homomorphic Encryption, and Differential Privacy within IoHT systems offers solutions to these challenges, ensuring that data are managed ethically and securely while enabling innovative healthcare solutions [15].”
From Balancing Privacy and Progress: A Review of Privacy Challenges, Systemic Oversight, and Patient Perceptions in AI-Driven Healthcare (2024) · cites this paper as [15]
In the citing paper
“Homomorphic Encryption and Differential Privacy further contribute to this patient-centric approach by allowing for the secure and private analysis of health data, ensuring that patients' identities are shielded from potential misuse [15].”
From Balancing Privacy and Progress: A Review of Privacy Challenges, Systemic Oversight, and Patient Perceptions in AI-Driven Healthcare (2024) · cites this paper as [15]
In the citing paper
“Federated Learning enables the development of more personalized treatment plans by analyzing data from a diverse patient population while keeping individual data localized and private.”
From Balancing Privacy and Progress: A Review of Privacy Challenges, Systemic Oversight, and Patient Perceptions in AI-Driven Healthcare (2024) · cites this paper as [15]
In the citing paper
“Together, these technologies form a robust defense against cyber threats, safeguarding the confidentiality, integrity, and availability of critical healthcare data [15].”
From Balancing Privacy and Progress: A Review of Privacy Challenges, Systemic Oversight, and Patient Perceptions in AI-Driven Healthcare (2024) · cites this paper as [15]
In the citing paper
“These centralized systems, typically managed by a single entity, are more vulnerable to cyberattacks and unauthorized access.”
From Balancing Privacy and Progress: A Review of Privacy Challenges, Systemic Oversight, and Patient Perceptions in AI-Driven Healthcare (2024) · cites this paper as [15]
In the citing paper
“Blockchain's inherent features, such as immutability and auditability, are critical in preventing unauthorized data alterations and tracing the source of attacks, thus enhancing the overall security posture of healthcare systems.”
From Balancing Privacy and Progress: A Review of Privacy Challenges, Systemic Oversight, and Patient Perceptions in AI-Driven Healthcare (2024) · cites this paper as [15]
In the citing paper
“Blockchain's decentralized approach, however, distributes data across a network of nodes, thereby reducing the risk of a single point of failure and enhancing resilience to attacks.”
From Balancing Privacy and Progress: A Review of Privacy Challenges, Systemic Oversight, and Patient Perceptions in AI-Driven Healthcare (2024) · cites this paper as [15]
In the citing paper
“Traditional systems also often struggle with data silos and interoperability challenges, whereas blockchain technology can enable seamless data exchange while upholding stringent security and privacy standards [15].”
From Balancing Privacy and Progress: A Review of Privacy Challenges, Systemic Oversight, and Patient Perceptions in AI-Driven Healthcare (2024) · cites this paper as [15]
In the citing paper
“AI can detect anomalous behavior indicative of a security breach, while blockchain secures data transactions and ensures the integrity of health records.”
From Balancing Privacy and Progress: A Review of Privacy Challenges, Systemic Oversight, and Patient Perceptions in AI-Driven Healthcare (2024) · cites this paper as [15]
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Citations
Cites
1 retracted
Financial Fraud Detection in Healthcare Using Machine Learning and Deep Learning Techniques
Mehbodniya, A., Alam, I., Pande, S. D., Neware, R., Rane, K. P., Shabaz, M., & Madhavan, M. V. (2021) · Security and Communication Networks · doi:10.1155/2021/9293877 · Cited by 96
RetractedMetaverse for Healthcare: A Survey on Potential Applications, Challenges and Future Directions
Chengoden, R., Victor, N., Huynh‐The, T., Yenduri, G., Jhaveri, R. H., Alazab, M., Bhattacharya, S., Hegde, P., Maddikunta, P. K. R., & Gadekallu, T. R. (2023) · IEEE Access · doi:10.1109/access.2023.3241628 · Cited by 569
Bhuiyan, M. N., Rahman, M. M., Billah, M. M., & Saha, D. (2021) · IEEE Internet of Things Journal · doi:10.1109/jiot.2021.3062630 · Cited by 490
An exhaustive survey on security and privacy issues in Healthcare 4.0
Hathaliya, J. J., & Tanwar, S. (2020) · Computer Communications · doi:10.1016/j.comcom.2020.02.018 · Cited by 462
Shi, S., He, D., Li, L., Kumar, N., Khan, M. K., & Choo, K. R. (2020) · Computers & Security · doi:10.1016/j.cose.2020.101966 · Cited by 431
5G-based smart healthcare network: architecture, taxonomy, challenges and future research directions
Ahad, A., Tahir, M., & Yau, K.-L. A. (2019) · IEEE Access · doi:10.1109/access.2019.2930628 · Cited by 352
Hijacking an insulin pump: Security attacks and defenses for a diabetes therapy system
Li, C., Raghunathan, A., & Jha, N. K. (2011) · IEEE International Conference on e-Health Networking, Applications and Services (Healthcom) · doi:10.1109/health.2011.6026732 · Cited by 328
Security and Privacy for the Internet of Medical Things Enabled Healthcare Systems: A Survey
Sun, Y., Lo, F. P.-W., & Lo, B. (2019) · IEEE Access · doi:10.1109/access.2019.2960617 · Cited by 292
Mansour, R. F., Amraoui, A. E., Nouaouri, I., Diaz, V. G., Gupta, D., & Kumar, S. (2021) · IEEE Access · doi:10.1109/access.2021.3066365 · Cited by 255 · cited 1×
“Thus, Mansour et al. [53] applied an AI and Internet of Things (IoT)- based disease diagnosis model for smart healthcare systems.”
BackgroundYaqoob, T., Abbas, H., & Atiquzzaman, M. (2019) · IEEE Communications Surveys & Tutorials · doi:10.1109/comst.2019.2914094 · Cited by 236
Cybersecurity of Hospitals: discussing the challenges and working towards mitigating the risks
Argaw, S. T., Troncoso-Pastoriza, J. R., Lacey, D., Florin, M., Calcavecchia, F., Anderson, D., Burleson, W. P., Vogel, J.-M., O’Leary, C., Eshaya-Chauvin, B., et al. (2020) · BMC Medical Informatics and Decision Making · doi:10.1186/s12911-020-01161-7 · Cited by 235
Secured Data Collection With Hardware-Based Ciphers for IoT-Based Healthcare
Hai, T., Bhuiyan, M. Z. A., Abdalla, A. N., Hassan, M. M., Zain, J. M., & Hayajneh, T. S. (2018) · IEEE Internet of Things Journal · doi:10.1109/jiot.2018.2854714 · Cited by 213
Almashhadani, A. O., Kaiiali, M., Sezer, S., & O’Kane, P. (2019) · IEEE Access · doi:10.1109/access.2019.2907485 · Cited by 192
Mansfield-Devine, S. (2022) · Network Security · doi:10.12968/s1353-4858(22)70049-9 · Cited by 191
Sustainability of Healthcare Data Analysis IoT-Based Systems Using Deep Federated Learning
Elayan, H., Aloqaily, M., & Guizani, M. (2021) · IEEE Internet of Things Journal · doi:10.1109/jiot.2021.3103635 · Cited by 176
Decentralized Federated Learning for Healthcare Networks: A Case Study on Tumor Segmentation
Tedeschini, B. C., Savazzi, S., Stoklasa, R., Barbieri, L., Stathopoulos, I., Nicoli, M., & Serio, L. (2022) · IEEE Access · doi:10.1109/access.2022.3141913 · Cited by 176
Aggarwal, S., Kumar, N., & Tanwar, S. (2020) · IEEE Internet of Things Journal · doi:10.1109/jiot.2020.3020819 · Cited by 163
Facial Sentiment Analysis Using AI Techniques: State-of-the-Art, Taxonomies, and Challenges
Patel, K., Mehta, D., Mistry, C., Gupta, R., Tanwar, S., Kumar, N., & Alazab, M. (2020) · IEEE Access · doi:10.1109/access.2020.2993803 · Cited by 157
Haghi, M., Neubert, S., Geißler, A., Fleischer, H., Stoll, N., Stoll, R., & Thurow, K. (2020) · IEEE Internet of Things Journal · doi:10.1109/jiot.2020.2980432 · Cited by 152
BIoTHR: Electronic Health Record Servicing Scheme in IoT-Blockchain Ecosystem
Ray, P. P., Chowhan, B., Kumar, N., & Almogren, A. (2021) · IEEE Internet of Things Journal · doi:10.1109/jiot.2021.3050703 · Cited by 149
Deebak, B. D., & Al-Turjman, F. M. (2020) · IEEE Journal on Selected Areas in Communications · doi:10.1109/jsac.2020.3020599 · Cited by 145
Gohar, A. N., AbdelMawgoud, S. A., & Farhan, M. S. (2022) · IEEE Access · doi:10.1109/access.2022.3202902 · Cited by 136
When Blockchain Meets Smart Grid: Secure Energy Trading in Demand Response Management
Kumari, A., Gupta, R., Tanwar, S., Tyagi, S., & Kumar, N. (2020) · IEEE Network · doi:10.1109/mnet.001.1900660 · Cited by 121
A taxonomy of blockchain-enabled softwarization for secure UAV network
Kumari, A., Gupta, R., Tanwar, S., & Kumar, N. (2020) · Computer Communications · doi:10.1016/j.comcom.2020.07.042 · Cited by 116
HARCI: A Two-Way Authentication Protocol for Three Entity Healthcare IoT Networks
Alladi, T., Chamola, V., & Naren, N. (2020) · IEEE Journal on Selected Areas in Communications · doi:10.1109/jsac.2020.3020605 · Cited by 116
Man-in-the-Middle Attack Mitigation in Internet of Medical Things
Salem, O., Alsubhi, K., Shaafi, A., Gheryani, M., Mehaoua, A., & Boutaba, R. (2021) · IEEE Transactions on Industrial Informatics · doi:10.1109/tii.2021.3089462 · Cited by 113
A Survey and Classification of Security and Privacy Research in Smart Healthcare Systems
Algarni, A. (2019) · IEEE Access · doi:10.1109/access.2019.2930962 · Cited by 112
Wu, G., Wang, S., Ning, Z., & Zhu, B. (2021) · IEEE Journal of Biomedical and Health Informatics · doi:10.1109/jbhi.2021.3123643 · Cited by 111
Trends, Technologies, and Key Challenges in Smart and Connected Healthcare
Navaz, A. N., Serhani, M. A., Kassabi, H. T. E., Al-Qirim, N. A. Y., & Ismail, H. (2021) · IEEE Access · doi:10.1109/access.2021.3079217 · Cited by 109
VAHAK: A Blockchain-based Outdoor Delivery Scheme using UAV for Healthcare 4.0 Services
Gupta, R., Shukla, A., Mehta, P., Bhattacharya, P., Tanwar, S., Tyagi, S., & Kumar, N. (2020) · IEEE Conference on Computer Communications Workshops (INFOCOM WKSHPS) · doi:10.1109/infocomwkshps50562.2020.9162738 · Cited by 107
Improvement of a Privacy Authentication Scheme Based on Cloud for Medical Environment
Chiou, S.-Y., Ying, Z., & Liu, J. (2016) · Journal of Medical Systems · doi:10.1007/s10916-016-0453-1 · Cited by 106
BSFR-SH: Blockchain-Enabled Security Framework Against Ransomware Attacks for Smart Healthcare
Wazid, M., Das, A. K., & Shetty, S. S. (2022) · IEEE Transactions on Consumer Electronics · doi:10.1109/tce.2022.3208795 · Cited by 106
AaYusH: A Smart Contract-Based Telesurgery System for Healthcare 4.0
Gupta, R., Shukla, A., & Tanwar, S. (2020) · IEEE International Conference on Communications workshops · doi:10.1109/iccworkshops49005.2020.9145044 · Cited by 105
Machine Learning-Based Student’s Native Place Identification for Real-Time
Verma, C., Stoffová, V., Illés, Z., Tanwar, S., & Kumar, N. (2020) · IEEE Access · doi:10.1109/access.2020.3008830 · Cited by 103
Adaptive and Fault-Tolerant Data Processing in Healthcare IoT Based on Fog Computing
Wang, K., Shao, Y. F., Xie, L., Wu, J., & Guo, S. (2018) · IEEE Transactions on Network Science and Engineering · doi:10.1109/tnse.2018.2859307 · Cited by 102
Mezghani, E., Expósito, E., & Drira, K. (2017) · IEEE Transactions on Emerging Topics in Computational Intelligence · doi:10.1109/tetci.2017.2699218 · Cited by 96
A Novel Chaos-Based Privacy-Preserving Deep Learning Model for Cancer Diagnosis
Rehman, M. U., Shafique, A., Ghadi, Y. Y., Boulila, W., Jan, S. U., Gadekallu, T. R., Driss, M., & Ahmad, J. E. (2022) · IEEE Transactions on Network Science and Engineering · doi:10.1109/tnse.2022.3199235 · Cited by 95
Patel, V. A., Bhattacharya, P., Tanwar, S., Gupta, R., Sharma, G., Bokoro, P. N., & Sharma, R. (2022) · IEEE Access · doi:10.1109/access.2022.3201876 · Cited by 88
Tanwar, S., Vora, J., Kaneriya, S., Tyagi, S., Kumar, N., Sharma, V., & You, I. (2019) · IEEE Consumer Electronics Magazine · doi:10.1109/mce.2019.2941456 · Cited by 82
Blockchain-based Interoperable Healthcare using Zero-Knowledge Proofs and Proxy Re-Encryption
Sharma, B., Halder, R., & Singh, J. (2020) · International Conference on Communication Systems & Networks (COMSNETS) · doi:10.1109/comsnets48256.2020.9027413 · Cited by 79
Access Control Schemes for Implantable Medical Devices: A Survey
Wu, L., Du, X., Guizani, M., & Mohamed, A. M. S. (2017) · IEEE Internet of Things Journal · doi:10.1109/jiot.2017.2708042 · Cited by 75
Rehman, A., Razzak, I., & Xu, G. (2022) · IEEE Journal of Biomedical and Health Informatics · doi:10.1109/jbhi.2022.3171852 · Cited by 72
Banerjee, S., Odelu, V., Das, A. K., Chattopadhyay, S., Kumar, N., Park, Y., & Tanwar, S. (2018) · IEEE Access · doi:10.1109/access.2018.2827027 · Cited by 71
Ravi, V., Pham, T. D., & Alazab, M. (2022) · IEEE Transactions on Computational Social Systems · doi:10.1109/tcss.2022.3198123 · Cited by 71
Reebadiya, D., Rathod, T., Gupta, R., Tanwar, S., & Kumar, N. (2021) · Peer-to-Peer Networking and Applications · doi:10.1007/s12083-021-01073-x · Cited by 70
Role of Drone Technology Helping in Alleviating the COVID-19 Pandemic
Mohsan, S. A. H., Zahra, Q. U. A., Khan, M. A., Alsharif, M. H., Elhaty, I. A., & Jahid, A. (2022) · Micromachines · doi:10.3390/mi13101593 · Cited by 68
Influence of Montoring: Fog and Edge Computing
Prasad, V. K., Bhavsar, M. D., & Tanwar, S. (2019) · Scalable Computing Practice and Experience · doi:10.12694/scpe.v20i2.1533 · Cited by 66
Thamer, N., & Alubady, R. (2021) · doi:10.1109/bicits51482.2021.9509877 · Cited by 65
Energy Efficient Fog based Healthcare Monitoring Infrastructure
Isa, I. S. M., El-Gorashi, T., Musa, M. O. I., & Elmirghani, J. (2020) · White Rose Research Online (University of Leeds, The University of Sheffield, University of York) · Cited by 64
New secure healthcare system using cloud of things
Alkeem, E. A., Shehada, D. J. M., Yeun, C. Y., Zemerly, M. J., & Hu, J. (2017) · Cluster Computing · doi:10.1007/s10586-017-0872-x · Cited by 61
A Taxonomy on Smart Healthcare Technologies: Security Framework, Case Study, and Future Directions
Chaudhary, S., Kakkar, R., Jadav, N. K., Nair, A. R., Gupta, R., Tanwar, S., Agrawal, S. S., Alshehri, M. D., Sharma, R., Sharma, G., et al. (2022) · Journal of Sensors · doi:10.1155/2022/1863838 · Cited by 61
EEMHR: Energy‐efficient multilevel heterogeneous routing protocol for wireless sensor networks
Tanwar, S., Kumar, N., & Niu, J. (2014) · International Journal of Communication Systems · doi:10.1002/dac.2780 · Cited by 60
Security in Wireless Body Area Networks: From In-Body to Off-Body Communications
Usman, M., Asghar, M. R., Ansari, I. S., & Qaraqe, M. (2018) · IEEE Access · doi:10.1109/access.2018.2873825 · Cited by 59
MedBlock: An AI-enabled and Blockchain-driven Medical Healthcare System for COVID-19
Mistry, C., Thakker, U., Gupta, R., Obaidat, M. S., Tanwar, S., Kumar, N., & Rodrigues, J. J. P. C. (2021) · International Conference on Communications · doi:10.1109/icc42927.2021.9500397 · Cited by 57
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Wazid, M., Singh, J., Das, A. K., Shetty, S. S., Khan, M. K., & Rodrigues, J. J. P. C. (2022) · IEEE Access · doi:10.1109/access.2022.3179418 · Cited by 57
Zahra, S. R., Chishti, M. A., Baba, A. I., & Wu, F. (2021) · Egyptian Informatics Journal · doi:10.1016/j.eij.2021.12.003 · Cited by 52
Subramanian, G., & Thampy, A. S. (2021) · IEEE Access · doi:10.1109/access.2021.3132302 · Cited by 52
Edge-Based IoT Medical Record System: Requirements, Recommendations and Conceptual Design
Subahi, A. F. (2019) · IEEE Access · doi:10.1109/access.2019.2927958 · Cited by 49
Likelihood of Questioning AI-Based Recommendations Due to Perceived Racial/Gender Bias
Parra, C. M., Gupta, M., & Dennehy, D. (2022) · IEEE Transactions on Technology and Society · doi:10.1109/tts.2021.3120303 · Cited by 48
Rughoobur, P., & Nagowah, L. (2017) · doi:10.1109/ictus.2017.8286118 · Cited by 47
Zia, M., Obaidat, M. S., Mahmood, K., Shamshad, S., Saleem, M. A., & Chaudhry, S. A. (2022) · IEEE Transactions on Industrial Informatics · doi:10.1109/tii.2022.3202968 · Cited by 47
Alshehri, M., Abugabah, A., Algarni, A., & Almotairi, S. (2022) · Computers & Electrical Engineering · doi:10.1016/j.compeleceng.2022.107868 · Cited by 46
Le, T., Lu, C.-F., Hsu, C.-L., Do, T. K., Chou, Y.-F., & Wei, W.-C. (2022) · IEEE Access · doi:10.1109/access.2022.3158756 · Cited by 41
A System for the Promotion of Traceability and Ownership of Health Data Using Blockchain
Pinto, R. P., Silva, B. M. C., & Inácio, P. R. M. (2022) · IEEE Access · doi:10.1109/access.2022.3203193 · Cited by 39
A holistic survey on the use of emerging technologies to provision secure healthcare solutions
Jagatheesaperumal, S. K., Mishra, P., Moustafa, N., & Chauhan, R. S. (2022) · Computers & Electrical Engineering · doi:10.1016/j.compeleceng.2022.107691 · Cited by 38
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Butt, U., Abbod, M., Lors, A., Jahankhani, H., Jamal, A., & Kumar, A. (2019) · International Conference on Global Security, Safety, and Sustainability · doi:10.1109/icgs3.2019.8688327 · Cited by 36
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Gupta, R., Patel, M. M., Tanwar, S., Kumar, N., & Zeadally, S. (2021) · IEEE Transactions on Green Communications and Networking · doi:10.1109/tgcn.2021.3111529 · Cited by 36
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Gourob, J. H., Raxit, S., & Hasan, A. (2021) · International Conference on Automation, Control and Mechatronics for Industry 4.0 (ACMI) · doi:10.1109/acmi53878.2021.9528192 · Cited by 35
A novel phishing detection system using binary modified equilibrium optimizer for feature selection
Minocha, S., & Singh, B. (2022) · Computers & Electrical Engineering · doi:10.1016/j.compeleceng.2022.107689 · Cited by 33
A lightweight security framework for electronic healthcare system
Chaudhary, R. R. K., & Chatterjee, K. (2022) · International Journal of Information Technology · doi:10.1007/s41870-022-01034-4 · Cited by 33
A Fog and Blockchain Software Architecture for a Global Scale Vaccination Strategy
Costa, H. J. D. M., da Costa, C. A., Righi, R. D. R., Antunes, R. S., Paz, J. F. D., & Leithardt, V. R. Q. (2022) · IEEE Access · doi:10.1109/access.2022.3169418 · Cited by 32
Gupta, R., Tanwar, S., & Kumar, N. (2021) · Physical Communication · doi:10.1016/j.phycom.2021.101355 · Cited by 30
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Hassan, K. M., Abdo, A., & Yakoub, A. (2022) · IEEE Access · doi:10.1109/access.2022.3211512 · Cited by 28
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Ansari, A. A., Mishra, B., Gera, P., Khan, M. K., Chakraborty, C., & Mishra, D. (2022) · IEEE Transactions on Industrial Informatics · doi:10.1109/tii.2022.3170148 · Cited by 24
Nyangaresi, V. O., Abduljabbar, Z. A., Ma, J., & Sibahee, M. A. A. (2022) · Global Power, Energy and Communication Conference · doi:10.1109/gpecom55404.2022.9815685 · Cited by 23
Miranda, D., Olivares, R., Muñoz, R., & Minonzio, J.-G. (2022) · IEEE Transactions on Ultrasonics Ferroelectrics and Frequency Control · doi:10.1109/tuffc.2022.3195477 · Cited by 19
Alghatani, K., Ammar, N., Rezgui, A., & Shaban‐Nejad, A. (2022) · IEEE Access · doi:10.1109/access.2022.3175304 · Cited by 18
Yin, J., Li, M., Wu, W., Sun, D., Zhou, J., Huo, W., & Xue, J. (2022) · IEEE Symposium on Security and Privacy (S&P) · doi:10.1109/sp46214.2022.9833723 · Cited by 16
Samaila, M. G., Lopes, C., Aires, É., Sequeiros, J. B. F., Simões, T. M. C., Freire, M. M., & Inácio, P. R. M. (2021) · Computer Networks · doi:10.1016/j.comnet.2021.108496 · Cited by 15
Gong, S., Ochiai, H., & Esaki, H. (2020) · 2020 IEEE 44th Annual Computers, Software, and Applications Conference (COMPSAC) · doi:10.1109/compsac48688.2020.00-43 · Cited by 14
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Letafati, M., Behroozi, H., Khalaj, B. H., & Jorswieck, E. A. (2021) · IEEE International Conference on Communications workshops · doi:10.1109/iccworkshops50388.2021.9473492 · Cited by 14
Rabbani, R., Najafiaghdam, H., Ghanbari, M. M., Papageorgiou, E. P., Zhao, B. R., Roschelle, M., Stojanovic, V. M., Muller, R., & Anwar, M. (2021) · Annual International Conference of the IEEE Engineering in Medicine and Biology Society (EMBC) · doi:10.1109/embc46164.2021.9631061 · Cited by 14
Kong, K., Zhang, Z., Guo, C., Han, J., & Long, G. (2022) · Future Generation Computer Systems · doi:10.1016/j.future.2022.08.002 · Cited by 14
Gountia, D., & Roy, S. K. (2019) · International Conference on Trends in Electronics and Informatics (ICOEI) · doi:10.1109/icoei.2019.8862598 · Cited by 13
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Xu, B., & Zhou, F. (2022) · IEEE Access · doi:10.1109/access.2022.3181147 · Cited by 13
Device authentication mechanism for IoT enabled healthcare system
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Williamson, S. M., & Prybutok, V. (2024) · Applied Sciences · doi:10.3390/app14020675 · Cited by 567
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Metaverse Chronicles: A Bibliometric Analysis of Its Evolving Landscape
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Blockchain for the Healthcare Supply Chain: A Systematic Literature Review
Fiore, M., Capodici, A., Rucci, P., Bianconi, A., Longo, G., Ricci, M., Sanmarchi, F., & Golinelli, D. (2023) · Applied Sciences · doi:10.3390/app13020686 · Cited by 80
Metadata
- Authors
- Alabdulatif, A., Khalil, I., & Saidur Rahman, M.
- Affiliations
- Qassim University (SA)
- RMIT University (AU)
- Computer Science and Software Engineering, School of Science, RMIT University, Melbourne, VIC 3000, Australia
- Corresponding author
- Alabdulatif, Abdulatif
- Venue
- Applied Sciences
- Publisher
- MDPI AG
- Published
- 2022-10-31
- Volume
- 12
- Issue
- 21
- Pages
- 11039
- ISSN
- 2076-3417 (ISSN-L)
- OpenAlex
- W4307751771
- Cited by
- 117
- References
- 95
- Citations by year
- 2026: 28 · 2025: 32 · 2024: 38 · 2023: 18 · 2022: 1
- FWCI
- 22.04
- Type
- journal article
- Language
- en
- Open access
- gold
- doi.org
- Licence
- Locations
- Applied Sciences (publishedVersion, cc-by)
- DOAJ (DOAJ: Directory of Open Access Journals) (submittedVersion, cc-by-sa)
- Figshare (submittedVersion, cc-by)
- Funders
- Qassim University (10118-coc-2020-1-3-I)
- Topics
- Physical Sciences › Computer Science › Information Systems › Blockchain Technology Applications and Security
- Physical Sciences › Computer Science › Information Systems › Internet of Things and AI
- Physical Sciences › Computer Science › Computer Networks and Communications › IoT and Edge/Fog Computing
- Keywords
- blockchain, artificial intelligence, wearable devices, malware detection, network attacks, patient data security, secure architecture, dynamic malware analysis, blockchain scalability
About these checks
Registry, retraction, Cites and statistics results are computed, not judged, with one exception: a printed reference that no index lists is read once by a model before it counts as a citation error. Claims are extracted by a model and kept only when their quote appears verbatim in the text; each cited source is then checked, with a model’s help, for whether it says what the claim relies on. Those verdicts can be wrong — read the claim’s quote and the cited source before relying on one.
Statistics and arithmetic are recomputed, not judged.
Confidence bands are shown only for fields calibrated on our annotated test set. None is calibrated yet, so no band is shown.
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