Review of Embedded Systems and Cyber Threat Intelligence for Enhancing Data Security in Mobile Health

Authors

  • Anayo Chukwu Ikegwu

    Computer Science Department, Faculty of Physical Sciences, Alex Ekwueme Federal University Ndufu-Alike, Abakaliki

    P.M.B. 1010, Nigeria
  • Victoria Chibuzo Uzuegbu

    Software Engineering Department, Faculty of Natural and Applied Sciences, Veritas University Abuja, Abuja 901101,

    Nigeria
  • Uzoma Rita Alo

    Computer Science Department, Faculty of Natural and Applied Sciences, Veritas University Abuja, Abuja 901101, Nigeria

DOI:

https://doi.org/10.30564/jeis.v7i1.10050
Received: 8 February 2025 | Revised: 17 March 2025 | Accepted: 27 March 2025 | Published Online: 5 April 2025

Abstract

Embedded systems play a vital role in mobile health (mHealth) by enabling real-time health monitoring and personalized care through devices like wearables and sensors. However, these systems handle sensitive health data, making them vulnerable to cyber threats such as data breaches and unauthorized access. Traditional security measures are often inadequate against evolving attacks, raising concerns over data safety. This paper reviews how cyber threat intelligence (CTI) can be integrated with embedded systems in mHealth to enhance security. CTI provides real-time insights into potential threats, enabling proactive detection and prevention through tools like intrusion detection systems and predictive analytics. The study examines current embedded system applications in mHealth, associated security challenges, and how CTI strengthens security frameworks. It emphasizes the need for specialized CTI models and collaborative threat intelligence sharing in the healthcare sector. We further provided a practical examples and case studies to showcase the application of CTI in securing embedded systems within mHealth environments. The key findings demonstrate CTI’s effectiveness in safeguarding vital health data and guiding future innovations in healthcare cybersecurity. The implications of this study would enhance data security, establish uniform security policies, and facilitate the growth of mHealth technology for effective development of optimal healthcare services by the practitioner, policymakers, medical health developers.

Keywords:

Embedded Systems; Cyber Threat Intelligence; Data Security; Mobile Health; Internet of Things; Healthcare Patient Data

References

[1] Anikwe, C.V., Nweke, H.F., Ikegwu, A.C., et al., 2022. Mobile and wearable sensors for data-driven health monitoring system: State-of-the-art and future prospect. Expert Systems with Applications. 202, 117362. DOI: https://doi.org/10.1016/j.eswa.2022.117362

[2] Dinh-Le, C., Chuang, R., Chokshi, S., et al., 2019. Wearable health technology and electronic health record integration: scoping review and future directions. JMIR mHealth and uHealth. 7(9), e12861. DOI: https://doi.org/10.2196/12861

[3] Chakraborty, C., Bhattacharya, M., Pal, S., et al., 2024. From machine learning to deep learning: Advances of the recent data-driven paradigm shift in medicine and healthcare. Current Research in Biotechnology. 7, 100164. DOI: https://doi.org/10.1016/j.crbiot.2023.100164

[4] Ksira, Z., Mellit, A., Blasuttigh, N., et al., 2024. A novel embedded system for real-time fault diagnosis of photovoltaic modules. IEEE Journal of Photovoltaics. 14(2), 354–362. DOI: https://doi.org/10.1109/JPHOTOV.2024.3359462

[5] Ikegwu, A.C., Nweke, H.F., Anikwe, C.V., et al., 2022. Big Data Analytics for Data-driven Industry: A Review of Data Sources, Tools, Challenges, Solutions and Research Directions. Cluster Computing. 25(5), 3343–3387. DOI: https://doi.org/https://doi.org/10.1007/s10586-022-03568-5

[6] Mahler, T., Nissim, N., Shalom, E., et al., 2018. Know your enemy: Characteristics of cyber-attacks on medical imaging devices. Available from: https://arxiv.org/abs/1801.05583 (cited 1 January 2025).

[7] Ewoh, P., Vartiainen, T., 2024. Vulnerability to cyberattacks and sociotechnical solutions for health care systems: systematic review. Journal of medical internet research. 26, e46904. DOI: https://doi.org/10.2196/46904

[8] Xing, Y., Lu, H., Zhao, L., et al., 2024. Privacy and security issues in mobile medical information systems MMIS. Mobile Networks and Applications. 29, 762–773. DOI: https://doi.org/10.1007/s11036-024-02299-8

[9] Ameen, A.H., Mohammed, M., Rashid, A., 2023. Dimensions of artificial intelligence techniques, blockchain, and cyber security in the Internet of medical things: Opportunities, challenges, and future directions. Journal of Intelligent Systems. 32(1), 20220267. DOI: https://doi.org/10.1515/jisys-2022-0267

[10] Abdulhussein, M., 2024. The impact of artificial intelligence and machine learning on organizations cybersecurity [PhD Thesis]. Lynchburg, VA: Liberty University. pp. 1-273.

[11] Willie, M.M., 2023. The role of organizational culture in cybersecurity: building a security-first culture. Journal of Research, Innovation and Technologies. 2(2(4)), 179–198. DOI: https://doi.org/10.57017/jorit.v2.2(4).05

[12] Ahmadi, H., Arji, G., Shahmoradi, L., et al. 2019. The application of internet of things in healthcare: a systematic literature review and classification. Universal Access in the Information Society. 18, 837–869. DOI: https://doi.org/10.1007/s10209-018-0618-4

[13] Wang, M., Ji, H., Jia, M., et al., 2023. Method and application of information sharing throughout the emergency rescue process based on 5G and AR wearable devices. Scientific Reports. 13(1), 6353. DOI: https://doi.org/10.1038/s41598-023-33610-4

[14] Istepanian, R.S., Woodward, B., 2016. M-health: Fundamentals and Applications. John Wiley & Sons: Hoboken, NJ, USA. pp. 1-85.

[15] Nweke, H., Ikegwu, A.C., Nwafor, C.A., et al., 2024. Smartphone-based human activity recognition using Artificial Intelligence Methods and Orientation Invariant Features. Nigeria Computer Society. 35, 1–8.

[16] Singh, B., Kaunert, C., 2024. Unmanned aerial vehicle: Integration in healthcare sector for transforming interplay among smart cities. In: Khan, I.U., Hajjami, S.E., Ouaissa, M., Belaqziz, S., Bhatia, T.K. (eds.). Cognitive Machine Intelligence. CRC Press: Boca Raton, FL, USA. pp. 108–129.

[17] Suppiah, R., Noori, K., Abidi, K., et al., 2024. Real-time edge computing design for physiological signal analysis and classification. Biomedical Physics & Engineering Express. 10(4), 045034. DOI: https://doi.org/10.1088/2057-1976/ad4f8d

[18] Parmar, R., Patel, D., Panchal, N., et al., 2022. 5G-enabled deep learning-based framework for healthcare mining: State of the art and challenges. Blockchain Applications for Healthcare Informatics. 401–420. DOI: https://doi.org/10.1016/B978-0-323-90615-9.00016-5

[19] Sharmila, V., Kannadhasan, S., Rajiv Kannan, A., et al., 2024. Challenges in Information, Communication and Computing Technology. Proceedings of the 2nd International Conference on Challenges in Information, Communication, and Computing Technology (ICCICCT 2024); April 26–April 27, 2024; Namakkal, India. pp. 1–890.

[20] Nolte, H., 2024. Utilizing HPC Systems to Serve Compute-Intensive Tasks from a Data Lake Managing Sensitive Data [PhD Thesis]. Göttingen: Georg-August-Universität Göttingen. pp. 1–173.

[21] Kumar, A., Singh, A.K., Choi, B.J., 2021. Analysis of User Interaction to Mental Health Application Using Topic Modeling Approach. In: Kim, J.H., Singh, M., Khan, J., Tiwary, U.S., Sur, M., Singh, D. (eds.). International Conference on Intelligent Human Computer Interaction, Lecture Notes in Computer Science, vol 13184. Springer: Cham, Switzerland. pp. 703–717.

[22] Choi, J.R., 2022. Exploring the surveillance culture in the context of smart health: a cross-cultural comparison between South Korea and the US [PhD Thesis]. Austin, TX: The University of Texas at Austin. pp. 1-231.

[23] Zhang, H., Ibrahim, A., Parsia, B., et al., 2023. Passive social sensing with smartphones: a systematic review. Computing. 105(1), 29–51. DOI: https://doi.org/10.1007/s00607-022-01112-2

[24] Huang, C., Wang, J., Wang, S.H., et al., 2023. Internet of medical things: A systematic review. Neurocomputing. 557, 126719. DOI: https://doi.org/10.1016/j.neucom.2023.126719

[25] El-Amrawy, F., Nounou, M.I., 2015. Are Currently Available Wearable Devices for Activity Tracking and Heart Rate Monitoring Accurate, Precise, and Medically Beneficial? Healthcare Informatics Research. 21(4), 315–320. DOI: https://doi.org/10.4258/hir.2015.21.4.315

[26] West, D.M., 2013. Improving health care through mobile medical devices and sensors. Brookings Institution Policy Report. 10(9), 1–13.

[27] Rajendran, S., Porwal, A., Anjali, K., et al., 2024. Portable IoT Devices in Healthcare for Health Monitoring and Diagnostics. In: Murthy, H., Zurek-Mortka, M., Pillai, V.J., Kumar, K.P. (eds.). Internet of Things in Bioelectronics: Emerging Technologies and Applications. Wiley: Hoboken, NJ, USA. pp. 263–296.

[28] Salama, R., Altrjman, C., Al-Turjman, F., 2024. Healthcare cybersecurity challenges: a look at current and future trends. Computational intelligence and Blockchain in complex systems. 97–111. DOI: https://doi.org/10.1016/B978-0-443-13268-1.00003-0

[29] Nisha, S., 2025. Securing Life-Saving Devices: Challenges and Solutions in Medical Device Cybersecurity. International Journal of Trend in Scientific Research and Development. 9(1), 776–783.

[30] Balaji, V., Sonowal, G., Gowda, S., 2024. Recent Research Techniques in Processing, Security, and Storage of Real-World Applications of Big Computing. In: Sardar, T.H., Pandey, B.K. (eds.). Big Data Computing. CRC Press: Boca Raton, FL, USA. pp. 145–179.

[31] Gonzalez, I., Calderón, A.J., Folgado, F.J., 2022. IoT real time system for monitoring lithium-ion battery long-term operation in microgrids. Journal of Energy Storage. 51, 104596. DOI: https://doi.org/10.1016/j.est.2022.104596

[32] Kramer, D.B., Yeh, R.W., 2017. Practical improvements for medical device evaluation. JAMA. 318(4), 332–334. DOI: https://doi.org/10.1001/jama.2017.8976

[33] Sonko, S., Monebi, A.M., Etukudoh, E.A., et al., 2024. Reviewing the impact of embedded systems in medical devices in the USA. International Medical Science Research Journal. 4(2), 158–169. DOI: https://doi.org/10.51594/imsrj.v4i2.767

[34] Yun, J., Rustamov, F., Kim, J., et al., 2022. Fuzzing of embedded systems: A survey. ACM Computing Surveys. 55(7), 1–33. DOI: https://doi.org/10.1145/3538644

[35] Khan, S., Jiangbin, Z., Ullah, F., et al., 2024. Hybrid computing framework security in dynamic offloading for IoT-enabled smart home system. PeerJ Computer Science. 10, e2211. DOI: https://doi.org/10.7717/peerj-cs.2211

[36] Garg, A., Kumar, A., Singh, A.K., 2024. Machine Learning-Based Security Approaches for Wireless Body Area Networks. In: Singh, A.K., Kumar, S. (eds.). Security, Privacy, and Trust in WBANs and E-Healthcare. CRC Press: Boca Raton, FL, USA. pp. 206–235.

[37] Nahta, P., 2025. Securing the Digital Supply Chain: Challenges, Innovations, and Best Practices in Cybersecurity. In: Lytras, M.D., Alkhaldi, A., Serban, A.C. (eds.). Innovation Management for a Resilient Digital Economy. IGI Global Scientific Publishing: Hershey, PA, USA. pp. 205–230.

[38] Aslam, M.M., Tufail, A., Apong, R.A.A.H.M., et al., 2024. Scrutinizing security in industrial control systems: An architectural vulnerabilities and communication network perspective. IEEE Access. 12, 67537–67573. DOI: https://doi.org/10.1109/ACCESS.2024.3394848

[39] Kumar, A., Kumar, A., Tomar, G.S., et al., 2023. Advanced Wireless Communication: Technology Overview, Challenges, and Security Issues. In: Bagwari, A., Tomar, G.S., Bagwari, J., Victória Barbosa, J.L., Sastry, M.K.S. (eds.). Advanced Wireless Communication and Sensor Networks. Chapman and Hall/CRC: New York, NY, USA. pp. 65–80

[40] Gowda, D., Reddy, P., Rajasekhara, V., et al., 2025. Revolutionizing Patient Care Through the Convergence of IoMT and Generative AI. In: Kumar, V.V., Katina, P.F., Zhao, J.Y. (eds.). Convergence of Internet of Medical Things (IoMT) and Generative AI. IGI Global Scientific Publishing: Hershey, PA, USA. pp. 217–242.

[41] Thantilage, R.D., Yasarathna, T.L., Le-Khac, N.-A., et al., 2023. Secure Emergency Data Sharing in Healthcare-A Data Warehouse View. Available from: https://www.researchsquare.com/article/rs-3659636/v1 (cited 1 January 2025).

[42] Sahu, A.K., 2024. Multimedia watermarking: Latest developments and trends. Springer Nature: Berlin, Germany. pp. 1–136.

[43] Chen, C.-S., Chen, W.-C., 2019. Research on a remote monitoring system for automatic control on dairy farms. In: Lam, A.K.T., Prior, S., Shen, S.-T., Young, S.-J., Ji, L.-W. (eds.). Engineering Innovation and Design. CRC Press: London, UK. pp. 285–290.

[44] Abbasi, N., Smith, D.A., 2024. Cybersecurity in Healthcare: Securing Patient Health Information (PHI), HIPPA compliance framework and the responsibilities of healthcare providers. Journal of Knowledge Learning and Science Technology. 3(3), 278–287. DOI: https://doi.org/10.60087/jklst.vol3.n3.p.278-287

[45] Singh, N., Buyya, R., Kim, H., 2025. Securing Cloud-Based Internet of Things: Challenges and Mitigations. Sensors. 25(1), 1–45. DOI: https://doi.org/10.3390/s25010079

[46] Jones, L.A., Burrell, D.N., 2025. Illegal Cybersecurity Threats Created by Organizational Arsonists in Healthcare Organizations. Law, Economics and Society. 1(1), 93. DOI: https://doi.org/10.30560/les.v1n1p93

[47] Kumar, R., 2024. Privacy and protection of patient sensitive data in the healthcare sector: a critical analysis [M.Sc.]. Dehradun: UPES. pp. 1-101.

[48] Wang, P., Jiang, C., Mao, A. W., Sun, Q., Zhu, H., Inman, J., ... & Chang, H. (2024). An AI-Powered tissue-agnostic cellular morphometrics biomarker for risk assessment in patients with pan-gastrointestinal precancerous lesions and cancers. medRxiv, 2024-11.

[49] Zhang, Z., Ning, H., Shi, F., et al., 2022. Artificial intelligence in cyber security: research advances, challenges, and opportunities. Artificial Intelligence Review. 55, 1029–1053. DOI: https://doi.org/10.1007/s10462-021-09976-0

[50] Nguyen, D.C., Pham, Q.-V., Pathirana, P.N., et al., 2022. Federated learning for smart healthcare: A survey. ACM Computing Surveys (Csur). 55(3), 1–37. DOI: https://doi.org/10.1145/3501296

[51] Ozcelik, M.M., Kok, I., Ozdemir, S., 2025. A Survey on Internet of Medical Things (IoMT): Enabling Technologies, Security and Explainability Issues, Challenges, and Future Directions. Expert Systems. 42(5), e70010. DOI: https://doi.org/10.1111/exsy.70010

[52] Tan-Smith, C., 2023. Medicalised Ketogenic Therapy Practice [PhD Thesis]. Te Pūkenga: Otago Polytechnic. pp. 1–405.

[53] Zhang, L.C., 2020. Interworking Mechanism of Blockchain Platforms for Secure Tourism Service [Master’s Thesis]. Jeju City: Judo National University. pp. 1–95.

[54] Xu, H., Seng, K.P., Ang, L.M., et al., 2024. Decentralized and distributed learning for AIoT: A comprehensive review, emerging challenges and opportunities. IEEE Access. 12, 101016–101052. DOI: https://doi.org/10.1109/ACCESS.2024.3422211

[55] Basheer, R., Alkhatib, B., 2021. Threats from the Dark: A Review over Dark Web Investigation Research for Cyber Threat Intelligence. Journal of Computer Networks and Communications. 2021(1), 302999. DOI: https://doi.org/10.1155/2021/1302999

[56] Zhou, M., Ruan, S., Liu, J., et al., 2022. vtpm-sm: An application scheme of SM2/SM3/SM4 algorithms based on trusted computing in cloud environment. Proceedings of 2022 IEEE 15th International Conference on Cloud Computing (CLOUD); July 10–July 16, 2022; Barcelona, Spain. pp. 351–356.

[57] Abohatem, A.Y., Ba-Alwi, F.M.M., Al-Khulaidi, A.A.G., 2023. Suggestion cybersecurity framework (CSF) for reducing cyber-attacks on information systems. Sana'a University Journal of Applied Sciences and Technology. 1(3), 234–252.

[58] Prince, N.U., Mamun, M.A.A., Olajide, A.O., et al., 2024. IEEE standards and deep learning techniques for securing internet of things (iot) devices against cyber-attacks. Journal of Computational Analysis and Applications. 33(7), 1270–1289.

[59] Tahmasebi, M., 2024. Beyond defense: Proactive approaches to disaster recovery and threat intelligence in modern enterprises. Journal of Information Security. 15(2), 106–133.

[60] Van W. A., 2024. Legislation within cybersecurity: preparing for NIS2–a detailed framework in the healthcare sector in the Netherlands [Master's Thesis]. Turku: University of Turku. pp. 1–158.

[61] Ejjami, R., 2024. Enhancing Cybersecurity through Artificial Intelligence: Techniques, Applications, and Future Perspectives. Journal of Next-Generation Research 5.0. 1(1). DOI: https://doi.org/10.70792/jngr5.0.v1i1.5

[62] Singh, P., Singh, N., Singh, K.K., et al., 2021. Diagnosing of disease using machine learning. In: Singh, K.K., Singh, A., Elhoseny, M., Elngar, A.A. (eds.). Machine learning and the internet of medical things in healthcare. Elsevier: Amsterdam, The Netherlands. pp. 89–111.

[63] Fontem, O., 2024. Strategies and Methods Used by Information Technology Security Professionals to Secure Cloud Access Infrastructure [PhD Thesis]. Minneapolis, MI: Walden University. pp. 1–158.

[64] Mehrotra, A., Zampetakis, M., Kassianik, P., et al., 2024. Tree of attacks: Jailbreaking black-box llms automatically. Advances in Neural Information Processing Systems. 37, 61065–61105.

[65] Miller, M.D., Redfern, R.E., Anderson, M.B., et al., 2024. Completion of patient-reported outcome measures improved with use of a mobile application in arthroplasty patients: results from a randomized controlled trial. The Journal of Arthroplasty. 39(7), 1656–1662. DOI: https://doi.org/10.1016/j.arth.2024.01.007

[66] Cinar, B., 2023. A Study on Cyber Threat Intelligence Based on Current Trends and Future Perspectives. In: Rafatullah, M., Santoso, L.W. (eds.). Advances and Challenges in Science and Technology Vol. 5. BP International: Kolkata, India. p. 37.

[67] Verma, O.P., Verma, S., Perumal, T., 2024. Advancement of intelligent computational methods and technologies. CRC Press: Boca Raton, FL, USA. pp. 1–206.

[68] Popoola, O.J., 2025. Designing a Privacy-Aware Framework for Ethical Disclosure of Sensitive Data [PhD Thesis]. Sheffield: Sheffield Hallam University. pp. 1–374.

[69] Li, N., Xu, M., Li, Q., et al., 2023. A review of security issues and solutions for precision health in Internet-of-Medical-Things systems. Security and Safety. 2, 2022010. DOI: https://doi.org/10.1051/sands/2022010

[70] Aminu, M., Akinsanya, A., Oyedokun, O., et al., 2024. Enhancing cyber threat detection through real-time threat intelligence and adaptive defense mechanisms. International Journal of Computer Applications Technology and Research. 13(8), 11–27. DOI: https://doi.org/10.7753/IJCATR1308.1002

[71] El-Amir, S., 2023. Comprehensive Cybersecurity Review: Modern Threats and Innovative Defense Approaches. International Journal of Computers and Informatics (Zagazig University). 1, 30–37.

[72] Hang, C.-N., Tsai, Y.-Z., Yu, P.-D., et al., 2023. Privacy-enhancing digital contact tracing with machine learning for pandemic response: a comprehensive review. Big Data and Cognitive Computing. 7(2), 108. DOI: https://doi.org/10.3390/bdcc7020108

[73] Zhang, C., Yang, S., Mao, L., et al., 2024. Anomaly detection and defense techniques in federated learning: a comprehensive review. Artificial Intelligence Review. 57(6), 150. DOI: https://doi.org/10.1007/s10462-024-10796-1

[74] Ndlovu, K., Mars, M., Scott, R.E., 2021. Interoperability frameworks linking mHealth applications to electronic record systems. BMC health services research. 21(1), 459. DOI: https://doi.org/10.1186/s12913-021-06473-6

[75] Ofoegbu, K.D.O., Osundare, O.S., Ike, C.S., et al., 2024. Enhancing cybersecurity resilience through real-time data analytics and user empowerment strategies. Engineering Science & Technology Journal. 4(6), 689–706. DOI: https://doi.org/10.51594/estj.v4i6.1527

[76] Chen, B., Shi, X., Feng, T., et al., 2024. Construction and Application of a Private 5G Standalone Medical Network in a Smart Health Environment: Exploratory Practice From China. Journal of medical internet research. 26, e52404. DOI: https://doi.org/10.2196/52404

[77] Volpe, U., Elkholy, H., Gargot, T., et al., 2023. Devices, Mobile Health, and Digital Phenotyping. In: Tasman, A., Riba, M.B., Alarcón, R.D., Alfonso, C.A., Kanba, S., Lecic-Tosevski, D., Ndetei, D.M., Ng, C.H., Schulze, T.G. (eds.). Tasman’s Psychiatry. Springer: Cham, Switzerland. pp. 5191–5216.

[78] Young, S., 2024. The Science and Technology of Growing Young, Updated Edition: An Insider's Guide to the Breakthroughs that Will Dramatically Extend Our Lifespan... and What You Can Do Right Now. BenBella Books: Dallas, TX, USA. pp. 1–288.

[79] ]79] Jansen, E., Supplieth, J., Lech, S., et al., 2025. Process evaluation of technologically assisted senior care using mixed methods: Results of the virtual assisted living (VAL, German: VBW Virtuell Betreutes Wohnen) project. Digital health. 11, 20552076241308445. DOI: https://doi.org/10.1177/20552076241308445

[80] Leong, C., 2024. An Exploratory Sequential Mixed Methods Study On Usable Cybersecurity And The Behaviorial Effects Of Cognitive Load [PhD Thesis]. Orlando, FL: University of Central Florida. pp. 1–88.

[81] Nag, A., Hassan, M.M., Das, A., et al., 2024. Exploring the applications and security threats of Internet of Thing in the cloud computing paradigm: A comprehensive study on the cloud of things. Transactions on Emerging Telecommunications Technologies. 35(4), e4897. DOI: https://doi.org/10.1002/ett.4897

[82] Im, H., Lee, D., Lee, S., 2024. A novel architecture for an intrusion detection system utilizing cross-check filters for in-vehicle networks. Sensors. 24(9), 2807. DOI: https://doi.org/10.3390/s24092807

[83] Patnala, V.N., Dinakarrao, S.M.P., Venkataramani, G., et al., 2024. Special Session: Detecting and Defending Vulnerabilities in Heterogeneous and Monolithic Systems: Current Strategies and Future Directions. Proceedings of 2024 International Conference on Compilers, Architecture, and Synthesis for Embedded Systems (CASES); September 29–October 4, 2024; Raleigh, NC, USA. pp. 5–14.

[84] González-Pérez, A., Matey-Sanz, M., Granell, C., et al., 2023. AwarNS: A framework for developing context-aware reactive mobile applications for health and mental health. Journal of Biomedical Informatics. 141, 104359. DOI: https://doi.org/10.1016/j.jbi.2023.104359

[85] Zhou, X., Li, B., Qi, Y., et al., 2020. Mimic encryption box for network multimedia data security. Security and Communication Networks. 2020(1), 8868672. DOI: https://doi.org/10.1155/2020/8868672

[86] Poh, G.S., Gope, P., Ning, J., 2019. PrivHome: Privacy-preserving authenticated communication in smart home environment. IEEE Transactions on Dependable and Secure Computing. 18(3), 1095–1107. DOI: https://doi.org/10.1109/TDSC.2019.2914911

[87] Alenezi, M.N., Al-Anzi, F.S., 2021. A study of Z-transform based encryption algorithm. International Journal of Communication Networks and Information Security. 13(2), 302–309. DOI: https://doi.org/10.17762/ijcnis.v13i2.5052

[88] Zhou, X., Guan, J., Xing, L., et al., 2022. Perils and Mitigation of Security Risks of Cooperation in Mobile-as-a-Gateway IoT. Proceedings of the ACM Conference on Computer and Communications Security; November 7–November 11, 2022; Los Angeles, CA, USA. pp. 3285–3299.

[89] Gao, S., Wang, Q., Liu, Y., et al., 2021. A Privacy-Preservation Scheme based on Mobile Terminals in Internet Medical. Available from: https://www.researchsquare.com/article/rs-288792/v1 (cited 5 January 2025).

[90] Batista, E., Moncusi, M.A., López-Aguilar, P., et al., 2021. Sensors for context-aware smart healthcare: A security perspective. Sensors. 21(20), 6886. DOI: https://doi.org/10.3390/s21206886

[91] Tawffaq, M.R., et al. 2024. IoT Security in a Connected World: Analyzing Threats, Vulnerabilities, and Mitigation Strategies. Proceedings of 2024 36th Conference of Open Innovations Association (FRUCT); October 30–November 1, 2024; Lappeenranta, Finland. pp. 626–638.

[92] Jain, R., Rai, M.S.S., 2023. Energy-Efficient and Secure Routing Protocol for Wireless Sensor Networks. Energy. 8(2).

[93] Ikegwu, A.C., Obianuju, O.J., Nwokoro, I.S., et al., 2025. Investigating the Impact of AI/ML for Monitoring and Optimizing Energy Usage in Smart Home. Artificial Intelligence Evolution. 6(1), 30–43. DOI: https://doi.org/https://doi.org/10.37256/aie.6120256065

[94] Mathew, D.E., Ebem, D.U., Ikegwu, A.C., et al., 2025. Recent Emerging Techniques in Explainable Artificial Intelligence to Enhance the Interpretable and Understanding of AI Models for Human. Neural Processing Letters. 57, 16. DOI: https://doi.org/10.1007/s11063-025-11732-2

[95] Abbas, H., Ansari, N.M., Sattar, A., et al., 2025. Enhancing Cryptographic Security with Deep Learning: Intelligent Threat Detection and Attack Prevention. Spectrum of engineering sciences. 3(2), 195–225.

[96] Kumar, R., Kumar, P., C.C., S., et al., 2025. Blockchain and AI in Shaping the Modern Education System. CRC Press: Boca Raton, FL, USA. pp. 1–284.

[97] Song, B., Kim, T., 2024. Analyzing Recent Research Trends in Post-Quantum Cryptography Using Latent Dirichlet Allocation. Proceedings of 2024 15th International Conference on Information and Communication Technology Convergence (ICTC); October 6–October 18, 2024; Jeju Island, Korea. pp. 92-97. IEEE

[98] Li, X., Cheng, J., Shi, Z., et al., 2023. Blockchain security threats and collaborative defense: A literature review. Computers, Materials and Continua. 76(3), 2597–2629. DOI: https://doi.org/10.32604/cmc.2023.040596

Downloads

How to Cite

Ikegwu, A. C., Uzuegbu, V. C., & Alo, U. R. (2025). Review of Embedded Systems and Cyber Threat Intelligence for Enhancing Data Security in Mobile Health. Journal of Electronic & Information Systems, 7(1), 98–120. https://doi.org/10.30564/jeis.v7i1.10050

Issue

Vol. 7 , Iss. 1 (April 2025)

Article Type

Review

License

Copyright © 2025 Anayo Chukwu Ikegwu, Victoria Chibuzo Uzuegbu, Uzoma Rita Alo

Creative Commons License

This is an open access article under the Creative Commons Attribution-NonCommercial 4.0 International (CC BY-NC 4.0) License.

Crossref
Scopus
Google Scholar
Europe PMC