Open Access
Security and Safety
Volume 2, 2023
Article Number 2022010
Number of page(s) 41
Section Medical and Healthcare
Published online 31 January 2023
  1. The precision medicine initiative, 2016. [Google Scholar]
  2. Gambhir SS, Ge TJ and Vermesh O et al. Toward achieving precision health. Sci Transl Med 2018; 10: eaao3612. [CrossRef] [PubMed] [Google Scholar]
  3. Vermeesch JR, Voet T and Devriendt K. Prenatal and pre-implantation genetic diagnosis. Nat Rev Genet 2016; 17: 643–56. [CrossRef] [PubMed] [Google Scholar]
  4. Pathinarupothi RK, Durga P and Rangan ES. IoT-based smart edge for global health: remote monitoring with severity detection and alerts transmission. IEEE Internet Things J 2019; 6: 2449–462. [CrossRef] [Google Scholar]
  5. Satija U, Ramkumar B and Manikandan MS. Real-time signal quality-aware ECG telemetry system for IoT-based health care monitoring. IEEE Internet Things J 2017; 4: 815–23. [CrossRef] [Google Scholar]
  6. Yang Z, Zhou Q and Lei L et al. An IoT-cloud based wearable ECG monitoring system for smart healthcare. J Med Syst 2016; 40: 1–11. [CrossRef] [PubMed] [Google Scholar]
  7. Catarinucci L, de Donno D and Mainetti L et al. An IoT-aware architecture for smart healthcare systems. IEEE Internet Things J 2015; 2: 515–26. [CrossRef] [Google Scholar]
  8. Castillejo P, Martinez JF and Rodriguez-Molina J et al. Integration of wearable devices in a wireless sensor network for an E-health application. IEEE Wireless Commun 2013; 20: 38–49. [CrossRef] [Google Scholar]
  9. Qadri YA, Nauman A and Zikria YB et al. The future of healthcare Internet of Things: a survey of emerging technologies. IEEE Commun Surv Tutorials 2020; 22: 1121–67. [CrossRef] [Google Scholar]
  10. Masud M, Gaba GS and Alqahtani S et al. A lightweight and robust secure key establishment protocol for Internet of Medical Things in covid-19 patients care. IEEE Internet Things J 2021; 8: 15694–703. [CrossRef] [PubMed] [Google Scholar]
  11. Lin H, Garg S and Hu J et al. Privacy-enhanced data fusion for covid-19 applications in intelligent Internet of Medical Things. IEEE Internet Things J 2021; 8: 15683–693. [CrossRef] [PubMed] [Google Scholar]
  12. Yang T, Gentile M and Shen CF et al. Combining point-of-care diagnostics and Internet of Medical Things (IoMT) to combat the covid-19 pandemic. Diagnostics 2020; 10: 224–6. [CrossRef] [Google Scholar]
  13. Liu J, Miao F and Yin L et al. A noncontact ballistocardiography-based iomt system for cardiopulmonary health monitoring of discharged covid-19 patients. IEEE Internet Things J 2021; 8: 15807–17. [CrossRef] [Google Scholar]
  14. Firouzi F, Rahmani AM and Mankodiya K et al. Internet-of-Things and big data for smarter healthcare: from device to architecture applications and analytics. Future Gener Comput Syst 2018; 78: 583–86. [CrossRef] [Google Scholar]
  15. Joyia J, Liaqat RM and Farooq A et al. Internet of medical things (IoMT): applications benefits and future challenges in healthcare domain. J Commun 2017; 12: 240–47. [Google Scholar]
  16. Jara AJ, Zamora-Izquierdo MA and Skarmeta AF. Interconnection framework for mHealth and remote monitoring based on the Internet of Things. IEEE J Sel Areas Commun 2013; 31: 47–65. [CrossRef] [Google Scholar]
  17. Verma P and Sood SK. Fog assisted-IoT enabled patient health monitoring in smart homes. IEEE Internet Things J 2018; 5: 1789–96. [CrossRef] [Google Scholar]
  18. Redondi A, Chirico M and Borsani L et al. An integrated system based on wireless sensor networks for patient monitoring localization and tracking. Ad Hoc Netw 2013; 11: 39–53. [CrossRef] [Google Scholar]
  19. Fan Y, Yin Y and Xu L et al. IoT-based smart rehabilitation system. IEEE Trans Ind Inform 2014; 10: 1568–77. [CrossRef] [Google Scholar]
  20. Occhiuzzi C, Vallese C and Amendola S et al. NIGHT-care: a passive RFID system for remote monitoring and control of overnight living environment. Proc Comput Sci 2014; 32: 190–7. [CrossRef] [Google Scholar]
  21. Liu L, Stroulia E and Nikolaidis I et al. Smart homes and home health monitoring technologies for older adults: a systematic review. Int J Med Inform 2016; 91: 44–59. [CrossRef] [PubMed] [Google Scholar]
  22. Pasluosta CF, Gassner H and Winkler J et al. An emerging era in the management of Parkinson’s disease: wearable technologies and the Internet of Things. IEEE J Biomed Health Inform 2015; 19: 1873–81. [CrossRef] [PubMed] [Google Scholar]
  23. Yang G, Xie L and Mäntysalo M et al. A health-IoT platform based on the integration of intelligent packaging, unobtrusive bio-sensor, and intelligent medicine box. IEEE Trans Ind Inform 2014; 10: 2180–91. [CrossRef] [Google Scholar]
  24. Cynerio. Health it security, 2022. [Google Scholar]
  25. He D, Ye R and Chan S et al. Privacy in the Internet of Things for smart healthcare. IEEE Commun Mag 2018; 56: 38–44. [CrossRef] [Google Scholar]
  26. Masud M, Gaba GS and Choudhary K et al. Lightweight and anonymity-preserving user authentication scheme for IoT-based healthcare. IEEE Internet Things J 2022; 9: 2649–56. [CrossRef] [Google Scholar]
  27. Kumar M and Chand S. A secure and efficient cloud-centric Internet-of-medical-things-enabled smart healthcare system with public verifiability. IEEE Internet Things J 2020; 17: 10650–59. [CrossRef] [Google Scholar]
  28. Stergiou CL and Gupta KEPBB. IoT-based big data secure management in the fog over a 6G wireless network. IEEE Internet Things J 2021; 8: 5164–71. [CrossRef] [Google Scholar]
  29. Lopes APG and Gondim PRL. Mutual authentication protocol for D2D communications in a cloud-based E-health system. Sensors 2020; 20: 2072–95. [CrossRef] [PubMed] [Google Scholar]
  30. Deebak BD and Al-Turjman F. Smart mutual authentication protocol for cloud based medical healthcare systems using Internet of medical things. IEEE J Sel Areas Commun 2021; 39: 346–60. [CrossRef] [Google Scholar]
  31. Cao R, Tang Z and Liu C et al. A scalable multicloud storage architecture for cloud-supported medical Internet of Things. IEEE Internet Things J 2020; 7: 1641–54. [CrossRef] [Google Scholar]
  32. Ning Z, Dong P and Wang X et al. Mobile edge computing enabled 5G health monitoring for Internet of medical things: a decentralized game theoretic approach. IEEE J Sel Areas Commun 2021; 39: 463–78. [CrossRef] [Google Scholar]
  33. Ghubaish A, Salman T and Zolanvari M et al. Recent advances in the internet-of-medical-things (IoMT) systems security. IEEE Internet Things J 2020; 8: 8707–18. [Google Scholar]
  34. Koutras D, Stergiopoulos G and Dasaklis T et al. Security in IoMT communications: a survey. Sensors 2020;20:4828. [CrossRef] [PubMed] [Google Scholar]
  35. Hatzivasilis G, Soultatos O and Ioannidis S et al. Review of security and privacy for the Internet of Medical Things (IoMT). In: 2019 15th International Conference on Distributed Computing in Sensor Systems (DCOSS). IEEE, 2019, 457–64. [Google Scholar]
  36. Hathaliya JJ and Tanwar S. An exhaustive survey on security and privacy issues in healthcare 4.0. Comput Commun 2020; 153: 311–35. [CrossRef] [Google Scholar]
  37. Newaz AI, Sikder AK and Rahman MA et al. A survey on security and privacy issues in modern healthcare systems: attacks and defenses. ACM Trans Comput Healthcare 2021; 2: 1–44. [CrossRef] [Google Scholar]
  38. Yaqoob T, Abbas H and Atiquzzaman M Security vulnerabilities, attacks, countermeasures, and regulations of networked medical devices – a review. IEEE Commun Surv Tutorials 2019; 21: 3723–768. [CrossRef] [Google Scholar]
  39. Smith D and Simpson K. Functional Safety, Routledge, 2004. [CrossRef] [Google Scholar]
  40. Wu J. Development paradigms of cyberspace endogenous safety and security. Sci China Inform Sci 2022; 65: 1–3. [CrossRef] [Google Scholar]
  41. Wu J. Cyberspace endogenous safety and security. Engineering 2021. [PubMed] [Google Scholar]
  42. Fatema N and Brad R. Security requirements, counterattacks and projects in healthcare applications using WSNs – a review. Int J Comput Netw Commun 2014; 2: 1–9. [Google Scholar]
  43. Clausing E, Schiefer M and Lösche U. Tech. rep., Independent IT-Security Institute 2015. [Google Scholar]
  44. Cao X, Shila DM and Cheng Y et al. Ghost-in-ZigBee: Energy depletion attack on zigbee-based wireless networks. IEEE Internet Things J 2016; 3: 816–29. [CrossRef] [Google Scholar]
  45. Gill SS, Xu M and Ottaviani C et al. AI for next generation computing: emerging trends and future directions. Internet Things 2022; 19: 100514. [CrossRef] [Google Scholar]
  46. Sun W, Cai Z and Li Y et al. Security and privacy in the medical internet of things: a review. Secur Commun Netw 2018; 2018: 1–9. [Google Scholar]
  47. Kasyoka P, Kimwele M and Angolo SM. Certificateless pairing-free authentication scheme for wireless body area network in healthcare management system. J Med Eng Technol 2020; 44: 12–9. [CrossRef] [PubMed] [Google Scholar]
  48. Bromwich M and Bromwich R. Privacy risks when using mobile devices in health care. Can Med Assoc J 2016; 188: 855–56. [CrossRef] [PubMed] [Google Scholar]
  49. Raposo VL. Electronic health records: is it a risk worth taking in healthcare delivery?. GMS Health Technol Assess 2015; 11: 1–9. [Google Scholar]
  50. Mooney G. Is HIPAA compliant with the GDPR?, 2018. [Google Scholar]
  51. Pearlman S. What is data integrity and why is it important?, 2019. [Google Scholar]
  52. Bienkowski T. GDPR is explicit about protecting availability, 2018. [Google Scholar]
  53. Crilly P and Muthukkumarasamy V, Using smart phones and body sensors to deliver pervasive mobile personal healthcare. In:Proceedings of the 6th International Conference on Intelligent Sensors, Sensor Networks and Information Processing, 2010, 291–296. [Google Scholar]
  54. Kogetsu A, Ogishima S and Kato K et al. Authentication of patients and participants in health information exchange and consent for medical research: a key step for privacy protection respect for autonomy and trustworthiness. Front Genet 2018; 9: 1–6. [CrossRef] [Google Scholar]
  55. Kambourakis G, Anonymity and closely related terms in the cyberspace: an analysis by example. J Inform Secur Appl 2014; 19: 2–17. [Google Scholar]
  56. Medical devices, 2022. [Google Scholar]
  57. Ray V, Freud applications of fib: invasive fib attacks and countermeasures in hardware security devices. In: East-Coast Focused Ion Beam User Group Meeting, 2009. [Google Scholar]
  58. Tarnovsky C, Security failures in secure devices. Black Hat DC Presentation 2008; 74. [Google Scholar]
  59. Shi Q, Asadizanjani N and Forte DA et al. A layout-driven framework to assess vulnerability of ICs to microprobing attacks. In: 2016 IEEE International Symposium on Hardware Oriented Security and Trust (HOST). IEEE, 2016, 155–60. [Google Scholar]
  60. Quadir SE, Chen J and Forte D et al. A survey on chip to system reverse engineering. ACM J Emerging Technol Comput Syst (JETC) 2016; 13: 1–34. [Google Scholar]
  61. Botero UJ, Wilson R and Lu H et al. Hardware trust and assurance through reverse engineering: a survey and outlook from image analysis and machine learning perspectives. ArXiv preprint [arXiv:2002.04210], 2020. [Google Scholar]
  62. Sidorkin V, van Veldhoven E and van der Drift et al. Sub-10-nm nanolithography with a scanning helium beam. J Vacuum Sci Technol B: Microelectron Nanometer Struct Process Meas Phenom 2009; 27: L18–20. [CrossRef] [Google Scholar]
  63. Fyrbiak M, Wallat S and Swierczynski P et al. HAL the missing piece of the puzzle for hardware reverse engineering, trojan detection and insertion. IEEE Trans Dependable Secure Comput 2018; 16: 498–510. [Google Scholar]
  64. Costin A, Zaddach J and Francillon A et al. A {Large-scale} analysis of the security of embedded firmwares. In: 23rd USENIX Security Symposium (USENIX Security 14), 2014, 95–110. [Google Scholar]
  65. Ben Yehuda R and Zaidenberg NJ. Protection against reverse engineering in ARM. Int J Inform Secur 2020; 19: 39–51. [CrossRef] [Google Scholar]
  66. Vosoughi A and Köse S. Leveraging On-Chip Voltage Regulators Against Fault Injection Attacks. In: Proceedings of the 2019 on Great Lakes Symposium on VLSI, GLSVLSI ‘19. New York, NY, USA: Association for Computing Machinery, 2019, 1–2. [Google Scholar]
  67. Nechvatal J, Barker E and Bassham L et al. Report on the development of the advanced encryption standard (AES). J Res Nat Inst Stand Technol 2001; 106: 511–77. [CrossRef] [Google Scholar]
  68. Tehranipoor M and Koushanfar F. A survey of hardware trojan taxonomy and detection. IEEE Des Test Comput 2010; 27: 10–25. [CrossRef] [Google Scholar]
  69. Wehbe T, Mooney VJ, Javaid AQ et al. A novel physiological features-assisted architecture for rapidly distinguishing health problems from hardware Trojan attacks and errors in medical devices. In: 2017 IEEE International Symposium on Hardware Oriented Security and Trust (HOST). IEEE, 2017, 106–09. [CrossRef] [Google Scholar]
  70. Jordaney R, Sharad K and Dash SK et al. Transcend: Detecting concept drift in malware classification models. In: 26th USENIX Security Symposium (USENIX Security 17), 2017, 625–642. [Google Scholar]
  71. Cai H, Meng N and Ryder B et al. Droidcat: Effective android malware detection and categorization via app-level profiling. IEEE Trans Inform Forensics Secur 2019; 14: 1455–70. [CrossRef] [Google Scholar]
  72. Lei T, Qin Z and Wang Z et al. Evedroid: Event-aware android malware detection against model degrading for IoT devices. IEEE Internet Things J 2019; 6: 6668–80. [CrossRef] [Google Scholar]
  73. Aman MN, Chua KC and Sikdar B. In: Cryptographic Security Solutions for the Internet of Things, IGI Global; 2019, 117–41. [Google Scholar]
  74. Qureshi MA and Munir A. PUF-RAKE: a PUF-based robust and lightweight authentication and key establishment protocol. IEEE Trans Dependable Secure Comput 2021; 19: 2457–75. [Google Scholar]
  75. Wang Z, Ding X and Pang C et al. To detect stack buffer overflow with polymorphic canaries. In: 2018 48th Annual IEEE/IFIP International Conference on Dependable Systems and Networks (DSN). IEEE, 2018, 243–54. [CrossRef] [Google Scholar]
  76. Xu B, Wang W and Hao Q et al. A security design for the detecting of buffer overflow attacks in IoT device. IEEE Access 2018; 6: 72862–869. [CrossRef] [Google Scholar]
  77. Shila DM, Geng P and Lovett T et al. I can detect you: Using intrusion checkers to resist malicious firmware attacks. In: 2016 IEEE Symposium on Technologies for Homeland Security (HST), 2016, 1–6. [Google Scholar]
  78. Hanna S, Rolles R and Molina-Markham A et al. Take Two Software Updates and See Me in the Morning: the Case for Software Security Evaluations of Medical Devices. In: HealthSec, Citeseer, 2011.2011. [Google Scholar]
  79. Aviv A, Černy P and Clark S et al. Security Evaluation of ES&S Voting Machines and Election Management System. In: Proceedings of 2008 USENIX/ACCURATE Electronic Voting Workshop (EVT 2008), 2008, 1–13. [Google Scholar]
  80. Cui A and Stolfo SJ. A quantitative analysis of the insecurity of embedded network devices: results of a wide-area scan. In: Proceedings of the 26th Annual Computer Security Applications Conference, 2010, 97–106. [Google Scholar]
  81. Sutton M. Corporate Espionage for Dummies: The Hidden Threat of Embedded Web Servers, Black Hat USA, 2011. [Google Scholar]
  82. Bettayeb M, Nasir Q and Talib MA. Firmware update attacks and security for IoT devices: survey. In: Proceedings of the ArabWIC 6th Annual International Conference Research Track, 2019, 1–6. [Google Scholar]
  83. Ling Z, Luo J and Xu Y et al. Security vulnerabilities of internet of things: a case study of the smart plug system. IEEE Internet Things J 2017; 4: 1899–909. [CrossRef] [Google Scholar]
  84. One A. Smashing the stack for fun and profit. Phrack Mag 1996; 7: 14–6. [Google Scholar]
  85. Shacham H. The geometry of innocent flesh on the bone: return-into-libc without function calls (on the x86). In: Proceedings of the 14th ACM Conference on Computer and Communications Security, 2007, 552–61. [Google Scholar]
  86. Mohanty A, Obaidat I and Yilmaz F et al. Control-hijacking vulnerabilities in IoT firmware: a brief survey. In: The 1st International Workshop on Security and Privacy for the Internet-of-Things (IoTSec), 2018. [Google Scholar]
  87. Burow N, Carr SA and Nash J et al. Control-flow integrity: Precision, security, and performance. ACM Comput Surv 2017; 50: 1–33. [Google Scholar]
  88. Jin Z, Chen Y and Liu T et al. A novel and fine-grained heap randomization allocation strategy for effectively alleviating heap buffer overflow vulnerabilities. In: Proceedings of the 2019 4th International Conference on Mathematics and Artificial Intelligence, ICMAI 2019. New York, NY, USA: Association for Computing Machinery, 2019, 115–22. , 2019, 115–22. [CrossRef] [Google Scholar]
  89. Xia H, Woodruff J and Ainsworth S et al. CHERIvoke: characterising pointer revocation using CHERI capabilities for temporal memory safety. In: Proceedings of the 52nd Annual IEEE/ACM International Symposium on Microarchitecture, MICRO ‘52. New York, NY, USA: Association for Computing Machinery, 2019;545–557. [CrossRef] [Google Scholar]
  90. Karimi E, Fei Y and Kaeli D et al. Hardware/software obfuscation against timing side-channel attack on a GPU. In 2020 IEEE International Symposium on Hardware Oriented Security and Trust (HOST). IEEE, 2020, 122–31. [CrossRef] [Google Scholar]
  91. Song W, Li B and Xue Z et al. Randomized last-level caches are still vulnerable to cache side-channel attacks! but we can fix it. In: 2021 IEEE Symposium on Security and Privacy (SP), 2021, 955–69. [Google Scholar]
  92. Qureshi MK. New attacks and defense for encrypted-address cache. In: 2019 ACM/IEEE 46th Annual International Symposium on Computer Architecture (ISCA). IEEE, 2019, 360–71. [Google Scholar]
  93. Werner M, Unterluggauer T and Giner L et al. {ScatterCache}: thwarting cache attacks via cache set randomization. In: 28th USENIX Security Symposium (USENIX Security 19), 2019, 675–92. [Google Scholar]
  94. Das D, Maity S and Nasir SB et al. High efficiency power side-channel attack immunity using noise injection in attenuated signature domain. In: 2017 IEEE International Symposium on Hardware Oriented Security and Trust (HOST), 2017, 62–7. [Google Scholar]
  95. Wei L, Luo B and Li Y et al. I know what you see: Power side-channel attack on convolutional neural network accelerators. In: Proceedings of the 34th Annual Computer Security Applications Conference, 2018, 393–406. [Google Scholar]
  96. Delgado-Lozano IM, Tena-SÁnchez E and NÚÑez J et al. Design and analysis of secure emerging crypto-hardware using hyperfet devices. IEEE Trans Emerging Top Comput 2021; 9: 787–96. [CrossRef] [Google Scholar]
  97. Yang WH, Chu LC and Yang SH et al. An enhanced-security buck DC-DC converter with true-random-number-based pseudo hysteresis controller for Internet-of-Everything (IoE) devices. In: 2018 IEEE International Solid-State Circuits Conference (ISSCC). IEEE, 2018, 126–28. [CrossRef] [Google Scholar]
  98. Das D, Nath M and Ghosh S et al. Killing EM side-channel leakage at its source. In: 2020 IEEE 63rd International Midwest Symposium on Circuits and Systems (MWSCAS), 2020, 1108–11. [Google Scholar]
  99. Cheng P, Bagci IE and Roedig U et al. Sonarsnoop: active acoustic side-channel attacks. Int J Inform Secur 2020; 19: 213–28. [CrossRef] [Google Scholar]
  100. de Souza Faria G and Kim HY. Differential audio analysis: a new side-channel attack on PIN pads. Int J Inform Secur 2019; 18: 73–84. [CrossRef] [Google Scholar]
  101. Carmon E, Seifert JP and Wool A et al. Photonic side channel attacks against RSA. In: 2017 IEEE International Symposium on Hardware Oriented Security and Trust (HOST), 2017, 74–8. [Google Scholar]
  102. Rivest RL, Shamir A and Adleman L et al. A method for obtaining digital signatures and public-key cryptosystems. Commun ACM 1978; 27: 120–26. [CrossRef] [Google Scholar]
  103. Aravindhan K and Karthiga R. One-time password: a survey. Int J Emerging Trends Eng Dev 2013; 1: 613–23. [Google Scholar]
  104. Zhang M and Yin Q. Research progress of static password authentication technology. J Cyberspace Secur 2018; 9: 11–4. [Google Scholar]
  105. Chien HY, Ke-Jan J and Tseng YM. An efficient and practical solution to remote authentication: smart card. Comput Secur 2002; 21: 372–75. [CrossRef] [Google Scholar]
  106. Shimizu A. A dynamic password authentication method using a one-way function. Syst Comput Jpn 1991; 22: 32–40. [CrossRef] [Google Scholar]
  107. KumarDas A, Sharma P and Chatterjee S et al. A dynamic password-based user authentication scheme for hierarchical wireless sensor networks. J Netw Comput Appl 2012; 35: 1646–56. [CrossRef] [Google Scholar]
  108. Harn L and Ren J. Generalized digital certificate for user authentication and key establishment for secure communications. IEEE Trans Wireless Commun 2011; 10: 2372–79. [CrossRef] [Google Scholar]
  109. Kumari A, Jangirala S and Abbasi MY et al. ESEAP: ECC-based secure and efficient mutual authentication protocol using smart card. J Inform Secur Appl 2020; 51: 1–12. [Google Scholar]
  110. Easttom C and Mei N. Mitigating implanted medical device cybersecurity risks. In: Proceeding of IEEE 10th Annual Ubiquitous Computing, Electronics & Mobile Communication Conference (UEMCON), 2019, 145–48. [Google Scholar]
  111. Ibtihel N and Hadj SM. Smart ECG monitoring through IoT In: ChinMay C (ed.), 2020. [Google Scholar]
  112. Youssef W, Zaid AO and Mourali MS et al. RFID-based system for secure logistic management of implantable medical devices in Tunisian health centres. In: Proceeding of IEEE International Smart Cities Conference (ISC2), 2019, 83–6. [Google Scholar]
  113. Jain A, Hong L and Bolle R et al. Online fingerprint verification. IEEE Trans Pattern Anal Mach Intell 1997; 19: 302–14. [CrossRef] [Google Scholar]
  114. Datta AK. Advances in Fingerprint Technology, CRC Press, 2001. [Google Scholar]
  115. Bruce V and Young A. Understanding face recognition. Br J Psychol 1986; 77: 305–27. [CrossRef] [PubMed] [Google Scholar]
  116. He X, Yan S and Hu Y et al. Face recognition using laplacianfaces. IEEE Trans Pattern Anal Mach Intell 2005; 27: 328–40. [CrossRef] [PubMed] [Google Scholar]
  117. Frank M, Biedert R and Ma E et al. Touchalytics: on the applicability of touchscreen input as a behavioral biometric for continuous authentication. IEEE Trans Inform Forensics Secur 2013; 8: 136–48. [CrossRef] [Google Scholar]
  118. Zheng N, Bai K and Huang H et al. You are how you touch: user verification on smartphones via tapping behaviors. In: Proceeding of the 22nd IEEE International Conference on Network Protocols. 2014, 221–32. [Google Scholar]
  119. Sitová Z, Šedenka J and Yang Q et al. Hmog: New behavioral biometric features for continuous authentication of smartphone users. IEEE Trans Inform Forensics Secur 2016; 11: 877–92. [CrossRef] [Google Scholar]
  120. Zheng G, Yang W and Johnstone M et al. Securing the elderly in cyberspace with fingerprints, Academic, 2020. [Google Scholar]
  121. Zheng G, Yang W and Valli C et al. Finger-to-heart (F2H): Authentication for wireless implantable medical devices. IEEE J Biomed Health Inform 2019; 23: 1546–57. [CrossRef] [PubMed] [Google Scholar]
  122. Fratini A, Sansone M and Bifulco P et al. Individual identification via electrocardiogram analysis. Biomed Eng Online 2015; 14: 1–23. [CrossRef] [PubMed] [Google Scholar]
  123. Yang M, Liu B and Zhao M et al. Normalizing electrocardiograms of both healthy persons and cardiovascular disease patients for biometric authentication. PLoS ONE 2013; 8: e71523. [Google Scholar]
  124. Irvine JM and Israel SA. A sequential procedure for individual identity verification using ECG. EURASIP J Adv Signal Process 2009; 5: 42–57. [Google Scholar]
  125. Pathoumvanh S, Airphaiboon S and Hamamoto K. Robustness study of ECG biometric identification in heart rate variability conditions. IEEJ Trans Electr Electr Eng 2014; 9: 42–57. [Google Scholar]
  126. Liu J, Yin L and He C et al. A multiscale autoregressive model-based electrocardiogram identification method. IEEE Access 2018; 6: 18251–263. [CrossRef] [Google Scholar]
  127. Sun F, Mao C and Fan X et al. Accelerometer-based speed-adaptive gait authentication method for wearable IoT devices. IEEE Internet Things J 2018; 6: 820–30. [Google Scholar]
  128. Sun F, Zang W and Gravina R et al. Gait-based identification for elderly users in wearable healthcare systems. Inform Fusion 2020; 53: 134–44. [CrossRef] [Google Scholar]
  129. Amin R, Kumar N and Biswas GP et al. A light weight authentication protocol for iot-enabled devices in distributed cloud computing environment. Future Gener Comput Syst 2018; 78: 1005–19. [CrossRef] [Google Scholar]
  130. Wazid M, Das AK and Kumar N et al. Design of secure key management and user authentication scheme for fog computing services. Future Gener Comput Syst 2019; 91: 475–92. [CrossRef] [Google Scholar]
  131. Tutari VH, Das B and Chowdhury DR. A continuous role-based authentication scheme and data transmission protocol for implantable medical devices. In: 2019 2nd International Conference on Advanced Computational and Communication Paradigms (ICACCP), 2019, 1–6. [Google Scholar]
  132. Yen TF, Xie Y and Yu F et al. Host fingerprinting and tracking on the web: privacy and security implications. In: Proceedings of the 19th Annual Network and Distributed System Security Symposium, 2012. [Google Scholar]
  133. Franklin J, McCoy D and Tabriz P et al. Passive Data Link Layer 802.11 Wireless Device Driver Fingerprinting. In: Proceedings of the 15th USENIX Conference on Security Symposium, 2006, 16–89. [Google Scholar]
  134. Desmond LCC, Yuan CC and Pheng TC et al. Identifying unique devices through wireless fingerprinting. In: Proceedings of the 1st ACM Conference on Wireless Network Security, 2008, 46–55. [Google Scholar]
  135. Radhakrishnan SV, Uluagac AS and Beyah R. GTID: a technique for physical device and device type fingerprinting. IEEE Trans Dependable Secure Comput 2015; 12: 519–32. [CrossRef] [Google Scholar]
  136. Hall J, Barbeau M and Kranakis E. Enhancing intrusion detection in wireless networks using radio frequency fingerprinting. In: Proceedings of Communications, Internet, and Information Technology, 2004, 201–06. [Google Scholar]
  137. Brik V, Banerjee S and Gruteser M et al. Wireless device identification with radiometric signatures. In: Proceedings of the 14th ACM International Conference on Mobile Computing and Networking, 2006, 116–27. [Google Scholar]
  138. van Goethem T, Scheepers W and Preuveneers D et al. Accelerometerbased Device Fingerprinting for Multifactor Mobile Authentication. In Proceedings of the 8th International Symposium on Engineering Secure Software and Systems, 2016, 106–21. [CrossRef] [Google Scholar]
  139. Baldini G, Steri G and Dimc F et al. Experimental identification of smartphones using fingerprints of builtin micro­electro mechanical systems. Sensors 2016; 6: 8–18. [Google Scholar]
  140. Zou L, He Q and Wu J. Source cellphone verification from speech recordings using sparse representation. Digital Signal Process 2017; 62: 125–36. [CrossRef] [Google Scholar]
  141. Zhou Z, Diao W and Liu X et al. Acoustic fingerprinting revisited: generate stable device ID stealthily with inaudible sound. In: Proceedings of the 2014 ACM SIGSAC Conference on Computer and Communications Security, 2014, 429–40. [Google Scholar]
  142. Dirik AE, Sencar HT and Memon N. Digital single lens reflex camera identification from traces of sensor dust. IEEE Trans Inform Forensics Secur 2008; 3: 539–52. [CrossRef] [Google Scholar]
  143. Aksu H, Uluagac AS and Bentley ES. Identification of wearable devices with bluetooth. IEEE Trans Sustainable Comput 2021; 6: 221–30. [CrossRef] [Google Scholar]
  144. Bojinov H, Michalevsky Y and Nakibly G et al. Mobile device identification via sensor fingerprinting. ArXiv preprint [arXiv:1408.1416], 2014. [Google Scholar]
  145. Hupperich T, Hosseini H and Holz T. Leveraging sensor fingerprinting for mobile device authentication. Detection Intrusions Malware Vulnerability Assess 2016; 9721: 377–96. [CrossRef] [Google Scholar]
  146. GopeP and Sikdar B. Lightweight and privacy-preserving two-factor authentication scheme for iot devices. IEEE Internet Things J 2019; 6: 580–89. [CrossRef] [Google Scholar]
  147. Chatterjee B, Das D and Maity S et al. RF-PUF: Enhancing IoT security through authentication of wireless nodes using in-situ machine learning. IEEE Internet Things J 2019; 6: 388–98. [CrossRef] [Google Scholar]
  148. Schürmann D and Sigg S. Secure communication based on ambient audio. IEEE Trans Mob Comput 2013; 12: 358–70. [CrossRef] [Google Scholar]
  149. Quach Q, Nguyen N and Dinh T. Secure authentication for mobile devices based on acoustic background fingerprint. Knowl Syst Eng 2014; 244: 375–87. [CrossRef] [Google Scholar]
  150. Karapanos N, Marforio C and Soriente C et al. Sound-proof: usable two­factor authentication based on ambient sound. In: Proceedings of the 24th USENIX Conference on Security Symposium, 2015, 483–98. [Google Scholar]
  151. Mayrhofer R and Gellersen H. Shake well before use: intuitive and secure pairing of mobile devices. IEEE Transac Mob Comput 2009; 8: 792–806. [CrossRef] [Google Scholar]
  152. Han J, Pan S and Sinha MK et al. Smart home occupant identification via sensor fusion across on-object devices. ACM Trans Sensor Networks 2018; 14: 1–22. [CrossRef] [Google Scholar]
  153. Han J, Chung AJ and Sinha MK et al. Do you feel what I hear? enabling autonomous IoT device pairing using different sensor types. In: Proceedings of the 2018 IEEE Symposium on Security and Privacy, 2018, 836–52. [Google Scholar]
  154. Shi C, Liu J and Liu H et al. Smart user authentication through actuation of daily activities leveraging WiFi-enabled IoT. In: Proceedings of the 18th ACM International Symposium on Mobile Ad Hoc Networking and Computing, 2017, 1–10. [Google Scholar]
  155. Kayacik HG, Just M and Baillie L. et al. Data driven authentication: on the effectiveness of user behaviour modelling with mobile device sensors, 2014. [Google Scholar]
  156. Mahalakshmi B and Suseendran G. Data Management, Analytics and Innovation, Springer, 2019, 467–82. [CrossRef] [Google Scholar]
  157. Maithili K, Vinothkumar V and Latha P. Analyzing the security mechanisms to prevent unauthorized access in cloud and network security. J Comput Theor Nanosci 2018; 15: 2059–63. [CrossRef] [Google Scholar]
  158. Chhabra A and Arora S. An elliptic curve cryptography based encryption scheme for securing the cloud against eavesdropping attacks. In: 2017 IEEE 3rd International Conference on Collaboration and Internet Computing (CIC). IEEE, 2017, 243–46. [CrossRef] [Google Scholar]
  159. Abusaimeh H. Security attacks in cloud computing and corresponding defending mechanisims. Int J Adv Trends Comput Sci Eng 2020; 9: 4141–8. [CrossRef] [Google Scholar]
  160. Mehrtak M, SeyedAlinaghi S and MohsseniPour M. et al. Security challenges and solutions using healthcare cloud computing. J Med Life 2021; 14: 448–61. [CrossRef] [PubMed] [Google Scholar]
  161. Maniah, Abdurachman E, Gaol FL and Soewito B. Survey on threats and risks in the cloud computing environment. Proc Comput Sci 2019; 161: 1325–32. [CrossRef] [Google Scholar]
  162. Alzahrani BA, Irshad A and Albeshri A et al. A provably secure and lightweight patient-healthcare authentication protocol in wireless body area networks. Wireless Personal Commun 2021; 117: 47–69. [CrossRef] [Google Scholar]
  163. Xu Z, Xu C and Liang W et al. A lightweight mutual authentication and key agreement scheme for medical internet of things. IEEE Access 2019; 7: 53922–31. [CrossRef] [Google Scholar]
  164. Kasyoka P, Kimwele M and Angolo SM. Certificateless pairing-free authentication scheme for wireless body area network in healthcare management system. J Med Eng Technol 2020; 44: 12–9. [CrossRef] [PubMed] [Google Scholar]
  165. Bhatia T, Verma A and Sharma G. Towards a secure incremental proxy re-encryption for e-healthcare data sharing in mobile cloud computing. Concurrency Comput Pract Experience 2019; 32: 1–16. [Google Scholar]
  166. Shen J, Tan H and Moh S et al. Enhanced secure sensor association and key management in wireless body area networks. J Commun Netw 2015; 17: 453–62. [CrossRef] [Google Scholar]
  167. Zhao H, Xu R and Shu M et al. Physiological-signal-based key negotiation protocols for body sensor networks: a survey. In: Proceeding of IEEE 12th International Symposium on Autonomous Decentralized Systems, 2015. [Google Scholar]
  168. Altop DK, Levi A and Tuzcu V. Deriving cryptographic keys from physiological signals. Pervasive Mob Comput 2016; 39: 65–79. [Google Scholar]
  169. Pirbhulal S, Zhang H and Wu W et al. Heart-beats based biometric random binary sequences generation to secure wireless body sensor networks. IEEE Trans Biomed Eng 2018; 65: 2751–59. [CrossRef] [PubMed] [Google Scholar]
  170. Sun Y and Lo B. An artificial neural network framework for gaitbased biometrics. IEEE J Biomed Health Inform 2019; 23: 987–98. [CrossRef] [PubMed] [Google Scholar]
  171. Poon C, Zhang YT and Bao SD. A novel biometrics method to secure wireless body area sensor networks for telemedicine and m-health. IEEE Commun Mag 2006; 44: 73–81. [CrossRef] [Google Scholar]
  172. Hu C, Cheng X and Zhang F et al. OPFKA: Secure and efficient ordered-physiological feature-based key agreement for wireless body area networks. In: Proceeding of IEEE 12th Int. Symp. Auton. Decentralized Syst., 2013, 14–19. [Google Scholar]
  173. Miao F, Bao S and Li Y. Biometric key distribution solution with energy distribution information of physiological signals for body sensor network security. IET Inform Secur 2013; 7: 87–96. [CrossRef] [Google Scholar]
  174. Ali A and Khan FA. Key agreement schemes in wireless body area networks: taxonomy and state-of-the-art. J Med Syst 2015; 39: 115. [CrossRef] [PubMed] [Google Scholar]
  175. Zaghouani EK, Jemai A and Benzina A et al. ELPA: a new key agreement scheme based on linear prediction of ECG features for WBAN. In: Proceeding of 23rd European Signal Processing Conference (EUSIPCO), 2015. [Google Scholar]
  176. Tams B, Mihailescu P and Munk A. Security considerations in minutiae-based fuzzy vaults. IEEE Trans Inform Forensics Secur 2015; 10: 985–98. [CrossRef] [Google Scholar]
  177. Davis R. The data encryption standard in perspective. IEEE Commun Soc Mag 1978; 16: 5–9. [CrossRef] [Google Scholar]
  178. Lee J, Yu S and Kim M et al. On the design of secure and efficient three-factor authentication protocol using honey list for wireless sensor networks. IEEE Access 2015; 8: 107046–62. [Google Scholar]
  179. Kim Y, Lee WS and Raghunathan V et al. Vibration-based secure side channel for medical devices. In: Proceedings of the 52nd Annual Design Automation Conference, 2015. [Google Scholar]
  180. Kim J, Jin Lee B and Yoo SK. Design of real-time encryption module for secure data protection of wearable healthcare devices. In; Proceeding of 35th Annual International Conference of the IEEE Engineering in Medicine and Biology Society (EMBC), 2013, 2283–86. [Google Scholar]
  181. Mosenia A and Jha NK. Opsecure: A secure unidirectional optical channel for implantable medical devices. IEEE Trans Multi-Scale Comput Syst 2018; 4: 410–19. [CrossRef] [Google Scholar]
  182. Sun F, Zang W and Huang H. Accelerometer-based key generation and distribution method for wearable IoT devices. IEEE Internet Things J 2020; 8: 1636–50. [Google Scholar]
  183. Bao S, Poon C and Zhang Y et al. Using the timing information of heartbeats as an entity identifier to secure body sensor network. IEEE Trans Inform Technol Biomed 2008; 12: 772–9. [CrossRef] [PubMed] [Google Scholar]
  184. Gope P. LAAP: Lightweight anonymous authentication protocol for D2D-aided fog computing paradigm. Comput Secur 2019; 86: 223–37. [CrossRef] [Google Scholar]
  185. Maji S, Banerjee U and Fuller SH et al. A low-power dual-factor authentication unit for secure implantable devices. In: Proceeding of IEEE Custom Integrated Circuits Conference (CICC), 2020. [Google Scholar]
  186. Tehrani MN, Uysal M and Yanikomeroglu H. Device-to-device communication in 5G cellular networks: challenges, solutions, and future directions. IEEE Commun Mag 2014; 52: 86–92. [CrossRef] [Google Scholar]
  187. Wyner AD. The wire-tap channel. Bell Syst Tech J 1975; 54: 1355–87. [CrossRef] [Google Scholar]
  188. Gabry F, Li N and Schrammar N et al. On the optimization of the secondary transmitter’s strategy in cognitive radio channels with secrecy. IEEE J Sel Areas Commun 2014; 32: 451–63. [CrossRef] [Google Scholar]
  189. Mathur S, Trappe W and Mandayam N et al. Radio-telepathy: extracting a secret key from an unauthenticated wireless channel. In: Proceedings of the 14th ACM International Conference on Mobile Computing and Networking, 2008, 128–39. [Google Scholar]
  190. Ahlswede R and Csiszar I. Common randomness in information theory and cryptography. Part I: secret sharing. IEEE Trans Inform Theory 1993; 39: 1121–32. [CrossRef] [Google Scholar]
  191. Sayeed AM and Perrig A. Secure wireless communications: secret keys through multipath. In: Proceedings of IEEE International Conference on Acoustics, Speech and Signal Processing, IEEE, 2008, 3013–16. [Google Scholar]
  192. Chou TH, Draper SC and Sayeed AM. Impact of channel sparsity and correlated eavesdropping on secret key generation from multipath channel randomness. In: Proceedings of IEEE International Symposium on Information Theory, 2010, 2518–22. [Google Scholar]
  193. Awan MF, Kansanen K and Simbor SP et al. RSS-based secret key generation in wireless in-body networks. In: 2019 13th International Symposium on Medical Information and Communication Technology, 2019, 1–6. [Google Scholar]
  194. Ray I, Kumar M and Yu L LRBAC: a location-aware role-based access control model. In: the 2nd international conference on information systems security, 2006, 147–61. [Google Scholar]
  195. Zhang Y and Feng D. A role-based access control model based on space, time and scale. J Comput Res Dev 2010; 7: 1252–60. [Google Scholar]
  196. Macaulay T. RIoT Control: Understanding and Managing Risks and the Internet of Things. Elsevier, 2016. [Google Scholar]
  197. Sun G, Dong Y and Li Y. CP-ABE based data access control for cloud storage. J Commun 2011; 7: 146–52. [Google Scholar]
  198. Ruj S, Stojmenovic M and Nayak A. Decentralized access control with anonymous authentication of data stored in clouds. IEEE Trans Parallel Distrib Syst 2014; 25: 384–94. [CrossRef] [Google Scholar]
  199. Belkhouja T, Sorour S and Hefeida MS. Role-based hierarchical medical data encryption for implantable medical devices. In: Proceedings of IEEE Global Communications Conference (GLOBECOM), 2019. [Google Scholar]
  200. He D, Kumar N and Khan MK et al. Efficient privacy-aware authentication scheme for mobile cloud computing services. IEEE Syst J 2018; 12: 1621–31. [CrossRef] [Google Scholar]
  201. Jariwala VJ and Jinwala DC. Chapter 4 – Adaptable SDA: Secure data aggregation framework in wireless body area networks, Academic, 2020. [Google Scholar]
  202. Kalyani G and Chaudhari S. An efficient approach for enhancing security in internet of things using the optimum authentication key. Int J Comput Appl 2019; 42: 306–14. [Google Scholar]
  203. Chang L and Moskowitz IS. A decision theoretical based system for information downgrading. In: Proceedings of the 5th Conference on Information Sciences, 2000, 82–9. [Google Scholar]
  204. Cramer R, Damgård I and Nielsen JB. Multiparty computation from threshold homomorphic encryption. In: International Conference on the Theory and Applications of Cryptographic Techniques. Springer, 2001, 280–300. [Google Scholar]
  205. Liu X, Choo KKR and Deng RH et al. Efficient and privacy-preserving outsourced calculation of rational numbers. IEEE Trans Dependable Secure Comput 2016; 15: 27–39. [Google Scholar]
  206. Song W, Wang B and Wang Q et al. Publicly verifiable computation of polynomials over outsourced data with multiple sources. IEEE Trans Inform Forensics Secur 2017; 12: 2334–47. [CrossRef] [Google Scholar]
  207. Baudry K. Data center site search and selection, Data Center Handbook: Plan, Design, Build, and Operations of a Smart Data Center, 2021, 367–80. [CrossRef] [Google Scholar]
  208. Mendonca J, Andrade E and Endo PT et al. Disaster recovery solutions for IT systems: a systematic mapping study. J Syst Software 2019; 149: 511–30. [CrossRef] [Google Scholar]
  209. Ko R, Lee SG and Rajan V. Cloud computing vulnerability incidents: A statistical overview, 2013. [Google Scholar]
  210. Garraghan P, Yang R and Wen Z et al. Emergent failures: rethinking cloud reliability at scale. IEEE Cloud Comput 2018; 5: 12–21. [CrossRef] [Google Scholar]
  211. Nachiappan R, Javadi B and Calheiros R et al. Cloud storage reliability for big data applications: a state of the art survey. J Netw Comput Appl. 2017; 97: 35–47. [CrossRef] [Google Scholar]
  212. Kirar A, Yadav AK and Maheswari S. An efficient architecture and algorithm to prevent data leakage in Cloud Computing using multi-tier security approach. In: 2016 International Conference System Modeling & Advancement in Research Trends (SMART). IEEE, 2016, 271–79. [CrossRef] [Google Scholar]
  213. Chen F, Luo Y and Zhang J et al. An infrastructure framework for privacy protection of community medical internet of things. World Wide Web 2018; 21: 33–57. [CrossRef] [Google Scholar]
  214. Xu M and Buyya R Brownout approach for adaptive management of resources and applications in cloud computing systems: a taxonomy and future directions. ACM Comput Surv 2019; 52: 1–27. [Google Scholar]
  215. Zhong Z, Xu M and Rodriguez MA et al. Machine learning-based orchestration of containers: a taxonomy and future directions. ACM Comput Surv 2022; 54: 1–35. [CrossRef] [Google Scholar]
  216. Xu M, Song C and Wu H et al. EsDNN: Deep neural network based multivariate workload prediction in cloud computing environments. ACM Trans Internet Technol 2022; to appear. [Google Scholar]
  217. Kaur K, Gupta I and Singh AK et al. A comparative evaluation of data leakage/loss prevention systems (DLPS), In: Proceedings of 4th International Conference on Computer Science & Information Technology (CS & IT-CSCP), IEEE, 2017, 87–95. [Google Scholar]
  218. Huang H, Sun X and Xiao F et al. Blockchain-based ehealth system for auditable EHRs manipulation in cloud environments. J Parallel Distrib Comput 2021; 148: 46–57. [CrossRef] [Google Scholar]
  219. Pandey AK, Khan AI and Abushark YB et al. Key issues in healthcare data integrity: analysis and recommendations. IEEE Access 2020; 8: 40612–628. [CrossRef] [Google Scholar]
  220. Theodouli A, Arakliotis S, Moschou Ket al. On the design of a blockchain-based system to facilitate healthcare data sharing. In: 2018 17th IEEE International Conference On Trust, Security And Privacy In Computing And Communications/12th IEEE International Conference On Big Data Science And Engineering (TrustCom/BigDataSE). IEEE, 2018, 1374–79. [Google Scholar]
  221. Manogaran G, Thota C and Lopez D et al. Cybersecurity for Industry 4.0, Springer, 2017, 103–26. [CrossRef] [Google Scholar]
  222. Zhang Y, Deng RH and Xu S et al. Attribute-based encryption for cloud computing access control: a survey. ACM Comput Surv 2020; 53: 1–41. [Google Scholar]
  223. Praveen Kumar P, Syam Kumar P and Alphonse PJA. Attribute based encryption in cloud computing: a survey, gap analysis, and future directions. J Netw Comput Appl 2018; 108: 37–52. [CrossRef] [Google Scholar]
  224. Huang Q, Yang Y and Shen M. Secure and efficient data collaboration with hierarchical attribute-based encryption in cloud computing. Future Gener Comput Syst 2017; 72: 239–49. [CrossRef] [Google Scholar]
  225. Yang Y, Chen X and Chen H et al. Improving privacy and security in decentralizing multi-authority attribute-based encryption in cloud computing. IEEE Access 2018; 6: 18009–21. [CrossRef] [Google Scholar]
  226. Li J, Chen N and Zhang Y. Extended file hierarchy access control scheme with attribute-based encryption in cloud computing. IEEE Trans Emerging Top Comput 2021; 9: 983–93. [CrossRef] [Google Scholar]
  227. Marnerides A, Watson M and Shirazi N et al. Malware analysis in cloud computing: network and system characteristics. In: 2013 IEEE Globecom workshops. IEEE, 2013, 482–87. [CrossRef] [Google Scholar]
  228. Watson M, Marnerides A and Mauthe A et al. Malware detection in cloud computing infrastructures. IEEE Trans Dependable Secure Comput 2015; 13: 192–205. [Google Scholar]
  229. Yadav RM. Effective analysis of malware detection in cloud computing. Comput Secur 2019; 83: 14–21. [CrossRef] [Google Scholar]
  230. Zhang W, Lin Y and Wu J et al. Inference attack-resistant e-healthcare cloud system with fine-grained access control. IEEE Trans Serv Comput 2018; 14: 167–78. [Google Scholar]
  231. Ma X, Ma J and Kumari S et al. Privacy-preserving distributed multi-task learning against inference attack in cloud computing. ACM Trans Internet Technol 2021; 22: 1–24. [Google Scholar]
  232. Deznabi I, Mobayen M and Jafari N et al. An inference attack on genomic data using kinship, complex correlations, and phenotype information. IEEE/ACM Trans Comput Biol Bioinform 2017; 15: 1333–43. [Google Scholar]
  233. Shakya S. An efficient security framework for data migration in a cloud computing environment. J Artif Intell 2019; 1: 45–53. [CrossRef] [Google Scholar]
  234. Ngnie Sighom JR, Zhang P and You L. Security enhancement for data migration in the cloud. Future Internet 2017; 9: 23. [CrossRef] [Google Scholar]
  235. Singh S, Jeong YS and Park JH. A survey on cloud computing security: issues, threats, and solutions. J Netw Comput Appl 2016; 75: 200–22. [CrossRef] [Google Scholar]
  236. Wu H, Wolter K and Jiao P et al. Eedto: an energy-efficient dynamic task offloading algorithm for blockchain-enabled iot-edge-cloud orchestrated computing. IEEE Internet Things J 2020; 8: 2163–76. [Google Scholar]
  237. Wu H, Zhang Z and Guan C et al. Collaborate edge and cloud computing with distributed deep learning for smart city internet of things. IEEE Internet Things J 2020; 7: 8099–110. [CrossRef] [Google Scholar]
  238. Xu L, Huang D and Tsai WT. Cloud-based virtual laboratory for network security education. IEEE Trans Educ 2013; 57: 145–50. [Google Scholar]
  239. Xu M, Toosi AN and Buyya R. A self-adaptive approach for managing applications and harnessing renewable energy for sustainable cloud computing. IEEE Trans Sustainable Comput 2021; 6: 544–58. [CrossRef] [Google Scholar]
  240. Souppaya M, Morello J and Scarfone K. Tech. rep., National Institute of Standards and Technology, 2017. [Google Scholar]
  241. Tang J, Cui Y and Li Q et al. Ensuring security and privacy preservation for cloud data services. ACM Comput Surv 2016; 49: 1–39. [Google Scholar]
  242. Wei J, Zhang X and Ammons G et al. Managing security of virtual machine images in a cloud environment. In: Proceedings of the 2009 ACM Workshop on Cloud Computing Security, 2006, 91–6. [Google Scholar]
  243. Loukidis-Andreou F, Giannakopoulos I and Doka K et al. Docker-Sec: a Fully Automated Container Security Enhancement Mechanism. In: 2018 IEEE 38th International Conference on Distributed Computing Systems (ICDCS), 2018, 1561–64. [Google Scholar]
  244. Kwon S and Lee JH. Divds: Docker image vulnerability diagnostic system. IEEE Access 2020; 8: 42666–673. [CrossRef] [Google Scholar]
  245. Huang W, Ganjali A and Kim BH et al. The state of public infrastructure-as-a-service cloud security. ACM Comput Surv 2015; 47: 1–31. [CrossRef] [Google Scholar]
  246. Lin K, Liu W, Zhang K et al. HyperMI: a privilege-level VM protection approach against compromised hypervisor. In: 2019 18th IEEE International Conference On Trust, Security and Privacy in Computing and Communications/13th IEEE International Conference On Big Data Science and Engineering (TrustCom/BigDataSE). IEEE, 2019, 58–65. [Google Scholar]
  247. Li SW, Koh JS and Nieh J. Protecting cloud virtual machines from hypervisor and host operating system exploits. In: 28th USENIX Security Symposium (USENIX Security 19), 2019, 1357–74. [Google Scholar]
  248. Liu W, Zhang K and Tu B et al. HyperPS: a hypervisor monitoring approach based on privilege separation. In: 2019 IEEE 21st International Conference on High Performance Computing and Communications; IEEE 17th International Conference on Smart City; IEEE 5th International Conference on Data Science and Systems (HPCC/SmartCity/DSS). IEEE, 2019, 981–88. [Google Scholar]
  249. Khalimov A, Benahmed S and Hussain R et al. Container-based sandboxes for malware analysis: a compromise worth considering. In: Proceedings of the 12th IEEE/ACM International Conference on Utility and Cloud Computing, 2019, 219–27. [Google Scholar]

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