The daily rapid malware growth and spread has enforced the security community of antivirus companies to introduce cloud computing technology to their existing protection methods so as to be able to deal with efficiently the active malware threats. A new hybrid security model, based on cloud computing, should be developed to offer optimized protection to the connected users. In this research, we describe our proposed cloud infrastructure and analyze it with mathematical models to export significant diagrams about various metrics. Our cloud model architecture consists of four layers: the master cloud server, the slave servers, the virtual subservers and the users connected to the cloud. Experimental results demonstrate that our proposed layered cloud architecture verifies the trust of its implementation and establishment, due to the fact that it makes the current architecture more lightweight, efficient and secure for e-health data transmission.
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Caviglione, L., Podolski, M., Mazurczyk, W., & Ianigro, M. (2016). Covert channels in personal cloud storage services: The case of dropbox. IEEE Transactions on Industrial Informatics, 13(4), 1921–1931. https://doi.org/10.1109/TII.2016.2627503.
Chen, D. (2017). Research on traffic flow prediction in the big data environment based on the improved RBF neural network. IEEE Transactions on Industrial Informatics, 13(4), 2000–2008. https://doi.org/10.1109/TII.2017.2682855.
Chen, H. C. H., & Lee, P. P. C. (2014). Enabling data integrity protection in regenerating-coding-based cloud storage: Theory and implementation. IEEE Transactions on Parallel and Distributed Systems, 25(2), 407–416. https://doi.org/10.1109/TPDS.2013.164.
Goswami, K., Lee, J. H., & Kim, B. G. (2016). Fast algorithm for the high efficiency video coding (HEVC) encoder using texture analysis. Information Sciences, 364–365, 72–90. https://doi.org/10.1016/j.ins.2016.05.018.
Goudos, S., Dallas, P., Chatziefthymiou, S., & Kyriazakos, S. (2017). A survey of iot key enabling and future technologies: 5g, mobile iot, sematic web and applications. Wireless Personal Communications, 97(2), 1645–1675.
He, W., Yan, G., & Xu, L. D. (2014). Developing vehicular data cloud services in the iot environment. IEEE Transactions on Industrial Informatics, 10(2), 1587–1595. https://doi.org/10.1109/TII.2014.2299233.
Hu, P., Ning, H., Qiu, T., Zhang, Y., & Luo, X. (2017). Fog computing based face identification and resolution scheme in internet of things. IEEE Transactions on Industrial Informatics, 13(4), 1910–1920. https://doi.org/10.1109/TII.2016.2607178.
Huang, H., Gong, T., Ye, N., Wang, R., & Dou, Y. (2017). Private and secured medical data transmission and analysis for wireless sensing healthcare system. IEEE Transactions on Industrial Informatics, 13(3), 1227–1237. https://doi.org/10.1109/TII.2017.2687618.
Jiang, L., Xu, L. D., Cai, H., Jiang, Z., Bu, F., & Xu, B. (2014). An iot-oriented data storage framework in cloud computing platform. IEEE Transactions on Industrial Informatics, 10(2), 1443–1451. https://doi.org/10.1109/TII.2014.2306384.
Kim, B. G., Hong, G. S., Park, C. S., & Jang, K. S. (2015). A novel hybrid 3D video service algorithm based on scalable video coding (SVC) technology. Displays, 40, 45–52. https://doi.org/10.1016/j.displa.2015.05.005. Next generation TV systems and technologies.
Li, J., Huang, L., Zhou, Y., He, S., & Ming, Z. (2017). Computation partitioning for mobile cloud computing in a big data environment. IEEE Transactions on Industrial Informatics, 13(4), 2009–2018. https://doi.org/10.1109/TII.2017.2651880.
Liu, C., Chen, J., Yang, L. T., Zhang, X., Yang, C., Ranjan, R., et al. (2014). Authorized public auditing of dynamic big data storage on cloud with efficient verifiable fine-grained updates. IEEE Transactions on Parallel and Distributed Systems, 25(9), 2234–2244. https://doi.org/10.1109/TPDS.2013.191.
Memos, V. A., & Psannis, K. E. (2015). A new methodology based on cloud computing for efficient virus detection. In K. Elleithy & T. Sobh (Eds.), New Trends in Networking, Computing, E-Learning, Systems Sciences, and Engineering (pp. 37–47). Berlin: Springer.
Plageras, A. P., Psannis, K. E., Ishibashi, Y. & Kim, B. G. (2016). Iot-based surveillance system for ubiquitous healthcare. In IECON 2016—42nd annual conference of the IEEE industrial electronics society (pp. 6226–6230). https://doi.org/10.1109/IECON.2016.7793281.
Seo, S. H., Nabeel, M., Ding, X., & Bertino, E. (2014). An efficient certificateless encryption for secure data sharing in public clouds. IEEE Transactions on Knowledge and Data Engineering, 26(9), 2107–2119. https://doi.org/10.1109/TKDE.2013.138.
Stergiou, C., & Psannis, K. E. (2017). Efficient and secure big data delivery in cloud computing. Multimedia Tools and Applications, 76(21), 22803–22822. https://doi.org/10.1007/s11042-017-4590-4.
Stergiou, C., & Psannis, K. E. (2017). Recent advances delivered by mobile cloud computing and internet of things for big data applications: A survey. International Journal of Network Management, 27(3), e1930. https://doi.org/10.1002/nem.1930.
Stergiou, C., Psannis, K. E., Kim, B. G., & Gupta, B. (2018). Secure integration of iot and cloud computing. Future Generation Computer Systems, 78, 964–975. https://doi.org/10.1016/j.future.2016.11.031.
Stergiou, C., Psannis, K.E., Plageras, A. P., Kokkonis, G. & Ishibashi, Y. (2017). Architecture for security monitoring in iot environments. In 2017 IEEE 26th international symposium on industrial electronics (ISIE) (pp. 1382–1385). https://doi.org/10.1109/ISIE.2017.8001447.
Tang, B., Chen, Z., Hefferman, G., Pei, S., Wei, T., He, H., et al. (2017). Incorporating intelligence in fog computing for big data analysis in smart cities. IEEE Transactions on Industrial Informatics, 13(5), 2140–2150. https://doi.org/10.1109/TII.2017.2679740.
Tang, Z., Qi, L., Cheng, Z., Li, K., Khan, S. U., & Li, K. (2016). An energy-efficient task scheduling algorithm in DVFS-enabled cloud environment. Journal of Grid Computing, 14(1), 55–74. https://doi.org/10.1007/s10723-015-9334-y.
Tao, F., Cheng, Y., Xu, L. D., Zhang, L., & Li, B. H. (2014). CCIoT-CMfg: Cloud computing and internet of things-based cloud manufacturing service system. IEEE Transactions on Industrial Informatics, 10(2), 1435–1442. https://doi.org/10.1109/TII.2014.2306383.
Wan, J., Tang, S., Li, D., Wang, S., Liu, C., Abbas, H., et al. (2017). A manufacturing big data solution for active preventive maintenance. IEEE Transactions on Industrial Informatics, 13(4), 2039–2047. https://doi.org/10.1109/TII.2017.2670505.
Wollschlaeger, M., Sauter, T., & Jasperneite, J. (2017). The future of industrial communication: Automation networks in the era of the internet of things and industry 4.0. IEEE Industrial Electronics Magazine, 11(1), 17–27. https://doi.org/10.1109/MIE.2017.2649104.
Wressnegger, C., Freeman, K., Yamaguchi, F., & Rieck, K. (2017). Automatically inferring malware signatures for anti-virus assisted attacks. In Proceedings of the 2017 ACM on Asia conference on computer and communications security, ASIA CCS ’17 (pp. 587–598). New York, NY: ACM. https://doi.org/10.1145/3052973.3053002.
Wressnegger, C., Freemany, K., Yamaguchi, F., & Rieck, K. (2017). Automatically inferring malware signatures for anti-virus assisted attacks. In ACM ASIA conference on computer and communications security (CCS).
Yaghmaee, M. H., Moghaddassian, M., & Leon-Garcia, A. (2017). Autonomous two-tier cloud-based demand side management approach with microgrid. IEEE Transactions on Industrial Informatics, 13(3), 1109–1120. https://doi.org/10.1109/TII.2016.2619070.
Yang, K., & Jia, X. (2013). An efficient and secure dynamic auditing protocol for data storage in cloud computing. IEEE Transactions on Parallel and Distributed Systems, 24(9), 1717–1726. https://doi.org/10.1109/TPDS.2012.278.
Zhao, Z., Taal, A., Jones, A., Taylor, I., Stankovski, V., Vega, I. G., et al. (2015). A software workbench for interactive, time critical and highly self-adaptive cloud applications (SWitch). In 2015 15th IEEE/ACM international symposium on cluster, cloud and grid computing (pp. 1181–1184). https://doi.org/10.1109/CCGrid.2015.73.
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Memos, V.A., Psannis, K.E., Goudos, S.K. et al. An Enhanced and Secure Cloud Infrastructure for e-Health Data Transmission. Wireless Pers Commun 117, 109–127 (2021). https://doi.org/10.1007/s11277-019-06874-1
- Cloud security
- Cloud model infrastructure
- Layered cloud architecture
- Mathematical model