ESP Journal of Engineering & Technology Advancements |
© 2021 by ESP JETA |
Volume 1 Issue 1 |
Year of Publication : 2021 |
Authors : Satyanarayan Kanungo |
![]() |
Satyanarayan Kanungo, 2021. "Enhancing IoT Security and Efficiency: The Role of Cloud Computing and Machine Learning" ESP Journal of Engineering & Technology Advancements 1(1): 7-14.
The proliferation of the Internet of Things (IoT) has brought numerous benefits, but it has also raised concerns about security and efficiency. The interconnected nature of IoT devices and networks exposes them to various vulnerabilities and risks. To address these challenges, cloud computing and machine learning have emerged as powerful technologies with the potential to enhance IoT security and efficiency. Cloud computing offers scalable and flexible resources for secure data storage, processing, and centralized monitoring of IoT devices and networks. It provides a robust infrastructure that can handle the large volumes of data generated by IoT devices while also offering advanced security features such as encryption and access control. By leveraging cloud computing, organizations can offload computational tasks and focus on strengthening their IoT security measures. Machine learning, on the other hand, plays a crucial role in identifying threats, detecting anomalies, and predicting potential security breaches in IoT systems. By analyzing vast amounts of data collected from IoT devices, machine learning algorithms can learn patterns and behaviors, enabling real-time threat detection and proactive security measures. Additionally, machine learning techniques can optimize resource allocation and energy consumption in IoT deployments, improving overall efficiency. The integration of cloud computing and machine learning in IoT security offers synergistic advantages. Cloud-based machine learning models can be trained and deployed to analyze IoT data in real-time, enabling prompt responses to security incidents. Furthermore, cloud computing provides the necessary computational power and storage for training complex machine learning models, which are then deployed to edge devices for local decision-making.
[1] Capra, M., Peloso, R., Masera, G., Ruo Roch, M., & Martina, M. (2019). Edge computing: A survey on the hardware requirements in the internet of things world. Future Internet, 11, 100.
[2] Luong, N.C., Wang, P., Niyato, D., Wen, Y., & Han, Z. (2017). Resource Management in Cloud Networking Using Economic Analysis and Pricing Models: A Survey. IEEE Communications Surveys & Tutorials, 19, 954–1001.
[3] Breitgand, D., Silva, D.M.D., Epstein, A., Glikson, A., Hines, M.R., Ryu, K.D., & Silva, M.A. (2018). Dynamic Virtual Machine Resizing in a Cloud Computing Infrastructure. U.S. Patent 9,858,095.
[4] Soumya, E., Kumar, V.S., Vineela, T., & Aishwarya, M. (2018). Conducive Tracking, Monitoring, and Managing of Cloud Resources. International Journal of Scientific Research in Computer Science, Engineering and Information Technology, 3, 385–390.
[5] Tsai, W., Sun, X., & Balasooriya, J. (2010). Service-Oriented Cloud Computing Architecture. In Proceedings of the 7th International Conference on Information Technology: New Generations (ITNG) (pp. 684–689). IEEE Computer Society.
[6] Alhamazani, K., Ranjan, R., Mitra, K., Rabhi, F.A., Jayaraman, P.P., Khan, S.U., Guabtni, A., & Bhatnagar, V. (2015). An Overview of the Commercial Cloud Monitoring Tools: Research Dimensions, Design Issues, and State-of-the-art. Computing, 97, 357–377.
[7] Amiri, M., & Khanli, L.M. (2017). Survey on prediction models of applications for resources provisioning in cloud. Journal of Network and Computer Applications, 82, 93–113.
[8] Chard, R., Chard, K., Wolski, R., Madduri, R.K., Ng, B., Bubendorfer, K., & Foster, I.T. (2017). Cost-Aware Cloud Profiling, Prediction, and Provisioning as a Service. IEEE Cloud Computing, 4, 48–59.
[9] Garg, R., & Prasad, V. (2017). Survey Paper on Cloud Demand Prediction and QoS Prediction. International Journal of Advanced Research in Computer Science, 8, 794–799.
[10] Souza, V.B., Masip-Bruin, X., Marín-Tordera, E., Ramírez, W., & Sánchez-López, S. (2017). Proactive vs reactive failure recovery assessment in combined Fog-to-Cloud (F2C) systems. In Proceedings of the 22nd International IEEE Workshop on Computer Aided Modeling and Design of Communication Links and Networks (CAMAD) (pp. 1–5). IEEE.
[11] Kauffman, R.J., Ma, D., & Yu, M. (2018). A Metrics Suite of Cloud Computing Adoption Readiness. Electronic Markets, 28, 11–37.
[12] Prasad, V.K., Shah, M., & Bhavsar, M.D. (2018). Trust Management and Monitoring at an IaaS Level of Cloud Computing. In Proceedings of the 3rd International Conference on Internet of Things and Connected Technologies (ICIoTCT) (pp. 26–27).
[13] Singh, A., & Kinger, S. (2013). An Efficient Fault Tolerance Mechanism Based on Moving Averages Algorithm. International Journal of Advanced Research in Computer Science and Software Engineering, 3, 937–942.
[14] Cai, H., Gu, Y., Vasilakos, A.V., Xu, B., & Zhou, J. (2018). Model-Driven Development Patterns for Mobile Services in Cloud of Things. IEEE Transactions on Cloud Computing, 6, 771–784.
[15] Comuzzi, M., Kotsokalis, C., Spanoudakis, G., & Yahyapour, R. (2009). Establishing and Monitoring SLAs in Complex Service Based Systems. In Proceedings of the IEEE International Conference on Web Services (ICWS) (pp. 783–790). IEEE Computer Society.
[16] Waldman, H., & Mello, D.A.A. (2009). On the Risk of non-compliance with some Plausible SLA Requirements. In Proceedings of the 11th International IEEE Conference on Transparent Optical Networks (pp. 1–4). IEEE.
[17] Kleinberg, J., Ludwig, J., Mullainathan, S., & Obermeyer, Z. (2015). Prediction Policy Problems. American Economic Review, 105, 491–495.
[18] Noshy, M., Ibrahim, A., & Ali, H.A. (2018). Optimization of live virtual machine migration in cloud computing: A survey and future directions. Journal of Network and Computer Applications, 110, 1–10.
[19] Liu, Y., Daum, P.H., McGraw, R., & Miller, M. (2006). Generalized Threshold Function Accounting for Effect of Relative Dispersion on Threshold Behavior of Autoconversion Process. Geophysical Research Letters, 33, 11.
[20] Rai, S.C., Nayak, S.P., Acharya, B., Gerogiannis, V.C., Kanavos, A., & Panagiotakopoulos, T. ITSS: An Intelligent Traffic Signaling System Based on an IoT Infrastructure. Electronics, 12, 1177.
[21] Somani, G., Gaur, M.S., Sanghi, D., Conti, M., & Buyya, R. (2017). DDoS Attacks in Cloud Computing: Issues, Taxonomy, and Future Directions. Computer Communications, 107, 30–48.
[22] Wu, X., Zhang, R., Zeng, B., & Zhou, S. (2013). A Trust Evaluation Model for Cloud Computing. In Proceedings of the 1st International Conference on Information Technology and Quantitative Management (ITQM) (pp. 1170–1177).
[23] Buyya, R., Broberg, J., & Goscinski, A.M. (2010). Cloud Computing: Principles and Paradigms. John Wiley & Sons.
[24] Jennings, B., & Stadler, R. (2015). Resource Management in Clouds: Survey and Research Challenges. Journal of Network and Systems Management, 23, 567–619.
[25] Prasad, V. K., Dansana, D., Bhavsar, M., Acharya, B., Gerogiannis, V. C., & Kanavos, A., November 19). Efficient Resource Utilization in IoT and Cloud Computing. Information. https://doi.org/10.3390/info14110619
[26] National Institute of Standards and Technology. (2019). NIST Special Publication 800-145: The NIST Definition of Cloud Computing.
[27] Tague, P., (2019). Internet of Things (IoT) Security: Current Status and Future Challenges.
[28] Shuja, J., & Latif, S. (2018). Machine Learning Based Security for Internet of Things Devices: A Review. IEEE Access, 6, 72176-72187.
[29] Yassein, M. B., Khamayseh, Y., & Al-Obaidy, M. A. (2018). Internet of Things: Review and Open Research Issues. Journal of Network and Computer Applications, 103, 1-19.
[30] Gubbi, J., Buyya, R., Marusic, S., & Palaniswami, M. (2013). Internet of Things (IoT): A vision, architectural elements, and future directions. Future Generation Computer Systems, 29(7), 1645-1660.
[31] Perera, C., Zaslavsky, A., Christen, P., & Georgakopoulos, D. (2014). Context Aware Computing for The Internet of Things: A Survey. IEEE Communications Surveys & Tutorials, 16(1), 414-454.
[32] Gokul Ramadoss,2023. “Cloud Migration Strategies for EDI Transactions in Healthcare Payor Ecosystems”, N. American. J. of Engg. Research, vol. 4, no. 3, Aug. 2023, Accessed: Oct. 18, 2024. [Online]. Available: https://najer.org/najer/article/view/42
[33] Gokul Ramadoss, "Optimizing TPA Data Exchange in MultiPayor Healthcare Ecosystems: Challenges and Solutions", International Journal of Science and Research (IJSR), Volume 13 Issue 8, August 2024, pp. 919-923, https://www.ijsr.net/getabstract.php?paperid=SR24813060052
Cloud Computing, Machine Learning, IoT Security.