Abstract
Cloud computing has the potential to offer an abundance of computing resources on demand due to its high scalability, which eliminates the need for providers to plan far in advance for hardware provisioning. Security remains a significant challenge in promoting cloud computing, but artificial intelligence (AI) can improve cloud services by enhancing security features. The privacy issue arises from the multiple data storage locations and available cloud services. Identity management services are crucial in establishing secure and efficient relationships in the cloud and cross-cloud environments by authenticating users based on their identity properties and past interactions. To incorporate AI, cloud security must be improved to provide an effective solution for data storage. We suggest AI-enabled cloud services security in this proposed architecture to protect cloud service users. Our model uses AI to identify users’ identities and restricts malicious access to cloud services, and it was trained based on CloudSim-generated datasets. Quality of services (QoS) was used to measure the architecture’s efficiency, and the results showed that the proposed architecture was effective and fruitful for users. The results effectively show the improvements in network throughput under congestion control with the proposed approach compared with existing state-of-the-art techniques.
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References
Ahmad, S., Khan, S., Jamil, F., Qayyum, F., Ali, A., Kim, D.: Design of a general complex problem-solving architecture based on task management and predictive optimization. Int. J. Distrib. Sensor Netw. 18(6), 15501329221107868 (2022)
Razaque, A., Frej, M., Alotaibi, B., Alotaibi, M.: Privacy preservation Models for Third-Party Auditor over Cloud Computing: a Survey (2021)
Werner, J., Westphall, C.M., Westphall, C.B.J.C.N.: Cloud identity management: a survey on privacy strategies. Comput. Netw. 122, 29–42 (2017)
Ali, A., et al.: An efficient dynamic-decision based task scheduler for task offloading optimization and energy management in mobile cloud computing. Sensors. 21(13), 4527 (2021)
Ali, A., Iqbal, M.M.J.I.A.: A cost and energy efficient task scheduling technique to offload microservices based applications in mobile cloud computing. IEEE Access. 10, 46633–46651 (2022)
Bowers, K.D., Juels, A., Oprea, A.: HAIL: a high-availability and integrity layer for cloud storage. In: Proceedings of the 16th ACM Conference on Computer and Communications Security, pp. 187–198 (2009)
Haji, L.M., Ahmad, O.M., Zeebaree, S., Dino, H.I., Zebari, R.R., Shukur, H.M.J.: Impact of cloud computing and internet of things on the future internet. Technol. Rep. Kansai Univ. 62(5), 2179–2190 (2020)
Naidu, P.R., Guruprasad, N., Gowda, V.D.: A high-availability and integrity layer for cloud storage, cloud computing security: from single to multi-clouds. J. Phys. Conf. Ser. 1921(1), 012072 (2021)
Torok, G., Day, M.R., Hartman-Baker, R.J., Snavely, C.: Iris: allocation banking and identity and access management for the exascale era. In: SC20: International Conference for High Performance Computing, Networking, Storage and Analysis, pp. 1–11. IEEE (2020)
Pöhn, D., Hommel, W.: Proven and modern approaches to identity management. In: Advances in Cybersecurity Management, pp. 421–443. Springer (2021)
Diaz-Piraquive, F.N., Ortiz-Ruiz, E., González-Aragón, J.J., Avila-Bermeo, H.H., Parada-Jaimes, W.D.: Knowledge management applied in the comparative study of the IRETE intrusion methodology to access to the computer systems. In: International Conference on Knowledge Management in Organizations, pp. 354–365. Springer (2021)
Ali, A., Jamil, F., Whangbo, T., Ahmad, S.: AI-Enabled Cybernetic Analytics of Security Models for Smart Serious Games-Based Mobile Operating Systems
Ali, A., et al.: VABLOCK: a blockchain-based secure communication in V2V network using icn network support technology. Microprocess. Microsyst. 93, 104569 (2022)
Zhang, Z., et al.: Artificial intelligence in cyber security: research advances, challenges, and opportunities. Artif. Intell. Rev. 55(2), 1029–1053 (2022)
Singh, P., Singh, N., Luxmi, P.R., Saxena, A.: Artificial intelligence for smart data storage in cloud-based IoT. In: Transforming Management with AI, Big-Data, and IoT, pp. 1–15. Springer (2022)
Ahmad, S., Mehfuz, S., Beg, J.: Empirical analysis of security enabled quantum computing for cloud environment. In: Quantum and Blockchain for Modern Computing Systems: Vision and Advancements, pp. 103–125. Springer (2022)
N. J. S. C. Moustafa and Society: A new distributed architecture for evaluating AI-based security systems at the edge: network TON_IoT datasets. Sustain. Cities Soc. 72, 102994 (2021)
Abd El-Latif, A.A., Abd-El-Atty, B., Hossain, M.S., Elmougy, S., Ghoneim, A.J.I.A.: Secure quantum steganography protocol for fog cloud internet of things. IEEE Access. 6, 10332–10340 (2018)
Tabrizchi, H., Rafsanjani, M.K.: A survey on security challenges in cloud computing: issues, threats, and solutions. J. Supercomput. 76(12), 9493–9532 (2020)
Li, Y., Kumar, R., Lasecki, W.S., Hilliges, O.: Artificial intelligence for HCI: A modern approach. In: Extended Abstracts of the 2020 CHI Conference on Human Factors in Computing Systems, pp. 1–8 (2020)
Abd-El-Atty, B., Iliyasu, A.M., Alaskar, H., El-Latif, A., Ahmed, A.J.S.: A robust quasi-quantum walks-based steganography protocol for secure transmission of images on cloud-based E-healthcare platforms. Sensors. 20(11), 3108 (2020)
Andrew, S., Watson, S., Oh, T., Tigwell, G.W.: A review of literature on accessibility and authentication techniques. In: The 22nd International ACM SIGACCESS Conference on Computers and Accessibility, pp. 1–4 (2020)
Jagadamba, G., Sheeba, R., Brinda, K., Rohini, K., Pratik, S.: Adaptive e-learning authentication and monitoring. In: 2020 2nd International Conference on Innovative Mechanisms for Industry Applications (ICIMIA), pp. 277–283. IEEE (2020)
Rizvi, S., Mitchell, J., Razaque, A., Rizvi, M.R., Williams, I.J.: A fuzzy inference system (FIS) to evaluate the security readiness of cloud service providers. J. Cloud Comput. 9(1), 1–17 (2020)
Alakbarov, R.G.: Challenges of mobile devices’ resources and in communication channels and their solutions. Int. J. Comp. Netw. Inf. Secur. 13(1), 39–46 (2021)
Fellah, H., Mezioud, C., Batouche, M.C.: Mobile cloud computing: architecture, advantages and security issues. In: Proceedings of the 3rd International Conference on Networking, Information Systems & Security, pp. 1–9 (2020)
Butt, U.A., Amin, R., Mehmood, M., Aldabbas, H., Alharbi, M.T., Albaqami, N.: Cloud security threats and solutions: A survey. Wirel. Pers. Commun. 128(1), 387–413 (2023)
Abd El-Latif, A.A., et al.: Secret images transfer in cloud system based on investigating quantum walks in steganography approaches. Phys. A Stat. Mech. Appl. 541, 123687 (2020)
Alibrahemi, D.A.M.: Block RADG Cipher Automata in Cloud Computing Networks. Ministry of Higher Education (2018)
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Jamil, H., Ali, A., Ammi, M., Kirichek, R., Muthanna, M.S.A., Jamil, F. (2024). Machine Learning–Based Identity and Access Management for Cloud Security. In: Abd El-Latif, A.A., Tawalbeh, L., Maleh, Y., Gupta, B.B. (eds) Secure Edge and Fog Computing Enabled AI for IoT and Smart Cities . EAI/Springer Innovations in Communication and Computing. Springer, Cham. https://doi.org/10.1007/978-3-031-51097-7_15
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