Secure Cloud Auditability for Virtual Machines by Adaptive Characterization Using Machine Learning Methods
With the Present days increasing demand for the higher performance with the application developers have started considering cloud computing and cloud-based data centres as one of the prime options for hosting the application. Number of parallel research outcomes have for making a data centre secure, the data centre infrastructure must go through the auditing process. During the auditing process, auditors can access VMs, applications and data deployed on the virtual machines. The downside of the data in the VMs can be highly sensitive and during the process of audits, it is highly complex to permits based on the requests and can increase the total time taken to complete the tasks. Henceforth, the demand for the selective and adaptive auditing is the need of the current research. However, these outcomes are criticised for higher time complexity and less accuracy. Thus, this work proposes a predictive method for analysing the characteristics of the VM applications and the characteristics from the auditors and finally granting the access to the virtual machine by building a predictive regression model. The proposed algorithm demonstrates 50% of less time complexity to the other parallel research for making the cloud-based application development industry a safer and faster place.
Dean, J., Corrado, G. S., & Monga, R. (2013). Large scale distributed deep networks. Proceedings of International Conference on Neural Information Process System, 1223-1231.
He, K., Huang, C., Shi, J., & Wang, J. (2016). Public integrity auditing for dynamic regenerating code-based cloud storage. Proceedings of IEEE Symposium of Computer and Communication (ISCC), 581-588.
HP Integrity Virtual Machines Version 4.0 Installation, Configuration, and Administration. URL: https://docstore.mik.ua/manuals/hp-ux/en/T2767-90141/ ch03.html
Hussain, A., & Subashini, R. (2019), Identity-based Proxy-Oriented Data Uploading and Remote Data Integrity Checking in Public Cloud. IEEE Transactions on Information Forensics Security, 11(6), 1165-1176.
Li, M. (2014). Scaling distributed machine learning with the parameter server. Proceedings of International Conference on Big Data Science in Computers, 583-598.
Li, Z., & Smola, A. (2013). Parameter Server for Distributed Machine Learning. Proceeding of Big Data Learners in NIPS Workshop,1-10.
Research Report from VM Ware. http://aspiresignaturetechnology.com/VMWare-installation-support.html
Shen, W., Qin, J., Yu, J., Hao, R., & Hu, J. (2019). Enabling Identity-Based Integrity Auditing and Data Sharing with Sensitive Information Hiding for Secure Cloud Storage. IEEE Transactions on Information Forensics Security, 14(2), 331-346.
Soner Sevinc, Planet Lab Data Sets https//www.planet-lab.org/datasets.
Sookhak, M., Yu, F. R., & Zomaya, A. Y. (2018). Auditing big data storage in cloud computing using divide and conquer tables. IEEE Transactions on Parallel Distributed. Systems, 29(5), 999-1012.
Wang, C., Chow, S. S. M., & Wang, Q. (2013). Privacy-Preserving Public Auditing For Secure Cloud Storage. IEEE Transactions on Computers, 62(2), 362-375.
Wang, H. ( 2015). Identity-based Distributed Provable Data Possession in Multi Cloud Storage. IEEE Transactions on Services Computers, 8(2), 328-340.
Yan, H., Li, J., Han, J., & Zhang, Y. (2017). A Novel Efficient Remote Data Possession Checking Protocol in Cloud Storage. IEEE Transactions on Information Forensic Security, 12(1), 78-88.
Yu, Y., Au, M. H., Ateniese, G., Huang, X., Susilo, W., Dai, Y., & Min, G. (2017). Identity-Based Remote Data Integrity Checking With Perfect Data Privacy Preserving for Cloud Storage. IEEE Transactions on Information Forensics Security, 12(4), 767-778.
Zhang, J. & Dong, Q. (2016). Efficient ID-based public auditing for the outsourced data in cloud storage. Information Science, 343(1), 1-14.
Zhao, H., Yao, X., Zheng, X., Qiu, T., & Ning, H. (2019). User Stateless Privacy-Preserving TPA Auditing Scheme for Cloud Storage. Journal of Networks and Computer Applications, 129(1), 62-70.
Zhu, H., Yuan, Y., Chen, Y., Zha, Y., Xi, W., Jia,B., & Xin, Y.(2019). A secure and efficient data integrity verification scheme for cloud-IoT based on short signature. IEEE Access, 7(1), 90036-90044.