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Hypervisor Attack Detection Using Advanced Encryption Standard (HADAES) Algorithm on Cloud Data


Affiliations
1 Computer Science Department, SRM Institute of Science and Technology, Kattankulathur, Tamil Nadu, India
2 School of Computing, SRM Institute of Science and Technology, Kattankulathur, Chengalpattu, Tamil Nadu, India
 

Cloud computing demonstrates excellent power to yield cost-efficient, easily manageable, flexible, and charged resources whenever required, over the Internet. Cloud computing, can make the potential of the hardware resources to increase huge through best and shared usage. The growth of the cloud computing concept has also resulted in security challenges, considering that there are resource sharing, and it is moderated with the help of a Hypervisor which can be the target of malicious guest Virtual Machines (VM) and remote intruders. The hypervisor itself is attacked by hackers. Since the hypervisor is attacked, the VMs under the hypervisor is also attacked by the attackers. Hence, to prevent the problems stated above, in this study, Enhanced Particle Swarm Optimization (EPSO) with Hypervisor Attack Detection using Advanced Encryption Standard (HADAES) algorithm is introduced with the intent of improving the cloud performance on the whole. This work contains important phases such as system model, optimal resource allocation, and hypervisor attack detection. The system model contains the data center model, migration request model, and energy model over the cloud computing environment. Resource allocation is done by using the EPSO algorithm which is used to select the optimal resources using local and global best values. Hypervisor attack detection is done by using HADAES algorithm. It is helpful to determine the hypervisor and VM attackers also it is focused to provide higher security for cloud data. From the test result, it is concluded that the proposed algorithm yields superior performance concerning improved reliability, throughput, and reduced energy consumption, cost complexity, and time complexity than the existing methods.

Keywords

Cloud Computing, Hypervisor Attack Detection, Resource Allocation, Enhanced Particle Swarm Optimization (EPSO), Advanced Encryption Standard (AES) algorithm
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  • Gan, C., Feng, Q., Zhang, X., Zhang, Z., and Zhu, Q., 2020. Dynamical propagation model of malware for cloud computing security. IEEE Access, 8, pp.20325-20333.
  • Meng, T., Wolter, K., Wu, H. and Wang, Q., 2018. A secure and costefficient offloading policy for mobile cloud computing against timing attacks. Pervasive and Mobile Computing, 45, pp.4-18.
  • Desai, M.R. and Patel, H.B., 2015, Efficient virtual machine migration in cloud computing. In 2015 Fifth international conference on communication systems and network technologies, pp. 1015-1019.
  • Hanini, M., Kafhali, S.E. and Salah, K., 2019. Dynamic VM allocation and traffic control to manage QoS and energy consumption in a cloud computing environment. International Journal of Computer
  • Applications in Technology, 60(4), pp.307-316.
  • Perez-Botero, D., Szefer, J. and Lee, R.B., 2013, Characterizing hypervisor vulnerabilities in cloud computing servers. In Proceedings of the 2013 international workshop on Security in cloud computing (pp. 310).
  • Nezarat, A. and Shams, Y., 2017. A game theoretic-based distributed detection method for VM-to-hypervisor attacks in a cloud environment. The Journal of Supercomputing, 73(10), pp.4407-4427.
  • Cao, T., Mao, J., Bhattacharya, T., Peng, X., Ku, W.S. and Qin, X., 2021, DDoS Detection Systems for Cloud Data Storage. In 2021 Third IEEE International Conference on Trust, Privacy and Security in Intelligent Systems and Applications (TPS-ISA), pp. 183-190.
  • Velliangiri, S. and Pandey, H.M., 2020. Fuzzy-Taylor-elephant herd optimization inspired Deep Belief Network for DDoS attack detection and comparison with state-of-the-art algorithms. Future Generation Computer Systems, 110, pp.80-90.
  • Varadharajan, V. and Tupakula, U., 2014. Security as a service model for cloud environment. IEEE Transactions on Network and Service Management, 11(1), pp.60-75.
  • Nikolai, J. and Wang, Y., 2014, Hypervisor-based cloud intrusion detection system. In 2014 International Conference on Computing, Networking and Communications (ICNC), pp. 989-993.
  • Dildar, M. S., Khan, N., Abdullah, J. B., & Khan, A. S. (2017). An effective way to defend the hypervisor attacks in cloud computing. In 2017 2ndInternational Conference on Anti-Cyber Crimes (ICACC), pp.
  • -159.
  • Feng, M., Wang, X., Zhang, Y. and Li, J., 2012, Multi-objective particle swarm optimization for resource allocation in cloud computing. In 2012 IEEE 2nd
  • International Conference on Cloud Computing and
  • Intelligence Systems, vol. 3, pp. 1161-1165.
  • Teng, L., Li, H., Yin, S., & Sun, Y. (2020). A Modified Advanced Encryption Standard for Data Security. Int. J. Netw. Secure., 22(1), 112117.
  • Anumukonda, N.S.K., Yadav, R.K. and NS, R., 2021, A Painstaking Analysis of Attacks on Hypervisors in Cloud Environment. In 2021 6th International Conference on Machine Learning Technologies, pp. 150157.
  • Annadanam, C.S., Chapram, S. and Ramesh, T., 2020. Intermediate node selection for Scatter-Gather VM migration in the cloud data center. Engineering Science and Technology, an International Journal, 23(5), pp.989-997.
  • Bansal, M. and Malik, S.K., 2020. A multi-faceted optimization scheduling framework based on the particle swarm optimization algorithm in cloud computing. Sustainable Computing: Informatics and Systems, 28, pp.1-8.
  • Saeedi, S., Khorsand, R., Bidgoli, S.G. and Ramezanpour, M., 2020.
  • Improved many-objective particle swarm optimization algorithm for scientific workflow scheduling in cloud computing. Computers & Industrial Engineering, 147, pp.1-23.
  • Pendli, V., Pathuri, M., Yandrathi, S. and Razaque, A., 2016, Improvising performance of advanced encryption standard algorithm.
  • In 2016 second international conference on mobile and secure services (MobiSecServ), pp. 1-5.
  • Kaushik, S. and Gandhi, C., 2020. Capability-based outsourced data access control with assured file deletion and efficient revocation with trust factor in cloud computing. International Journal of Cloud Applications and Computing (IJCAC), 10(1), pp.64-84.
  • Xiaoyu Li, Shaohua Tang, Lingling Xu, Huaqun Wang, and Jie Chen, “Two-Factor Data Access Control With Efficient Revocation for MultiAuthority Cloud Storage Systems”, IEEE Access, Volume 5, 2017.

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  • Hypervisor Attack Detection Using Advanced Encryption Standard (HADAES) Algorithm on Cloud Data

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Authors

R. Mangalagowri
Computer Science Department, SRM Institute of Science and Technology, Kattankulathur, Tamil Nadu, India
Revathi Venkataraman
School of Computing, SRM Institute of Science and Technology, Kattankulathur, Chengalpattu, Tamil Nadu, India

Abstract


Cloud computing demonstrates excellent power to yield cost-efficient, easily manageable, flexible, and charged resources whenever required, over the Internet. Cloud computing, can make the potential of the hardware resources to increase huge through best and shared usage. The growth of the cloud computing concept has also resulted in security challenges, considering that there are resource sharing, and it is moderated with the help of a Hypervisor which can be the target of malicious guest Virtual Machines (VM) and remote intruders. The hypervisor itself is attacked by hackers. Since the hypervisor is attacked, the VMs under the hypervisor is also attacked by the attackers. Hence, to prevent the problems stated above, in this study, Enhanced Particle Swarm Optimization (EPSO) with Hypervisor Attack Detection using Advanced Encryption Standard (HADAES) algorithm is introduced with the intent of improving the cloud performance on the whole. This work contains important phases such as system model, optimal resource allocation, and hypervisor attack detection. The system model contains the data center model, migration request model, and energy model over the cloud computing environment. Resource allocation is done by using the EPSO algorithm which is used to select the optimal resources using local and global best values. Hypervisor attack detection is done by using HADAES algorithm. It is helpful to determine the hypervisor and VM attackers also it is focused to provide higher security for cloud data. From the test result, it is concluded that the proposed algorithm yields superior performance concerning improved reliability, throughput, and reduced energy consumption, cost complexity, and time complexity than the existing methods.

Keywords


Cloud Computing, Hypervisor Attack Detection, Resource Allocation, Enhanced Particle Swarm Optimization (EPSO), Advanced Encryption Standard (AES) algorithm

References





DOI: https://doi.org/10.22247/ijcna%2F2022%2F215916