A Study on Machine Learning Based Light Weight Authentication Vector

Do-Hyeon Choi, Jung-Oh Park

Abstract


Artificial Intelligence area has been rapidly advanced around the global companies such as Google, Amazon, IBM and so on. In addition, it is anticipated to facilitate the innovation in a variety of industries in the future. AI provides us with convenience in our lives, on the other hand, the valuable information on the subjects that utilize this has the potential to be exposed at anytime and anywhere. In the next advancement of AI area, the technical developments of the new security are required other than the existing methods. Generation and validation methods of light-weight authentication vector are suggested in this study to be used in many areas as an expanded security function. Upon the results of the capacity analysis, it was verified that efficient and safe security function could be performed using the existing machine learning algorithm. Authentication vector is designed to insert the encrypted data as variable according to the change of time. The security function was performed by comparing coordinate distance values within the authentication vector, and the internal structure was verified to optimize the performance cost required for data reverse search.

Keywords


Artificial Intelligence; Machine Learning; Authentication Vector; Virtualization; Next Generation Security

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References


IDC(International Data Corporation), Worldwide Semiannual Cognitive/Artificial Intelligence Systems Spending Guide, 2016.

Danial Hooshyar, Moslem Yousefi, and Heuiseok Lim, “Data-driven Approaches to Game Player Modeling: A Systematic Literature Reviewâ€, ACM Computing Surveys 50(6), 2017, pp. 1-19.

Yeongwook Yang, Wonhee Yu, and Heuiseok Lim, “Predicting Second Language Proficiency Level Using Linguistic Cognitive Task and Machine Learning Techniquesâ€, Wireless Personal Communications: An International Journal, 86(1), 2016, pp. 271-285.

OWL Cyber Security, OWL Cybersecurity Launches Darknet Index Reranking the Fortune 500 by Darknet Footprint and Security Threat Levels, 2017.

Dakota Rudesill, James Caverlee, Daniel Sui, The Deep Web and the Darknet: A Look Inside the Internet's Massive Black Box, Science+Technology Innovation Program, 2015.

Peter Harrington, Machine Learning in Action, black & white, 2012.

Shai Shalev-Shwartz and Shai Ben-David, “Understanding Machine Learning: From Thory to Algorithmsâ€, Cambridge University Press, 2014.

Leonardo Araujo dos Santos, Artificial Intelligence, GitBook, 2017.

BENGIO, Yoshua, et al, Learning deep architectures for AI, Foundations and trends® in Machine Learning, 2(1), 2009, pp. 1-127.

VMWARE, Virtualization Overview, Vmware White Paper, 2006.

Fayyad-Kazan, Hasan, Luc Perneel, and Martin Timmerman, Benchmarking the performance of Microsoft Hyper-V server, VMware ESXi and Xen hypervisors, Journal of Emerging Trends in Computing and Information Sciences, 4(12), 2013, pp. 922-933.

Graziano, Charles David. A performance analysis of Xen and KVM hypervisors for hosting the Xen Worlds Project, 2011.

Zhang, Minjie, and Raj Jain, Virtualization security in data centers and clouds, http://www.cse.wustl.edu/~jain/index.html, 2011.

Sabahi, Farzad, Secure virtualization for cloud environment using hypervisor-based technology, International Journal of Machine Learning and Computing, 2(1), 2012, pp. 39-45.

Bhunia, Swarup, et al, Hardware Trojan attacks: threat analysis and countermeasures, Proceedings of the IEEE, 102(8), 2014, pp. 1229-1247.

Nguyen, Anh M., et al, Mavmm: Lightweight and purpose built vmm for malware analysis, Computer Security Applications Conference, ACSAC'09, Annual. IEEE, 2009.

Garfinkel, Tal, and Mendel Rosenblum, A Virtual Machine Introspection Based Architecture for Intrusion Detection, Ndss. 3, 2003, pp. 191-206.

Hwang, T., Shin, Y., Son, K., & Park, H, Design of a hypervisor-based rootkit detection method for virtualized systems in cloud computing environments, In Proceedings of the 2013 AASRI Winter International Conference on Engineering and Technology, 2013, pp. 27-32.




DOI: http://dx.doi.org/10.18517/ijaseit.8.4.5738

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