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Title: A stochastic programming approach for risk management in mobile cloud computing
Authors: Hoang, Dinh Thai
Niyato, Dusit
Wang, Ping
Wang, Shaun Shuxun
Nguyen, Diep
Dutkiewicz, Eryk
Keywords: Engineering::Computer science and engineering
Issue Date: 2018
Source: Hoang, D. T., Niyato, D., Wang, P., Wang, S. S., Nguyen, D., & Dutkiewicz, E. (2018). A stochastic programming approach for risk management in mobile cloud computing. Proceedings of 2018 IEEE Wireless Communications and Networking Conference (WCNC). doi:10.1109/WCNC.2018.8377035
Abstract: The development of mobile cloud computing has brought many benefits to mobile users as well as cloud service providers. However, mobile cloud computing is facing some challenges, especially security-related problems due to the growing number of cyberattacks which can cause serious losses. In this paper, we propose a dynamic framework together with advanced risk management strategies to minimize losses caused by cyberattacks to a cloud service provider. In particular, this framework allows the cloud service provider to select appropriate security solutions, e.g., security software/hardware implementation and insurance policies, to deal with different types of attacks. Furthermore, the stochastic programming approach is adopted to minimize the expected total loss for the cloud service provider under its financial capability and uncertainty of attacks and their potential losses. Through numerical evaluation, we show that our approach is an effective tool in not only dealing with cyberattacks under uncertainty, but also minimizing the total loss for the cloud service provider given its available budget.
ISBN: 9781538617342
DOI: 10.1109/WCNC.2018.8377035
Rights: © 2018 IEEE. Personal use of this material is permitted. Permission from IEEE must be obtained for all other uses, in any current or future media, including reprinting/republishing this material for advertising or promotional purposes, creating new collective works, for resale or redistribution to servers or lists, or reuse of any copyrighted component of this work in other works. The published version is available at:
Fulltext Permission: open
Fulltext Availability: With Fulltext
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