Virtualisation security risk assessment for enterprise cloud services based on stochastic game nets model

Liu, Jun-Jie; Rong, Juling

doi:10.1049/iet-ifs.2017.0038

Cited by 9 publications

(4 citation statements)

References 21 publications

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“…Based on graphical tools, the virtualization security risk scenarios of cloud services can be clearly described, and virtualization security risk factors can be accurately evaluated. e analysis results prove that the method has a strong ability to simulate complex and dynamic security problems in cloud services [4]. He et al evaluated the occupational health risk of decorative paint production enterprises, discussed the applicability of occupational hazard risk index model in health risk assessment, and provided a basis for enterprise health management [5].…”

Section: Related Workmentioning

confidence: 99%

Application of Machine Learning in Enterprise Risk Management

Jia

2022

Security and Communication Networks

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With the development of China’s economic globalization, more and more enterprises have realized the importance of risk management. As a new technology, machine learning has injected new vitality into enterprise risk management. This paper analyzes the factors of enterprise risk from the perspective of risk management, explores and analyzes the application of machine learning in enterprise risk management, and points out the direction for scientific enterprise risk management. This paper first establishes a set of credit evaluation factors and then builds a model based on this evaluation factor set, which ensures the accuracy of the evaluation and also adds pertinence. With the increase of the number of samples, the accuracy of the prediction results also increases. Also, basically, with the increase of training samples and realistic samples, the accuracy rate continues to increase. In the data of the experimental results, it is shown in the process of increasing the number of samples from 400 to 600. The accuracy of the traditional method is 0.81 and 0.8316, respectively. The accuracy of the improved method is 0.842 and 0.905, respectively. This paper studies the process of enterprise risk assessment from the perspective of risk management and attempts to build an enterprise risk management model based on industry risk coefficients, which have certain practical significance.

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Section: Related Workmentioning

confidence: 99%

Application of Machine Learning in Enterprise Risk Management

Jia

2022

Security and Communication Networks

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“…Lv et al [22] suggested a security evaluation method for cloud services based on stochastic game nets. Cloud services' virtualization security risk paradigm is properly defined using graphical tools, and virtualization security risk elements are accurately assessed.…”

Section: Literature Reviewmentioning

confidence: 99%

Randomized MILP framework for Securing Virtual Machines from Malware Attacks

Mangalagowri¹,

Venkataraman²

2023

Intelligent Automation &Amp; Soft Computing

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Cloud computing involves remote server deployments with public network infrastructures that allow clients to access computational resources. Virtual Machines (VMs) are supplied on requests and launched without interactions from service providers. Intruders can target these servers and establish malicious connections on VMs for carrying out attacks on other clustered VMs. The existing system has issues with execution time and false-positive rates. Hence, the overall system performance is degraded considerably. The proposed approach is designed to eliminate Cross-VM side attacks and VM escape and hide the server's position so that the opponent cannot track the target server beyond a certain point. Every request is passed from source to destination via one broadcast domain to confuse the opponent and avoid them from tracking the server's position. Allocation of SECURITY Resources accepts a safety game in a simple format as input and finds the best coverage vector for the opponent using a Stackelberg Equilibrium (SSE) technique. A Mixed Integer Linear Programming (MILP) framework is used in the algorithm. The VM challenge is reduced by a firewall-based controlling mechanism combining behavior-based detection and signature-based virus detection. The proposed method is focused on detecting malware attacks effectively and providing better security for the VMs. Finally, the experimental results indicate that the proposed security method is efficient. It consumes minimum execution time, better false positive rate, accuracy, and memory usage than the conventional approach.

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“…These interactions themselves are becoming increasingly complex [24] as the design of such infrastructures follows a tendency towards abstraction. From bare metal to virtual machines, from physical networks to virtualized fabrics, from local storage to distributed datastores, risk is more and more difficult to assess [19] as there is a clear break between physical and virtual infrastructures.…”

Section: Introductionmentioning

confidence: 99%

How IT Infrastructures Break: Better Modeling for Better Risk Management

Somers

Bach

Dagnat

2023

Lecture Notes in Computer Science

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IT infrastructures break. Whether it be computer attacks or software, human or hardware failures, IT safety and security risk is present in many technical and organizational domains. Risk management is therefore essential to ensure infrastructure resilience, compliance with legal and contractual requirements and a better knowledge of what causes what. But risk management is hard to automate, sometimes because criteria are subject to human appreciation, sometimes because of an incomplete or wrong knowledge of the infrastructure itself. And this latter factor has become more evident with the advent of modern cloudnative architectures: complex and dynamic infrastructures make risk assessment difficult. In this article, we propose an approach based on infrastructure modeling to help automate the risk assessment process for IT infrastructures. Instead of focusing first on hazard analysis, our approach attempts to consider (most of) such an analysis as a consequence of infrastructure modeling. By deciding to focus on the infrastructure modeling itself and by involving as many of the company's stakeholders as possible in the process, we intend to make risk assessment more collaborative and thorough, by taking advantage of everyone's expertise.

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Virtualisation security risk assessment for enterprise cloud services based on stochastic game nets model

Cited by 9 publications

References 21 publications

Application of Machine Learning in Enterprise Risk Management

Application of Machine Learning in Enterprise Risk Management

Randomized MILP framework for Securing Virtual Machines from Malware Attacks

How IT Infrastructures Break: Better Modeling for Better Risk Management

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