A Threat Computation Model using a Markov Chain and Common Vulnerability Scoring System and its Application to Cloud Security

Le, Ngoc T.; Hoang, Doan B.

doi:10.18080/jtde.v7n1.181

Cited by 6 publications

(4 citation statements)

References 17 publications

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“…The simulated results revealed that the architecture using the Markov model could be applied to achieve a flexible balance between network operating cost and MEC reliability. For computing the probability distribution of cloud security threats, a novel approach based on a Markov chain and common vulnerability scoring System was proposed in [29]. The approach allowed estimating the probabilities of cloud threats and types of attacks, which were confirmed by the simulation results.…”

Section: State Of the Artmentioning

confidence: 76%

“…The analyzed references considering Markov modelling for assessing the availability and cybersecurity of cloud and IoT systems can be divided into three groups: the references related to assessing availability and reliability [11][12][13][14][15][16][17][18][19][20][21][22][23][24][25][26][27], the references related to assessing cybersecurity [28][29][30][31][32][33][34][35][36][37][38][39], and references related to assessing both availability and cybersecurity as attributes of dependability of cloud and IoT systems [40][41][42][43].…”

Section: State Of the Artmentioning

confidence: 99%

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Combining Markov and Semi-Markov Modelling for Assessing Availability and Cybersecurity of Cloud and IoT Systems

et al. 2022

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This paper suggests a strategy (C5) for assessing cloud and IoT system (CIS) dependability, availability, and cybersecurity based on the continuous collection, comparison, choice, and combination of Markov and semi-Markov models (MMs and SMMs). It proposes the systematic building of an adequate and accurate model to evaluate CISs considering (1) continuous evolution of the model(s) together with systems induced by changes in the CIS or physical and cyber environment parameters; (2) the necessity of collecting data on faults, failures, vulnerabilities, cyber-attacks, privacy violations, and patches to obtain actual data for assessment; (3) renewing the model set based on analysis of CIS operation; (4) the possibility of choice and utilizing “off-the-shelf” models with understandable techniques for their development to assure improved accuracy of assessment; (5) renewing the models during application of CIS by time, component or mixed combining, taking into consideration different operation and maintenance events. The results obtained were algorithms for data collection and analysis, choice, and combining appropriate MM and SMMs and their different types, such as multi-fragmental and multiphase models, considering changing failure rates, cyber-attack parameters, periodical maintenance, etc. To provide and verify the approach, several private and public clouds and IoT systems were researched and discussed in the context of C5 and proposed algorithms.

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Section: State Of the Artmentioning

confidence: 76%

Section: State Of the Artmentioning

confidence: 99%

Combining Markov and Semi-Markov Modelling for Assessing Availability and Cybersecurity of Cloud and IoT Systems

et al. 2022

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“…Let us consider classification methods using the example in the paper [10]. The given table explains the relationship between threats and vulnerabilities as follows:…”

Section: Classification Example With Qanalysismentioning

confidence: 99%

Vulnerability classification using Q-analysis

Polutsyhanova

2023

TACS

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Today, vulnerability analysis is of great importance in assessing system security. This approach is especially important in cyber systems. The complex relationship between vulnerabilities is dictated by the threats that potentially arise from their presence. The work provides a methodology and an example of building, analyzing and classifying vulnerabilities depending on the threats that they generate. This approach will allow a better understanding of the connections between vulnerabilities, as well as the degree of impact of each of them.

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“…The work in Reference 31 proposed a framework for detecting probabilities of compromising the network. Other recent works are also presented in References 32,33 . The presented model is based on dynamic analysis during the deployed phase of the network.…”

Section: Related Workmentioning

confidence: 99%

Probabilistic analysis of security attacks in cloud environment using hidden Markov models

Al‐Karaki

Gawanmeh

Almalkawi

et al. 2020

Trans Emerging Tel Tech

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The rapidly growing cloud computing paradigm provides a cost-effective platform for storing, sharing, and delivering data and computation through internet connectivity. However, one of the biggest barriers for massive cloud adoption is the growing cybersecurity threats/risks that influence its confidence and feasibility. Existing threat models for clouds may not be able to capture complex attacks. For example, an attacker may combine multiple security vulnerabilities into an intelligent, persistent, and sequence of attack behaviors that will continuously act to compromise the target on clouds. Hence, new models for detection of complex and diversified network attacks are needed. In this article, we introduce an effective threat modeling approach that has the ability to predict and detect the probability of occurrence of various security threats and attacks within the cloud environment using hidden Markov models (HMMs). The HMM is a powerful statistical analysis technique and is used to create a probability matrix based on the sensitivity of the data and possible system components that can be attacked. In addition, the HMM is used to provide supplemental information to discover a trend attack pattern from the implicit (or hidden) raw data. The proposed model is trained to identify anomalous sequences or threats so that accurate and up-to-date information on risk exposure of cloud-hosted services are properly detected. The proposed model would act as an underlying framework and a guiding tool for cloud systems security experts and administrators to secure processes and services over the cloud. The performance evaluation shows the effectiveness of the proposed approach to find attack probability and the number of correctly detected attacks in the presence of multiple attack scenarios. INTRODUCTIONToday's cloud datacenters host a vast number of servers with almost infinite computing and storage capacity. Cloud security can be defined as a set of technologies, policies and controls, cloud security is implemented to protect cloud-hosted services, applications, and data. Providing security to customer data is important. Consequently, the major goals of cloud controlling security models includes providing data authenticity, availability, integrity, and confidentiality. 1 If security countermeasures are not provided correctly, then this brings risk to data in transmissions and operations.

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A Threat Computation Model using a Markov Chain and Common Vulnerability Scoring System and its Application to Cloud Security

Cited by 6 publications

References 17 publications

Combining Markov and Semi-Markov Modelling for Assessing Availability and Cybersecurity of Cloud and IoT Systems

Combining Markov and Semi-Markov Modelling for Assessing Availability and Cybersecurity of Cloud and IoT Systems

Vulnerability classification using Q-analysis

Probabilistic analysis of security attacks in cloud environment using hidden Markov models

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