Availability and reliability analysis of cloud computing under economic denial of sustainability (EDoS) attack: a semi-Markov approach

Lalropuia, K.C.; Khaitan, Vandana

doi:10.1007/s10586-021-03257-9

Cited by 7 publications

(10 citation statements)

References 32 publications

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“…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%

“…This model allows finding the main influence factors for the efficiency of the network and strategies to improve its efficiency. To determine the steady state availability and the mean time to failure of the cloud under an economic denial of sustainability (EDoS) attack, the work [36] presented a semi-Markov model. This model is a part of the cost management strategy aimed at preventing a cloud adopter from undergoing bankruptcy.…”

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: 99%

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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“…Shah et al [22] proposed a mitigation technique called Internet Control Messaging Protocol attack detection for flooding attacks which can lead to an EDoS attack. In our recent work [17], we have dealt with the availability and reliability issues of cloud computing under EDoS attack based on a semi-Markov process. However, in this paper [17] we have not taken into account the different strategies that the malicious users as well as the defender can adopt in the event of an EDoS attack.…”

Section: Related Workmentioning

confidence: 99%

“…In our recent work [17], we have dealt with the availability and reliability issues of cloud computing under EDoS attack based on a semi-Markov process. However, in this paper [17] we have not taken into account the different strategies that the malicious users as well as the defender can adopt in the event of an EDoS attack. Chowdhury et al [5] proposed a game theoretical model for mitigating EDoS attacks.…”

Section: Related Workmentioning

confidence: 99%

“…Cloud services with the pay-as-you-go pricing model offer auto-scaling features. Auto-scaling is an underlying feature of cloud computing that facilitates the scaling-up or scaling-down of computing resources based upon the usage of cloud users [17]. That is, the implementation of auto-scaling of the cloud causes dynamic allocation or deallocation of computing resources such as CPUs, memory and networking devices depending on the users’ demands [3].…”

Section: Introductionmentioning

confidence: 99%

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Game theoretic modeling of economic denial of sustainability (EDoS) attack in cloud computing

Lalropuia¹,

Khaitan²

2021

Prob. Eng. Inf. Sci.

Self Cite

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In this paper, we develop a novel game theoretic model of the interactions between an EDoS attacker and the defender based on a signaling game that is a dynamic game of incomplete information. We then derive the best defense strategies for the network defender to respond to the EDoS attacks. That is, we compute the perfect Bayesian Nash Equilibrium (PBE) of the proposed game model such as the pooling PBE, separating PBE and mixed strategy PBE. In the pooling equilibrium, each type of the attacker takes the same action and the attacker's type is not revealed to the defender, whereas in the separating equilibrium, each type of the attacker uses different actions and hence the attacker's type is completely revealed to the defender. On the other hand, in the mixed strategy PBE, both the attacker and the defender randomize their strategies to optimize their payoffs. Numerical illustration is also presented to show the efficacy of the proposed model.

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Uncovering collateral damages and advanced defense strategies in cloud environments against DDoS attacks: A comprehensive review

Verma,

Bharot,

Breslin

et al. 2024

Trans Emerging Tel Tech

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Cloud computing (CC) offers on‐demand computing and resources to users, and organizations, and is also used in many human‐centric intelligent systems. Attacks in cloud networks cause huge damage to service providers and users. Distributed Denial of Service (DDoS) is one of them that greatly impacts the cloud network. The unavailability of resources is the major concern pertaining to cloud service and resources. Apart from the direct targets of a DDoS attack, there are also indirect effects on non‐targets within a cloud network. These effects on the non‐target stakeholders of the cloud are called collateral damages. However, this area of research is not explored much by researchers. Thus, the defense methods pertaining to direct and indirect effects need to be explored. This article aims to describe the consequences of DDoS attacks and the solutions available in the cloud network. The novelty of this article lies in shedding light on the indirect impacts of DDoS in cloud networks and possible solution approaches. The article also provides a comparative analysis of the existing defense solution under different categories and available tools and datasets to evaluate the proposed solutions. The article also mentions the solution considerations and effective solution scenarios. The idea behind this article is to impart guidelines to the researchers, for developing efficient defense solutions against direct target and indirect target DDoS attacks. Moreover, the research shortcomings, pros–cons, and existing challenges are outlined, and directions for future research are mentioned.

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Availability and reliability analysis of cloud computing under economic denial of sustainability (EDoS) attack: a semi-Markov approach

Cited by 7 publications

References 32 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

Game theoretic modeling of economic denial of sustainability (EDoS) attack in cloud computing

Uncovering collateral damages and advanced defense strategies in cloud environments against DDoS attacks: A comprehensive review

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