A methodology for ranking cloud system vulnerabilities

Kamongi, Patrick; Kotikela, Srujan; Gomathisankaran, Mahadevan; Kavi, Krishna M.

doi:10.1109/icccnt.2013.6726854

Cited by 4 publications

(2 citation statements)

References 5 publications

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“…Therefore, they face many problems in dealing with risk and recovery. Moreover, dependents need more extra services for safety; thus, the presence of a large number of dependents in an area may put more constraints on existing services which in turn is a factor in increasing vulnerability [ 26 , 44 , 54 ].…”

Section: Methodsmentioning

confidence: 99%

A neighborhood-level analysis of association between social vulnerability and COVID-19 in ahvaz, Iran

Arvin

Beiki

Shahraki

2023

International Journal of Disaster Risk Reduction

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Section: Methodsmentioning

confidence: 99%

A neighborhood-level analysis of association between social vulnerability and COVID-19 in ahvaz, Iran

Arvin

Beiki

Shahraki

2023

International Journal of Disaster Risk Reduction

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“…Kamongi et al [16] proposed a method of ranking cloud system vulnerability and implemented it with vulnerability discovery on a cloud web application: the result was a list of ranked vulnerabilities associated with an attack path. They conceptualized cloud vulnerabilities as the attack paths from a pre-generated attack graph.…”

Section: Fuzzy Logic and Related Workmentioning

confidence: 99%

Multiple Layers of Fuzzy Logic to Quantify Vulnerabilities in IoT

Shojaeshafiei¹,

Etzkorn²,

Anderson³

2020

Computer Science &Amp; Information Technology

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Quantifying vulnerabilities of network systems has been a highly controversial issue in the fields of network security and IoT. Much research has been conducted on this purpose; however, these have many ambiguities and uncertainties. In this paper, we investigate the quantification of vulnerability in the Department of Transportation (DOT) as our proof of concept. We initiate the analysis of security requirements, using Security Quality Requirements Engineering (SQUARE) for security requirements elicitation. Then we apply published security standards such as NIST SP-800 and ISO 27001 to map our security factors and sub-factors. Finally, we propose our Multi-layered Fuzzy Logic (MFL) approach based on Goal question Metrics (GQM) to quantify network security and IoT (Mobile Devices) vulnerability in DOT.

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Vulnerability Assessment for Virtual Machines in Virtual Environment of Cloud Computing

Patil

Modi

2018

Advances in Intelligent Systems and Computing

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A methodology for ranking cloud system vulnerabilities

Cited by 4 publications

References 5 publications

A neighborhood-level analysis of association between social vulnerability and COVID-19 in ahvaz, Iran

A neighborhood-level analysis of association between social vulnerability and COVID-19 in ahvaz, Iran

Multiple Layers of Fuzzy Logic to Quantify Vulnerabilities in IoT

Vulnerability Assessment for Virtual Machines in Virtual Environment of Cloud Computing

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