A New Methodology Based on Cloud Computing for Efficient Virus Detection

Memos, Vasileios A.; Psannis, Kostas E.

doi:10.1007/978-3-319-06764-3_6

Cited by 2 publications

(4 citation statements)

References 5 publications

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“…Cloud computing technology is a new approach applied to antivirus software as a solution to the increased malware samples discovered and spread on the internet every day. Last years, several research studies have taken place in the computer and network security areas, and all of them converge to the importance of cloud technology incorporation to the existing antivirus software [13,[16][17][18].…”

Section: Related Workmentioning

confidence: 99%

“…Today viruses and other malicious software (malware) have increased and spread rapidly daily. Previous existing protection methods provided by traditional antivirus companies were not adequate to deal with this problem, and thus security community has turned to cloud computing technology to reinforce antivirus software against such threats [13].…”

Section: Introductionmentioning

confidence: 99%

“…In addition, since user computers do not need to store virus signatures and all the malware analysis takes part in the "cloud"-in the cloud servers where signatures are collected into it-the new cloud based antivirus programs consume low memory and system resources and the most of the processes of these programs take part in the connected server somewhere in the cloud. In other words, these programs operate as clients in the cloud environment [13,[16][17][18].…”

Section: Introductionmentioning

confidence: 99%

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An Enhanced and Secure Cloud Infrastructure for e-Health Data Transmission

Memos

Psannis

Goudos

et al. 2019

Wireless Pers Commun

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The daily rapid malware growth and spread has enforced the security community of antivirus companies to introduce cloud computing technology to their existing protection methods so as to be able to deal with efficiently the active malware threats. A new hybrid security model, based on cloud computing, should be developed to offer optimized protection to the connected users. In this research, we describe our proposed cloud infrastructure and analyze it with mathematical models to export significant diagrams about various metrics. Our cloud model architecture consists of four layers: the master cloud server, the slave servers, the virtual subservers and the users connected to the cloud. Experimental results demonstrate that our proposed layered cloud architecture verifies the trust of its implementation and establishment, due to the fact that it makes the current architecture more lightweight, efficient and secure for e-health data transmission.

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Section: Related Workmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

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An Enhanced and Secure Cloud Infrastructure for e-Health Data Transmission

Memos

Psannis

Goudos

et al. 2019

Wireless Pers Commun

Self Cite

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“…Working towards this direction, Pumma, Achalakul, and Li argued that suitable amount of cloud resources should be determined prior to the start of execution. Consequently, the field of predicting and provisioning cloud resources gained momentum. Nevertheless, the work on big data application scheduling is still sparse.…”

Section: Preliminariesmentioning

confidence: 99%

Dynamic resource allocation for big data streams based on data characteristics (5Vs)

Kaur

Sood

2017

Int J Network Mgmt

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Summary Various Internet‐based applications such as social media, business transactions, mobile applications, cyber‐physical systems, and Internet of Things have led to the generation of big data streams in every field. The growing need to extract knowledge from big data streams has pioneered the challenge of selecting appropriate cloud resources. The current techniques allocate resources based on data characteristics. But because of the stochastic nature of data generation, the characteristics of data in big data streams are unknown. This poses difficulty in selecting and allocating appropriate resources to big data stream. Working towards this direction, this paper proposes a system that predicts the data characteristics in terms of volume, velocity, variety, variability, and veracity. The predicted values are expressed in a quadruple called Characteristics of Big data (CoBa). Thereafter, the proposed system uses self‐organizing maps to dynamically create clusters of cloud resources. One of these clusters is allocated to the big data stream based on its CoBa. The proposed system is dynamic in the sense that it changes the cloud cluster allocated to big data stream if its CoBa changes. Experimental results show that the proposed system has a performance edge over other streaming data processing tools such as Storm, Flume, and S4.

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A New Methodology Based on Cloud Computing for Efficient Virus Detection

Cited by 2 publications

References 5 publications

An Enhanced and Secure Cloud Infrastructure for e-Health Data Transmission

An Enhanced and Secure Cloud Infrastructure for e-Health Data Transmission

Dynamic resource allocation for big data streams based on data characteristics (5Vs)

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