SDS&lt;inf&gt;2&lt;/inf&gt;: A novel software-defined security service for protecting cloud computing infrastructure

Farahmandian, Sara; Hoang, Doan B.

doi:10.1109/nca.2017.8171388

Cited by 7 publications

(1 citation statement)

References 2 publications

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“…Initial experiment over average round trip delay among producers and customers is reported. Farahmandian and Hoang [9] designed an software-defined security service (SDS2) to protect cloud domains. This SDS2 mainly focuses on defining the concerns of security related to the virtual and physical boundaries of tenants, resources, data, and detection of security breaches via limits violations.…”

Section: Related Workmentioning

confidence: 99%

Security and risk analysis in the cloud with software defined networking architecture

Thatha¹,

Donepudi²,

Safali

et al. 2023

IJECE

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Cloud computing has emerged as the actual trend in business information technology service models, since it provides processing that is both cost-effective and scalable. Enterprise networks are adopting software-defined networking (SDN) for network management flexibility and lower operating costs. Information technology (IT) services for enterprises tend to use both technologies. Yet, the effects of cloud computing and software defined networking on business network security are unclear. This study addresses this crucial issue. In a business network that uses both technologies, we start by looking at security, namely distributed denial-of-service (DDoS) attack defensive methods. SDN technology may help organizations protect against DDoS assaults provided the defensive architecture is structured appropriately. To mitigate DDoS attacks, we offer a highly configurable network monitoring and flexible control framework. We present a dataset shift-resistant graphic model-based attack detection system for the new architecture. The simulation findings demonstrate that our architecture can efficiently meet the security concerns of the new network paradigm and that our attack detection system can report numerous threats using real-world network data.

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Section: Related Workmentioning

confidence: 99%