Cloud computing enables on demand access of computing, storage and network resources for the purpose of running IT workloads. It requires minimal management of various computing resources as well as minimal interactions with cloud computing vendors. Organizations are exploring options for cheaper, reliable and secured cloud computing environments to shift their on-premises workloads to cloud. For organizations and individuals’ data are important and are valuable resource. While organizations are outsourcing their data and applications to Cloud providers, security issues such as confidentiality and privacy are challenging concerns. Over the years researchers have come up with various recommended models of the security ecosystem. In this paper, we have proposed a solution that would use multi-layer multi-class neural network classification technique, to classify and categorize IoT devices’ data, such as Sensitive, Semi-sensitive, Non-sensitive, etc., for optimum utilization of resources.

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Application of Deep Learning to Address Cloud Security Concerns for Smart IoT Solutions

  • Sukant Kumar Sahoo,
  • Smitaprava Mishra,
  • Biswaranjan Jena,
  • Tirthankar

摘要

Cloud computing enables on demand access of computing, storage and network resources for the purpose of running IT workloads. It requires minimal management of various computing resources as well as minimal interactions with cloud computing vendors. Organizations are exploring options for cheaper, reliable and secured cloud computing environments to shift their on-premises workloads to cloud. For organizations and individuals’ data are important and are valuable resource. While organizations are outsourcing their data and applications to Cloud providers, security issues such as confidentiality and privacy are challenging concerns. Over the years researchers have come up with various recommended models of the security ecosystem. In this paper, we have proposed a solution that would use multi-layer multi-class neural network classification technique, to classify and categorize IoT devices’ data, such as Sensitive, Semi-sensitive, Non-sensitive, etc., for optimum utilization of resources.