Big Data Analysis-Based Secure Cluster Management for Optimized Control Plane in Software-Defined Networks

Wu, Jun; Dong, Mianxiong; Ota, Kaoru; Li, Jianhua; Guan, Zhitao

doi:10.1109/tnsm.2018.2799000

Cited by 179 publications

(79 citation statements)

References 34 publications

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“…In [36], Map function is used to perform the procedure of assigning each sample to the closest centroid while reduce function is used to update the new centroids. Several papers (e.g., [37][38][39][40][41][42]) have studied related issues.…”

Section: Related Workmentioning

confidence: 99%

Achieving data utility-privacy tradeoff in Internet of Medical Things: A machine learning approach

Guan

et al. 2019

Future Generation Computer Systems

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The emergence and rapid development of the Internet of Medical Things (IoMT), an application of the Internet of Things into the medical and healthcare systems, have brought many changes and challenges to modern medical and healthcare systems. Particularly, machine learning technology can be used to process the data involved in IoMT for medical analysis and disease diagnosis. However, in this process, the disclosure of personal privacy information must receive considerable attentions especially for sensitive medical data. Cluster analysis is an important technique for medical analysis and disease diagnosis. To enable privacy-preserving cluster analysis in IoMT, this paper proposed an Efficient Differentially Private Data Clustering scheme (EDPDCS) based on MapReduce framework. In EDPDCS, we optimize the allocation of privacy budgets and the selection of initial centroids to improve the accuracy of differentially private Kmeans clustering algorithm. Specifically, the number of iterations of the K-means algorithm is set to a fixed value according to the total privacy budget and the minimal privacy budget of each iteration. In addition, an improved initial centroids selection method is proposed to increase the accuracy and efficiency of the clustering algorithm. Finally, we prove that the proposed EDPDCS can improve the accuracy of the differentially private k-means algorithm by comparing the Normalized Intra-Cluster Variance (NICV) produced by our algorithm on two datasets with two other algorithms.

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Section: Related Workmentioning

confidence: 99%

Achieving data utility-privacy tradeoff in Internet of Medical Things: A machine learning approach

Guan

et al. 2019

Future Generation Computer Systems

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“…Concurrently, the security of cluster technology becomes an open issue caused by the dynamic features of SDNs. To meet the above challenges, a big data analysis based secure cluster management framework for optimal control plane has been proposed recently [28]. An ant colony optimized method that uses big data analysis approach and the implementation system that optimizes the control plane are proposed.…”

Section: Fog Networking (Sdn and Nfv)mentioning

confidence: 99%

“…CISCO related studies and information [26] explained an overview about a developing fog computing architecture with a three-layered approach of distributed AI services for better QoS at edge location of the network as well as distributed intelligence and predictable fog computing. Two systems can be discussed for the availability of resources near the edge of the network [72] that is explained for live sensor streaming analysis and cloudlets [10,28] that are interactive for applications which are previously proposed and also will be discussed in next sections. A sequence in SDN [73,74] is the basic structured property of a secured communications network in fog computing, having confidentiality, integrity, availability of information, authentication, and nonrepudiation.…”

Section: Fog Frame Work and Architecturesmentioning

confidence: 99%

“…To overcome the requirements of large scale network setups, the control plane is typically implemented as a distributed controller. To meet the above challenges, a big data analysis based secure cluster management framework for optimal control plane has been proposed recently [28]. Respectively, cluster management technology operates all types of proceedings and must continue a steady global network status, which significantly leads to big data in SDNs.…”

Section: The Future Of Handling Big Datamentioning

confidence: 99%

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Fog Computing: An Overview of Big IoT Data Analytics

Anawar

Wang

Zia

et al. 2018

Wireless Communications and Mobile Computing

152

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A huge amount of data, generated by Internet of Things (IoT), is growing up exponentially based on nonstop operational states. Those IoT devices are generating an avalanche of information that is disruptive for predictable data processing and analytics functionality, which is perfectly handled by the cloud before explosion growth of IoT. Fog computing structure confronts those disruptions, with powerful complement functionality of cloud framework, based on deployment of micro clouds (fog nodes) at proximity edge of data sources. Particularly big IoT data analytics by fog computing structure is on emerging phase and requires extensive research to produce more proficient knowledge and smart decisions. This survey summarizes the fog challenges and opportunities in the context of big IoT data analytics on fog networking. In addition, it emphasizes that the key characteristics in some proposed research works make the fog computing a suitable platform for new proliferating IoT devices, services, and applications. Most significant fog applications (e.g., health care monitoring, smart cities, connected vehicles, and smart grid) will be discussed here to create a well-organized green computing paradigm to support the next generation of IoT applications.

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“…Due to the open nature of wireless links, the security performance is another important consideration in wireless communication networks [7], [8]. To address this, physical layer security (PLS) techniques have been proposed to protect legitimate transmissions in MEC systems from being overheard by an eavesdropper.…”

Section: Introductionmentioning

confidence: 99%

Offloading Optimization for Low-Latency Secure Mobile Edge Computing Systems

Zhou

Yeoh

Pan

et al. 2020

IEEE Wireless Commun. Lett.

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This paper proposes a low-latency secure mobile edge computing (MEC) system where multiple users offload computing tasks to a base station in the presence of an eavesdropper. We jointly optimize the users' transmit power, computing capacity allocation, and user association to minimize the computing and transmission latencies over all users subject to security and computing resource constraints. Numerical results show that our proposed algorithm outperforms baseline strategies. Furthermore, we highlight a novel trade-off between the latency and security of MEC systems.

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Big Data Analysis-Based Secure Cluster Management for Optimized Control Plane in Software-Defined Networks

Cited by 179 publications

References 34 publications

Achieving data utility-privacy tradeoff in Internet of Medical Things: A machine learning approach

Achieving data utility-privacy tradeoff in Internet of Medical Things: A machine learning approach

Fog Computing: An Overview of Big IoT Data Analytics

Offloading Optimization for Low-Latency Secure Mobile Edge Computing Systems

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