(iv)Using labels in order to differentiate between traffic information that comes from different networks. France, Copyright @ 2010 International Journal Of Current Research. However, there is an obvious contradiction between Big Data security and privacy and the widespread use of Big Data. Performs header and label information checking: Assumptions: secured data comes with extra header size such as ESP header, (i) Data Source and Destination (DSD) information are used and. In contrast, the authors in  focused on the big data multimedia content problem within a cloud system. Therefore, header information can play a significant role in data classification. Figure 4 illustrates the mapping between the network core, which is assumed here to be a Generalized Multiprotocol Label Switching (GMPLS) or MPLS network. Big Data. At this stage, the traffic structure (i.e., structured or unstructured) and type (i.e., security services applied or required, or no security) should be identified. Special Collection on Big Data and Machine Learning for Sensor Network Security To have your paper considered for this Special Collection, submit by October 31, 2020. Algorithms 1 and 2 can be summarized as follows:(i)The two-tier approach is used to filter incoming data in two stages before any further analysis. At the same time, privacy and security concerns may limit data sharing and data use. The journal will accept papers on … The Gateways are responsible for completing and handling the mapping in between the node(s), which are responsible for processing the big data traffic arriving from the core network. 32. Total processing time in seconds for variable network data rate. This is a common security model in big data installations as big data security tools are lacking and network security people aren’t necessarily familiar with the specific requirements of security big data systems. The effect of labeling implementation on the total nodal processing time for big data analysis has been shown in Figure 6. Just Accepted. Big Data could not be described just in terms of its size. Even worse, as recent events showed, private data may be hacked, and misused. “Big data” emerges from this incredible escalation in the number of IP-equipped endpoints. Future work on the proposed approach will handle the visualization of big data information in order to provide abstract analysis of classification. In the proposed GMPLS/MPLS implementation, this overhead does not apply because traffic separation is achieved automatically by the use of MPLS VPN capability, and therefore our solution performs better in this regard. The first part challenges the credibility of security professionals’ discourses in light of the knowledge that they apparently mobilize, while the second part suggests a series of conceptual interchanges around data, relationships, and procedures to address some of the restrictions of current activities with the big data security assemblage. Total processing time in seconds for variable big data size. The type of data used in the simulation is VoIP, documents, and images. An internal node consists of a Name_Node and Data_Node(s), while the incoming labeled traffic is processed and analyzed for security services based on three factors: Volume, Velocity, and Variety. Next, the node internal architecture and the proposed algorithm to process and analyze the big data traffic are presented. The simulations were conducted using the NS2 simulation tool (NS-2.35). Furthermore, the proposed classification method should take the following factors into consideration . Daily tremendous amount of digital data is being produced. Figure 5 shows the effect of labeling on the network overhead. So, All of authors and contributors must check their papers before submission to making assurance of following our anti-plagiarism policies. In Section 3, the proposed approach for big data security using classification and analysis is introduced. Security Issues. The authors declare that they have no conflicts of interest. Forbes, Inc. 2012. As mentioned in previous section, MPLS is our preferred choice as it has now been adopted by most Internet Service Providers (ISPs). Data Security. Keywords: Big data, health, information, privacy, security . GMPLS/MPLS are not intended to support encryption and authentication techniques as this can downgrade the performance of the network. It can be noticed that the total processing time has been reduced significantly. Hill K. How target figured out a teen girl was pregnant before her father did. This factor is used as a prescanning stage in this algorithm, but it is not a decisive factor. Online Now. (v)Visualization: this process involves abstracting big data and hence it helps in communicating data clearly and efficiently. Why your kids will want to be data scientists. European Journal of Public Health, Volume 29, Issue Supplement_3, ... Big Data in health encompasses high volume, high diversity biological, clinical, ... finds a fertile ground from the public. An emerging research topic in data mining, known as privacy-preserving data mining (PPDM), has been extensively studied in recent years. Consequently, the gateway is responsible for distributing the labeled traffic to the appropriate node (NK) for further analysis and processing at Tier 2. The obtained results show the performance improvements of the classification while evaluating parameters such as detection, processing time, and overhead. The proposed architecture supports security features that are inherited from the GMPLS/MPLS architecture, which are presented below: Traffic Separation.
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