An Effective Classification Approach for Big Data Security Based on GMPLS/MPLS Networks. The core idea in the proposed algorithms depends on the use of labels to filter and categorize the processed big data traffic. The growing popularity and development of data mining technologies bring serious threat to the security of individual,'s sensitive information. 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. Specifically, they summarized and analyzed the main results obtained when external integrity verification techniques are used for big data security within a cloud environment. In this section, we present and focus on the main big data security related research work that has been proposed so far. Nevertheless, securing these data has been a daunting requirement for decades. It is the procedure of verifying information are accessible just to the individuals who need to utilize it for a legitimate purpose. Actually, the traffic is forwarded/switched internally using the labels only (i.e., not using IP header information). As big data becomes the new oil for the digital economy, realizing the benefits that big data can bring requires considering many different security and privacy issues. Big data security analysis and processing based on volume. As can be noticed from the obtained results, the labeling methodology has lowered significantly the total processing time of big data traffic. 33. In [3], the authors investigated the security issues encountered by big data when used in cloud networks. A big–data security mechanism based on fully homomorphic encryption using cubic spline curve public key cryptography. 1. By 2020, 50 billion devices are expected to be connected to the Internet. Algorithms 1 and 2 are the main pillars used to perform the mapping between the network core and the big data processing nodes. The technique analyzes big data by extracting valuable content that needs protection. 52 ibid. Big Data Encryption and Authentication. Vulnerability to fake data generation 2. Volume: the size of data generated and storage space required. (ii) Real time data are usually assumed less than 150 bytes per packet. Based on the DSD probability value(s), decision is made on the security service? Authors in [2] propose an attribute selection technique that protects important big data. Big data security and privacy are potential challenges in cloud computing environment as the growing usage of big data leads to new data threats, particularly when dealing with sensitive and critical data such as trade secrets, personal and financial information. Possibility of sensitive information mining 5. Variety: the category of data and its characteristics. At the same time, privacy and security concerns may limit data sharing and data use. 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 is the collection of large and complex data sets that are difficult to process using on-hand database management tools or traditional data processing applications. In Scopus it is regarded as No. Security Issues. Thus, security analysis will be more likely to be applied on structured data or otherwise based on selection. Big Data could not be described just in terms of its size. https://data.mendeley.com/datasets/7wkxzmdpft/2, Function for getting Big Data traffic by Name_node, (i) Real time data is assigned different label than file transfer data and, thus the label value should indicate the Volume size. However, the traditional methods do not comply with big data security requirements where tremendous data sets are used. The initiative aims at exploring proper and efficient ways to use big data in solving problems and threats facing the nation, government, and enterprise. This Cloud Security Alliance (CSA) document lists out, in detail, the best practices that should be followed by big data service providers to fortify 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. Hiding Network Interior Design and Structure. Therefore, a big data security event monitoring system model has been proposed which consists of four modules: data collection, integration, analysis, and interpretation [ 41 ]. The MPLS header and labeling distribution protocols make the classification of big data at processing node(s) more efficient with regard to performance, design, and implementation. The network core labels are used to help tier node(s) to decide on the type and category of processed data. In Section 4, the validation results for the proposed method are shown. This press … IEEE websites place cookies on your device to give you the best user experience. Therefore, security implementation on big data information is applied at network edges (e.g., network gateways and the big data processing nodes). Editor-in-Chief: Zoran Obradovic, PhD. Next, the node internal architecture and the proposed algorithm to process and analyze the big data traffic are presented. Therefore, with security in mind, big data handling for encrypted content is not a simple task and thus requires different treatment. 2018, Article ID 8028960, 10 pages, 2018. https://doi.org/10.1155/2018/8028960. In addition, authentication deals with user authentication and a Certification Authority (CA). Therefore, we assume that the network infrastructure core supports Multiprotocol Label Switching (MPLS) or the Generalized Multiprotocol Label Switching (GMPLS) [25], and thus labels can be easily implemented and mapped. In case encryption is needed, it will be supported at nodes using appropriate encryption techniques. Big Data in Healthcare – Pranav Patil, Rohit Raul, Radhika Shroff, Mahesh Maurya – 2014 34. Copyright © 2018 Sahel Alouneh et al. Finance, Energy, Telecom). Velocity: the speed of data generation and processing. The demand for solutions to handle big data issues has started recently by many governments’ initiatives, especially by the US administration in 2012 when it announced the big data research and development initiative [1]. The current security challenges in big data environment is related to privacy and volume of data. The authors declare that they have no conflicts of interest. Even worse, as recent events showed, private data may be hacked, and misused. So, All of authors and contributors must check their papers before submission to making assurance of following our anti-plagiarism policies. The proposed algorithm relies on different factors for the analysis and is summarized as follows:(i)Data Source and Destination (DSD): data source as well as destination may initially help to guess the structure type of the incoming data. Analyzing and processing big data at Networks Gateways that help in load distribution of big data traffic and improve the performance of big data analysis and processing procedures. It can be clearly seen that the proposed method lowers significantly the processing time for data classification and detection. As technology expands, the journal devotes coverage to computer and information security, cybercrime, and data analysis in investigation, prediction and threat assessment. Other security factors such as Denial of Service (DoS) protection and Access Control List (ACL) usage will also be considered in the proposed algorithm. Nowadays, big data has become unique and preferred research areas in the field of computer science. Data Source and Destination (DSD): data source as well as destination may initially help to guess the structure type of the incoming data. Chief Scientific Officer and Head of a Research Group An emerging research topic in data mining, known as privacy-preserving data mining (PPDM), has been extensively studied in recent years. The “ Big Data Network Security Software market” report covers the overview of the market and presents the information on business development, market size, and share scenario. The core network consists of provider routers called here P routers and numbered A, B, etc. However, to generate a basic understanding, Big Data are datasets which can’t be processed in conventional database ways to their size. Because of the velocity, variety, and volume of big data, security and privacy issues are magnified, which results in the traditional protection mechanisms for structured small scale data are inadequate for big data. Moreover, Tier 2 is responsible for evaluating the incoming traffic according to the Velocity, Volume, and Variety factors. Troubles of cryptographic protection 4. Before processing the big data, there should be an efficient mechanism to classify it on whether it is structured or not and then evaluate the security status of each category. For example, the IP networking traffic header contains a Type of Service (ToS) field, which gives a hint on the type of data (real-time data, video-audio data, file data, etc.). In this special issue, we discuss relevant concepts and approaches for Big Data security and privacy, and identify research challenges to be addressed to achieve comprehensive solutions. In this paper, a new security handling approach was proposed for big data. 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That reliability and availability can greatly be improved using GMPLS/MPLS core networks [ ]...
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