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Thus, security analysis will be more likely to be applied on structured data or otherwise based on selection. 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. (ii)Treatment and conversion: this process is used for the management and integration of data collected from different sources to achieve useful presentation, maintenance, and reuse of data. Big data is becoming a well-known buzzword and in active use in many areas. 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. Another work that targets real-time content is presented in [10], in which a semantic-based video organizing platform is proposed to search videos in big data volumes. . 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.). Therefore, with security in mind, big data handling for encrypted content is not a simple task and thus requires different treatment. 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. This paper discusses the security issues related to big data due to inadequate research and security solutions also the needs and challenges faced by the big data security, the security framework and proposed approaches. Total processing time in seconds for variable big data size. 2018, Article ID 8028960, 10 pages, 2018. https://doi.org/10.1155/2018/8028960. Classifying big data according to its structure that help in reducing the time of applying data security processes. In general, big data are collected in real time, typically running into the millions of transactions per second for large organizations. Google Scholar. Every generation trusts online retailers and social networking websites or applications the least with the security of their data, with only 4% of millennials reporting they have a lot of trust in the latter. The report also emphasizes on the growth prospects of the global Big Data Network Security Software market for the period 2020-2025. The simulations were conducted using the NS2 simulation tool (NS-2.35). The classification requires a network infrastructure that supports GMPLS/MPLS capabilities. The proposed security framework focuses on securing autonomous data content and is developed in the G-Hadoop distributed computing environment. This has led human being in big dilemma. In case encryption is needed, it will be supported at nodes using appropriate encryption techniques. Simulation results demonstrated that using classification feedback from a MPLS/GMPLS core network proved to be key in reducing the data evaluation and processing time. Furthermore, the Tier 1 classification process can be enhanced by using traffic labeling. Even worse, as recent events showed, private data may be hacked, and misused. 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. The type of traffic used in the simulation is files logs. Although there remains much to do in the field of big data security, research in this area is moving forward, both from a scientific and commercial point of view. The need for effective approaches to handle big data that is characterized by its large volume, different types, and high velocity is vital and hence has recently attracted the attention of several research groups. So far, the node architecture that is used for processing and classifying big data information is presented. The GMPLS/MPLS simplifies the classification by providing labeling assignments for the processed big data traffic. (2018). The new research report titles Global Big Data Network Security Software market Growth 2020-2025 that studies all the vital factors related to the Global Big Data Network Security Software market that are crucial for the growth and development of businesses in the given market parameters. 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. Sectorial healthcare strategy 2012-2016- Moroccan healthcare ministry. Indeed, the purpose of making the distance between nodes variable is to help measuring the distance effect on processing time. At the same time, privacy and security concerns may limit data sharing and data use. The technique analyzes big data by extracting valuable content that needs protection. Furthermore, in [9], they considered the security of real-time big data in cloud systems. Figure 5 shows the effect of labeling on the network overhead. Forget big brother - big sister's arrived. The GMPLS/MPLS network is terminated by complex provider Edge routers called here in this work Gateways. (v)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. In [3], the authors investigated the security issues encountered by big data when used in cloud networks. Specifically, they summarized and analyzed the main results obtained when external integrity verification techniques are used for big data security within a cloud environment. Keywords: Big data, health, information, privacy, security . We will be providing unlimited waivers of publication charges for accepted research articles as well as case reports and case series related to COVID-19. 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. The proposed technique uses a semantic relational network model to mine and organize video resources based on their associations, while the authors in [11] proposed a Dynamic Key Length based Security Framework (DLSeF) founded on a common key resulting from synchronized prime numbers. Large volumes of data are processed using big data in order to obtain information and be able Actually, the traffic is forwarded/switched internally using the labels only (i.e., not using IP header information). Therefore, attacks such as IP spoofing and Denial of Service (DoS) can efficiently be prevented. The network core labels are used to help tier node(s) to decide on the type and category of processed data. Thus, the treatment of these different sources of information should not be the same. 18 Concerns evolve around the commercialization of data, data security and the use of data against the interests of the people providing the data. Potential challenges for big data handling consist of the following elements [3]:(i)Analysis: this process focuses on capturing, inspecting, and modeling of data in order to extract useful information. Struggles of granular access control 6. Executive Office of the President, “Big Data Across the Federal Government,” WH official website, March 2012. They proposed a novel approach using Semantic-Based Access Control (SBAC) techniques for acquiring secure financial services. The analysis focuses on the use of Big Data by private organisations in given sectors (e.g. The authors in [4] developed a new security model for accessing distributed big data content within cloud networks. Now think of all the big data security issues that could generate! In addition, the protocol field indicates the upper layers, e.g., UDP, TCP, ESP security, AH security, etc. Each node is also responsible for analyzing and processing its assigned big data traffic according to these factors. Big data network security systems should be find abnormalities quickly and identify correct alerts from heterogeneous data. Sahel Alouneh, Feras Al-Hawari, Ismail Hababeh, Gheorghita Ghinea, "An Effective Classification Approach for Big Data Security Based on GMPLS/MPLS Networks", Security and Communication Networks, vol. Sensitivities around big data security and privacy are a hurdle that organizations need to overcome. It is the procedure of verifying information are accessible just to the individuals who need to utilize it for a legitimate purpose. Having reliable data transfer, availability, and fast recovery from failures are considered important protection requirements and thus improve the security. Handlers of big data should … Big data security analysis and processing based on volume. Impact Factor: * 3.644 *2019 Journal Citation Reports (Clarivate, 2020) The leading peer-reviewed journal covering the challenges and opportunities in collecting, analyzing, and disseminating vast amounts of data. This special issue aims to identify the emerged security and privacy challenges in diverse domains (e.g., finance, medical, and public organizations) for the big data. The term “big data” refers to the massive amounts of digital information companies and governments collect about human beings and our environment. Please feel free to contact me if you have any questions or comments. We are committed to sharing findings related to COVID-19 as quickly as possible. Moreover, Tier 2 is responsible for evaluating the incoming traffic according to the Velocity, Volume, and Variety factors. An Effective Classification Approach for Big Data Security Based on GMPLS/MPLS Networks. In addition, the simulated network data size ranges from 100 M bytes to 2000 M bytes. Besides that, other research studies [14–24] have also considered big data security aspects and solutions. 32. 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. 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. Troubles of cryptographic protection 4. Management topics covered include evaluation of security measures, anti-crime design and planning, staffing, and regulation of the security … (iii)Tier 2 is responsible to process and analyze big data traffic based on Volume, Velocity, and Variety factors. Google Scholar. IEEE websites place cookies on your device to give you the best user experience. Abouelmehdi, Karim and Beni-Hessane, Abderrahim and Khaloufi, Hayat, 2018, Big healthcare data: preserving security and privacy, Journal of Big Data, volume 5,number 1, pages 1, 09-Jan 2018. Based on the DSD probability value(s), decision is made on the security service? 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. In Section 2, the related work that has been carried out on big data in general with a focus on security is presented. Furthermore, the proposed classification method should take the following factors into consideration [5]. Therefore, a big data security event monitoring system model has been proposed which consists of four modules: data collection, integration, analysis, and interpretation [ 41 ]. Big Data Encryption and Authentication. The initiative aims at exploring proper and efficient ways to use big data in solving problems and threats facing the nation, government, and enterprise. 33. Most Read. This in return implies that the entire big data pipeline needs to be revisited with security and privacy in mind. Big data security analysis and processing based on velocity and variety. IJCR is following an instant policy on rejection those received papers with plagiarism rate of more than 20%. In Figure 7, total processing time simulation has been measured again but this time for a fixed data size (i.e., 500 M bytes) and a variable data rate that ranges from 10 Mbps to 100 Mbps. Big data can contain different kinds of information such as text, video, financial data, and logs, as well as secure or insecure information. The use of the GMPLS/MPLS core network provides traffic separation by using Virtual Private Network (VPN) labeling and the stacking bit (S) field that is supported by the GMPLS/MPLS headers. Accordingly, we propose to process big data in two different tiers. In the following subsections, the details of the proposed approach to handle big data security are discussed. The Journal of Big Data publishes high-quality, scholarly research papers, methodologies and case studies covering a broad range of topics, from big data analytics to data-intensive computing and all applications of big data research. We have chosen different network topologies with variable distances between nodes ranging from 100m to 4000Km in the context of wired networks (LAN, WAN, MAN). 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. This Cloud Security Alliance (CSA) document lists out, in detail, the best practices that should be followed by big data service providers to fortify Why your kids will want to be data scientists. The role of the first tier (Tier 1) is concerned with the classification of the big data to be processed. It can be clearly seen that the proposed method lowers significantly the processing time for data classification and detection. 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. Accepted research articles as well as news, analysis and advice regarding current research is to! While considering and respecting customer privacy was interestingly studied in recent years IP-equipped.! Be prevented labels in order to provide abstract analysis of big data security aspects solutions... 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