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Among the topics covered are new security management techniques, as well as news, analysis and advice regarding current research. “Big data” emerges from this incredible escalation in the number of IP-equipped endpoints. As recent trends show, capturing, storing, and mining "big data" may create significant value in industries ranging from healthcare, business, and government services to the entire science spectrum. Misuse of information from big data often results in violations of privacy, security, and cybercrime. It mainly extracts information based on the relevance factor. Now, our goal in this section is to test by simulations and analyze the impact of using the labeling approach on improving the classification of big data and thus improving the security. But it’s also crucial to look for solutions where real security data can be analyzed to drive improvements. 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]. Big Data could not be described just in terms of its size. An emerging research topic in data mining, known as privacy-preserving data mining (PPDM), has been extensively studied in recent years. Finance, Energy, Telecom). At the same time, privacy and security concerns may limit data sharing and data use. 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. Other security factors such as Denial of Service (DoS) protection and Access Control List (ACL) usage will also be considered in the proposed algorithm. 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. Just Accepted. Finance, Energy, Telecom). To understand how Big Data is constructed in the context of law enforcement and security intelligence, it is useful, following Valverde (2014), to conceive of Big Data as a technique that is being introduced into one or more security projects in the governance of society. 31. 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. Therefore, this research aims at exploring and investigating big data security and privacy threats and proposes twofold approach for big data classification and security to minimize data threats and implements security controls during data exchange. In addition, the gateways outgoing labeled traffic is the main factor used for data classification that is used by Tier 1 and Tier 2 layers. Thus, security analysis will be more likely to be applied on structured data or otherwise based on selection. (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. Furthermore, in [9], they considered the security of real-time big data in cloud systems. International Journal of Production Re search 47(7), 1733 –1751 (2009) 22. Please feel free to contact me if you have any questions or comments. The type of data used in the simulation is VoIP, documents, and images. 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. Complicating matters, the healthcare industry continues to be one of the most susceptible to publicly disclosed data breaches. The proposed classification algorithm is concerned with processing secure big data. The global Big Data Security market is forecast to reach USD 49.00 Billion by 2026, according to a new report by Reports and Data. Having reliable data transfer, availability, and fast recovery from failures are considered important protection requirements and thus improve the security. Data provenance difficultie… Handlers of big data should … In addition, the. Big Data. We also have conducted a simulation to measure the big data classification using the proposed labeling method and compare it with the regular method when no labeling is used as shown in Figure 8. IJCR is following an instant policy on rejection those received papers with plagiarism rate of more than 20%. Google Scholar. All rights reserved, IJCR is following an instant policy on rejection those received papers with plagiarism rate of. This is especially the case when traditional data processing techniques and capabilities proved to be insufficient in that regard. The current security challenges in big data environment is related to privacy and volume of data. 32. 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. In contrast, the second tier analyzes and processes the data based on volume, variety, and velocity factors. The second tier (Tier 2) decides on the proper treatment of big data based on the results obtained from the first tier, as well as based on the analysis of velocity, volume, and variety factors. Authentication: some big data may require authentication, i.e., protection of data against modification. Therefore, a big data security event monitoring system model has been proposed which consists of four modules: data collection, integration, analysis, and interpretation [ 41 ]. Therefore, with security in mind, big data handling for encrypted content is not a simple task and thus requires different treatment. Loshima Lohi, Greeshma K V, 2015, Big Data and Security, INTERNATIONAL JOURNAL OF ENGINEERING RESEARCH & TECHNOLOGY (IJERT) NSDMCC – 2015 (Volume 4 – Issue 06), Open Access ; Article Download / Views: 27. The labels can carry information about the type of traffic (i.e., real time, audio, video, etc.). While opportunities exist with Big Data, the data can overwhelm traditional CiteScore: 7.2 ℹ CiteScore: 2019: 7.2 CiteScore measures the average citations received per peer-reviewed document published in this title. (2018). 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. The articles will provide cro. Data security is the practice of keeping data protected from corruption and unauthorized access. Furthermore, more security analysis parameters are to be investigated such as integrity and real time analysis of big data. Sensitivities around big data security and privacy are a hurdle that organizations need to overcome. Now think of all the big data security issues that could generate! Troubles of cryptographic protection 4. I. Narasimha, A. Sailaja, and S. Ravuri, “Security Issues Associated with Big Data in Cloud Computing,”, S.-H. Kim, N.-U. Online Now. However, Virtual Private Networks (VPNs) capabilities can be supported because of the use of GMPLS/MPLS infrastructure. In addition, authentication deals with user authentication and a Certification Authority (CA). Nevertheless, securing these data has been a daunting requirement for decades. It can be noticed that the total processing time has been reduced significantly. The study aims at identifying the key security challenges that the companies are facing when implementing Big Data solutions, from infrastructures to analytics applications, and how those are mitigated. Big data can contain different kinds of information such as text, video, financial data, and logs, as well as secure or insecure information. Jain, Priyank and Gyanchandani, Manasi and Khare, Nilay, 2016, Big … As mentioned in previous section, MPLS is our preferred choice as it has now been adopted by most Internet Service Providers (ISPs). A flow chart of the general architecture for our approach. However, more institutions (e.g. The GMPLS/MPLS network is terminated by complex provider Edge routers called here in this work Gateways. Hill K. How target figured out a teen girl … 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. Hence, it helps to accelerate data classification without the need to perform a detailed analysis of incoming data. The ratio effect of labeling use on network overhead. The journal aims to promote and communicate advances in big data research by providing a fast and high quality forum for researchers, practitioners and policy makers from the very many different communities working on, and with, this topic. Data Security. Chief Scientific Officer and Head of a Research Group Forget big brother - big sister's arrived. The key is dynamically updated in short intervals to prevent man in the middle attacks. Nowadays, big data has become unique and preferred research areas in the field of computer science. Kim, and T.-M. Chung, “Attribute relationship evaluation methodology for big data security,” in, J. Zhao, L. Wang, J. Tao et al., “A security framework in G-Hadoop for big data computing across distributed cloud data centres,”, G. Lafuente, “The big data security challenge,”, K. Gai, M. Qiu, and H. Zhao, “Security-Aware Efficient Mass Distributed Storage Approach for Cloud Systems in Big Data,” in, C. Liu, C. Yang, X. Zhang, and J. Chen, “External integrity verification for outsourced big data in cloud and IoT: a big picture,”, A. Claudia and T. Blanke, “The (Big) Data-security assemblage: Knowledge and critique,”, V. Chang and M. Ramachandran, “Towards Achieving Data Security with the Cloud Computing Adoption Framework,”, Z. Xu, Y. Liu, L. Mei, C. Hu, and L. Chen, “Semantic based representing and organizing surveillance big data using video structural description technology,”, D. Puthal, S. Nepal, R. Ranjan, and J. Chen, “A Dynamic Key Length Based Approach for Real-Time Security Verification of Big Sensing Data Stream,” in, Y. Li, K. Gai, Z. Ming, H. Zhao, and M. Qiu, “Intercrossed access controls for secure financial services on multimedia big data in cloud systems,”, K. Gai, M. Qiu, H. Zhao, and J. Xiong, “Privacy-Aware Adaptive Data Encryption Strategy of Big Data in Cloud Computing,” in, V. Chang, Y.-H. Kuo, and M. Ramachandran, “Cloud computing adoption framework: A security framework for business clouds,”, H. Liang and K. Gai, “Internet-Based Anti-Counterfeiting Pattern with Using Big Data in China,”, Z. Yan, W. Ding, X. Yu, H. Zhu, and R. H. Deng, “Deduplication on Encrypted Big Data in Cloud,” in, A. Gholami and E. Laure, “Big Data Security and Privacy Issues in the Coud,”, Y. Li, K. Gai, L. Qiu, M. Qiu, and H. Zhao, “Intelligent cryptography approach for secure distributed big data storage in cloud computing,”, A. Narayanan, J. Huey, and E. W. Felten, “A Precautionary Approach to Big Data Privacy,” in, S. Kang, B. Veeravalli, and K. M. M. Aung, “A Security-Aware Data Placement Mechanism for Big Data Cloud Storage Systems,” in, J. Domingo-Ferrer and J. Soria-Comas, “Anonymization in the Time of Big Data,” in, Y.-S. Jeong and S.-S. Shin, “An efficient authentication scheme to protect user privacy in seamless big data services,”, R. F. Babiceanu and R. Seker, “Big Data and virtualization for manufacturing cyber-physical systems: A survey of the current status and future outlook,”, Z. Xu, Z. Wu, Z. Li et al., “High Fidelity Data Reduction for Big Data Security Dependency Analyses,” in, S. Alouneh, S. Abed, M. Kharbutli, and B. J. Mohd, “MPLS technology in wireless networks,”, S. Alouneh, A. Agarwal, and A. En-Nouaary, “A novel path protection scheme for MPLS networks using multi-path routing,”. However, the algorithm uses a controlling feedback for updating. The type of traffic analyzed in this simulation is files logs, and the simulated data size ranges from a traffic size of 100 Mbytes to 2000 Mbytes. Security Journal brings new perspective to the theory and practice of security management, with evaluations of the latest innovations in security technology, and insight on new practices and initiatives. At the same time, privacy and security concerns may limit data sharing and data use. Therefore, security implementation on big data information is applied at network edges (e.g., network gateways and the big data processing nodes). Total processing time in seconds for variable big data size. 2018, Article ID 8028960, 10 pages, 2018. https://doi.org/10.1155/2018/8028960. The initiative aims at exploring proper and efficient ways to use big data in solving problems and threats facing the nation, government, and enterprise. The term “big data” refers to the massive amounts of digital information companies and governments collect about human beings and our environment. 33. Possibility of sensitive information mining 5. Moreover, it also can be noticed the data rate variation on the total processing with labeling is very little and almost negligible, while without labeling the variation in processing time is significant and thus affected by the data rate increase. Classifying big data according to its structure that help in reducing the time of applying data security processes. This press … Actually, the traffic is forwarded/switched internally using the labels only (i.e., not using IP header information). Besides that, other research studies [14–24] have also considered big data security aspects and 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. Data security is a hot-button issue right now, and for a good reason. (ii) Real time data are usually assumed less than 150 bytes per packet. Share. The IEEE Transactions on Big Data publishes peer reviewed articles with big data as the main focus. 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. Vulnerability to fake data generation 2. 33. Big Data in Healthcare – Pranav Patil, Rohit Raul, Radhika Shroff, Mahesh Maurya – 2014 34. It is worth noting that label(s) is built from information available at (DH) and (DSD). For example, if two competing companies are using the same ISP, then it is very crucial not to mix and forward the traffic between the competing parties. The simulations were conducted using the NS2 simulation tool (NS-2.35). The invention of online social networks, smart phones, fine tuning of ubiquitous computing and many other technological advancements have led to the generation of multiple petabytes of both structured, unstructured and … In this article, security challenges and concerns of IOT big data associated with smart grid are discussed along with the new security enhancements for identification and authentications of things in IOT big data … The report also emphasizes on the growth prospects of the global Big Data Network Security Software market for the period 2020-2025. Total Downloads: 24; Authors : Loshima Lohi, Greeshma K V; Paper ID : IJERTCONV4IS06016; Volume & … In [3], the authors investigated the security issues encountered by big data when used in cloud networks. Figure 4 illustrates the mapping between the network core, which is assumed here to be a Generalized Multiprotocol Label Switching (GMPLS) or MPLS network. Indeed, the purpose of making the distance between nodes variable is to help measuring the distance effect on processing time. Google Scholar. 12 Big data are usually analyzed in batch mode, but increasingly, tools are becoming available for real-time analysis. So, All of authors and contributors must check their papers before submission to making assurance of following our anti-plagiarism policies. Authors in [2] propose an attribute selection technique that protects important big data. 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.). Tier 1 is responsible to filter incoming data by deciding on whether it is structured or nonstructured. Automated data collection is increasing the exposure of companies to data loss. In case encryption is needed, it will be supported at nodes using appropriate encryption techniques. 1 journal in Big data research with IF 8.51 for 2017 metric. In the proposed approach, big data is processed by two hierarchy tiers. 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. Forbes, Inc. 2012. 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. As recent trends show, capturing, storing, and mining "big data" may create significant value in industries ranging from healthcare, business, and government services to the entire science spectrum. Each Tier 2 node applies Algorithms 1 and 2 when processing big data traffic. Therefore, attacks such as IP spoofing and Denial of Service (DoS) can efficiently be prevented. The increasing trend of using information resources and the advances of data processing tools lead to extend usage of big data. However, in times of a pandemic the use of location data provided by telecom operators and/or technology … Big Data. This problem is exaggerated in the context of the Internet of Things (IoT). All-Schemes.TCL and Labeling-Tier.c files should be incorporated along with other MPLS library files available in NS2 and then run them for the intended parameters to generated simulation data. When considering a big data solution, you can best mitigate the risks through strategies such as employee training and varied encryption techniques. 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. Big Data Encryption and Authentication. The type of traffic used in the simulation is files logs. Moreover, the work in [13] focused on the privacy problem and proposed a data encryption method called Dynamic Data Encryption Strategy (D2ES). The purpose is to make security and privacy communities realize the challenges and tasks that we face in Big Data. Editor-in-Chief: Zoran Obradovic, PhD. Daily tremendous amount of digital data is being produced. One basic feature of GMPLS/MPLS network design and structure is that the incoming or outgoing traffic does not require the knowledge of participating routers inside the core network. The VPN capability that can be supported in this case is the traffic separation, but with no encryption. Variety: the category of data and its characteristics. This kind of data accumulation helps improve customer care service in many ways. The current security challenges in big data environment is related to privacy and volume of data. The network core labels are used to help tier node(s) to decide on the type and category of processed data. Velocity: the speed of data generation and processing. However, to generate a basic understanding, Big Data are datasets which can’t be processed in conventional database ways to their size. In contrast, the authors in [12] focused on the big data multimedia content problem within a cloud system. Then, it checks the type of security service that is applied on the data, i.e., whether encryption is applied or not on the processed data, or if authentication is implemented or required on the processed data. Big data is a new term that refers not only to data of big size, but also to data with unstructured characteristic types (i.e., video, audio, unstructured text, and social media information). The type of traffic used in the simulation is files logs. Specifically, they summarized and analyzed the main results obtained when external integrity verification techniques are used for big data security within a cloud environment. Big Data is the leading peer-reviewed journal covering the challenges and opportunities in collecting, analyzing, and disseminating vast amounts of data. In other words, Labels (L) can be used to differentiate or classify incoming traffic data. The journal will accept papers on … Big Data is a term used to describe the large amount of data in the networked, digitized, sensor-laden, information-driven world. CiteScore values are based on citation counts in a range of four years (e.g. The MPLS header is four bytes long and the labels are created from network packet header information. On the other hand, if nodes do not support MPLS capabilities, then classification with regular network routing protocols will consume more time and extra bandwidth. Hill K. How target figured out a teen girl was pregnant before her father did. In today’s era of IT world, Big Data is a new curve and a current buzz word now. 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. In other words, this tier decides first on whether the incoming big data traffic is structured or unstructured. In this subsection, the algorithm used to classify big data information (Tier 1) (i.e., whether data is structured or unstructured and whether security is applied or not) is presented. 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. Using of data-carrying technique, Multiprotocol Label Switching (MPLS) to achieve high-performance telecommunication networks. It is the procedure of verifying information are accessible just to the individuals who need to utilize it for a legitimate purpose. Moreover, it also can be noticed that processing time increases as the traffic size increases; however, the increase ratio is much lower in the case of labeling compared to that with no labeling. Finally, in Section 5, conclusions and future work are provided. 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. 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. However, there is an obvious contradiction between Big Data security and privacy and the widespread use of Big Data. Executive Office of the President, “Big Data Across the Federal Government,” WH official website, March 2012. Consequently, the gateway is responsible for distributing the labeled traffic to the appropriate node (NK) for further analysis and processing at Tier 2. Big Data has gained much attention from the academia and the IT industry. Therefore, header information can play a significant role in data classification. Struggles of granular access control 6. Although bringing AI into big data processing could comprehensively enhance service quality, the issues of security, privacy and trust remain a challenge due to the high possibility of a data breach during the multimedia compression, transmission and analysis. Nevertheless, traffic separation can be achieved by applying security encryption techniques, but this will clearly affect the performance of the network due to the overhead impact of extra processing and delay. This article examines privacy and security in the big data paradigm through proposing a model for privacy and security in the big data age and a classification of big data-driven privacy and security. In Section 3, the proposed approach for big data security using classification and analysis is introduced. The analysis focuses on the use of Big Data by private organisations in given sectors (e.g. This factor is used as a prescanning stage in this algorithm, but it is not a decisive factor. Hiding Network Interior Design and Structure. These security technologies can only exert their value if applied to big data systems. 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. Thus, security analysis will be more likely to be applied on structured data or otherwise based on selection. Communication parameters include traffic engineering-explicit routing for reliability and recovery, traffic engineering- for traffic separation VPN, IP spoofing. As can be noticed from the obtained results, the labeling methodology has lowered significantly the total processing time of big data traffic. In this section, we present and focus on the main big data security related research work that has been proposed so far. In this paper, a new security handling approach was proposed for big data. The growing popularity and development of data mining technologies bring serious threat to the security of individual,'s sensitive information. Data Header information (DH): it has been assumed that incoming data is encapsulated in headers. And in our digitized world, remote workers bear a greater risk when it comes to being hacked. In [8], they proposed to handle big data security in two parts. The study aims at identifying the key security challenges that the companies are facing when implementing Big Data solutions, from infrastructures to analytics applications, and how those are mitigated. 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. 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 innovations do advance, yet their security highlights are as yet disregarded since it’s trusted that security will be allowed on the application level. The two-tier approach is used to filter incoming data in two stages before any further analysis. The security and privacy protection should be considered in all through the storage, transmission and processing of the big data. This has led human being in big dilemma. Reliability and Availability. 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. The performance factors considered in the simulations are bandwidth overhead, processing time, and data classification detection success. The GMPLS extends the architecture of MPLS by supporting switching for wavelength, space, and time switching in addition to the packet switching. To illustrate more, traffic separation is an essential needed security feature. We are committed to sharing findings related to COVID-19 as quickly as possible. 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. (iii)Tier 2 is responsible to process and analyze big data traffic based on Volume, Velocity, and Variety factors. The effect of labeling implementation on the total nodal processing time for big data analysis has been shown in Figure 6. Transferring big data from one node to another based on short path labels rather than long network addresses to avoid complex lookups in a routing table. The challenge to legitimately use big data while considering and respecting customer privacy was interestingly studied in [5]. The core idea in the proposed algorithms depends on the use of labels to filter and categorize the processed big data traffic. This in return implies that the entire big data pipeline needs to be revisited with security and privacy in mind. The main components of Tier 2 are the nodes (i.e., N1, N2, …, ). Indeed, our work is different from others in considering the network core as a part of the big data classification process. Sign up here as a reviewer to help fast-track new submissions. In addition, the protocol field indicates the upper layers, e.g., UDP, TCP, ESP security, AH security, etc. The analysis focuses on the use of Big Data by private organisations in given sectors (e.g. All four generations -- millennials, Gen Xers, baby boomers and traditionalists -- share a lack of trust in certain institutions. The research on big data has so far focused on the enhancement of data handling and performance. Big data is becoming a well-known buzzword and in active use in many areas. Big data is becoming a well-known buzzword and in active use in many areas. Copyright © 2018 Sahel Alouneh et al. 18 Concerns evolve around the commercialization of data, data security and the use of data against the interests of the people providing the data. This is an open access article distributed under the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited. This study aims to determine how aware of the younger generation of security and privacy of their big data. In Section 4, the validation results for the proposed method are shown. Spanning a broad array of disciplines focusing on novel big data technologies, policies, and innovations, the Journal brings together the community to address current challenges and enforce effective efforts to organize, store, disseminate, protect, manipulate, and, most importantly, find the most effective strategies to make this incredible amount of information work to benefit society, industry, academia, and … Any loss that could happen to this data may negatively affect the organization’s confidence and might damage their reputation. By using our websites, you agree to the placement of these cookies. The primary contributions of this research for the big data security and privacy are summarized as follows:(i)Classifying big data according to its structure that help in reducing the time of applying data security processes. 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. (ii)Tier 1 is responsible to filter incoming data by deciding on whether it is structured or nonstructured. Total processing time in seconds for variable network data rate. The network overhead is here defined as the overhead needed to communicate big data traffic packets through the network core until being processed by edge node(s). Based on the DSD probability value(s), decision is made on the security service? The proposed security framework focuses on securing autonomous data content and is developed in the G-Hadoop distributed computing environment. Most Cited. 52 ibid. In general, big data are collected in real time, typically running into the millions of transactions per second for large organizations. Review articles are excluded from this waiver policy. Many recovery techniques in the literature have shown that reliability and availability can greatly be improved using GMPLS/MPLS core networks [26]. (vi)Security and sharing: this process focuses on data privacy and encryption, as well as real-time analysis of coded data, in addition to practical and secure methods for data sharing. Research work in the field of big data started recently (in the year of 2012) when the White House introduced the big data initiative [1]. 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. Thus, the use of MPLS labels reduces the burden on tier node(s) to do the classification task and therefore this approach improves the performance. Thus, the treatment of these different sources of information should not be the same. Transparency is the key to letting us harness the power of big data while addressing its security and privacy challenges. Next, the node internal architecture and the proposed algorithm to process and analyze the big data traffic are presented. However, the proposed approach also requires feedback from the network in order to classify the processed data. The authors declare that they have no conflicts of interest. In the world of big data surveillance, huge amounts of data are sucked into systems that store, combine and analyze them, to create patterns and reveal trends that can be used for marketing, and, as we know from former National Security Agency (NSA) contractor Edward Snowden’s revelations, for policing and security as well. 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 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. The security industry and research institute are paying more attention to the emerging security challenges in big data environment. 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. So, All of authors and contributors must check their papers before submission to making assurance of following our anti-plagiarism policies. 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.). The first tier classifies the data based on its structure and on whether security is required or not. However, the algorithm uses a controlling feedback for updating. Accordingly, we propose to process big data in two different tiers. On the other hand, handling the security of big data is still evolving and just started to attract the attention of several research groups. Using an underlying network core based on a GMPLS/MPLS architecture makes recovery from node or link failures fast and efficient. This approach as will be shown later on in this paper helps in load distribution for big data traffic, and hence it improves the performance of the analysis and processing steps. 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. 51 Aradau, C and Blanke, T, “ The (Big) Data-security assemblage: Knowledge and critique ” (2015) 2 (2) Security Dialogue. 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. Most Read. Therefore, header information can play a significant role in data classification. (ii) Data source indicates the type of data (e.g., streaming data, (iii) DSD_prob is the probability of the Velocity or Variety data, Function for distributing the labeled traffic for the designated data node(s) with. In [7], they also addressed big data issues in cloud systems and Internet of Things (IoT). 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. The proposed method is based on classifying big data into two tiers (i.e., Tier 1 and Tier 2). 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). This factor is used as a prescanning stage in this algorithm, but it is not a decisive factor. 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. The GMPLS/MPLS simplifies the classification by providing labeling assignments for the processed big data traffic. Data Source and Destination (DSD): data source as well as destination may initially help to guess the structure type of the incoming data. Furthermore, honestly, this isn’t a lot of a smart move. It is also worth noting that analyzing big data information can help in various fields such as healthcare, education, finance, and national security. (ii)Data Header information (DH): it has been assumed that incoming data is encapsulated in headers. A flow chart for the general architecture of the proposed method is shown in Figure 1. Abstract: While Big Data gradually become a hot topic of research and business and has been everywhere used in many industries, Big Data security and privacy has been increasingly concerned. So instead of giving generic advice about “security,” I want to show you some ways you can secure yourself and … Moreover, Tier 2 is responsible for evaluating the incoming traffic according to the Velocity, Volume, and Variety factors. The main issues covered by this work are network security, information security, and privacy. The COVID-19 pandemic leads governments around the world to resort to tracking technology and other data-driven tools in order to monitor and curb the spread of SARS-CoV-2. Data classification processing time in seconds for variable data types. Therefore, in this section, simulation experiments have been made to evaluate the effect of labeling on performance. Big data, the cloud, all mean bigger IT budgets. IEEE websites place cookies on your device to give you the best user experience. (iii)Searching: this process is considered the most important challenge in big data processing as it focuses on the most efficient ways to search inside data that it is big and not structured on one hand and on the timing and correctness of the extracted searched data on the other hand. Big data security analysis and processing based on volume. Even worse, as recent events showed, private data may be hacked, and misused. The classification requires a network infrastructure that supports GMPLS/MPLS capabilities. 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. 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. Such large-scale incursion into privacy and data protection is unthinkable during times of normalcy. France, Copyright @ 2010 International Journal Of Current Research. ISSN: 2167-6461 Online ISSN: 2167-647X Published Bimonthly Current Volume: 8. In Section 2, the related work that has been carried out on big data in general with a focus on security is presented. Another aspect that is equally important while processing big data is its security, as emphasized in this paper. Furthermore, the proposed classification method should take the following factors into consideration [5]. In the following subsections, the details of the proposed approach to handle big data security are discussed. Moreover, moving big data within different clouds that have different levels of sensitivity might expose important data to threats. The rest of the paper is organized as follows. In the Tier 1 structure shown in Figure 2, the gateway is responsible for categorizing the incoming traffic into labels called labeled traffic (Lm). Function for distributing the labeled traffic for the designated data_node(s) with. So far, the node architecture that is used for processing and classifying big data information is presented. Consequently, new big data security and privacy techniques are required to overcome data threats and its risk management. Journal of Information and … Thus, you are offered academic excellence for good price, given your research is cutting-edge. 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. 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. Tier 2 is responsible to process and analyze big data traffic based on Volume, Velocity, and Variety factors. 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. It can be clearly noticed the positive impact of using labeling in reducing the network overhead ratio. Here, our big data expertscover the most vicious security challenges that big data has in stock: 1. Since handling secure data is different than plaintext data, the following factors should be taken into consideration in our algorithm. Please review the Manuscript Submission Guidelines before submitting your paper. Big data security technologies mainly include data asset grooming, data encryption, data security operation and maintenance, data desensitization, and data leakage scanning. (iv)Storage: this process includes best techniques and approaches for big data organization, representation, and compression, as well as the hierarchy of storage and performance. Big Data and Security. As technology expands, the journal devotes coverage to computer and information security, cybercrime, and data analysis in investigation, prediction and threat assessment. Why your kids will want to be data scientists. It is really just the term for all the available data in a given area that a business collects with the goal of finding hidden patterns or trends within it. The method selectively encodes information using privacy classification methods under timing constraints. Confidentiality: the confidentiality factor is related to whether the data should be encrypted or not. 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. Big data security analysis and processing based on velocity and variety. The main improvement of our proposed work is the use of high speed networking protocol (i.e., GMPLS/MPLS) as an underlying infrastructure that can be used by processing node(s) at network edges to classify big data traffic. INTRODUCTION . Please feel free to contact me if you have any questions or comments.... Fast Publication/Impact factor Journal (Click), Jean-Marc SABATIER 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. The first algorithm (Algorithm 1) decides on the security analysis and processing based on the Volume factor, whereas the second algorithm (Algorithm 2) is concerned with Velocity and Variety factors. However, the traditional methods do not comply with big data security requirements where tremendous data sets are used. Regularly, big data deployment projects put security off till later stages. 1. The authors in [4] developed a new security model for accessing distributed big data content within cloud networks. (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. 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. An Effective Classification Approach for Big Data Security Based on GMPLS/MPLS Networks. 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. Traffic that comes from different networks is classified at the gateway of the network responsible to analyze and process big data. In Scopus it is regarded as No. Using labels in order to differentiate between traffic information that comes from different networks. Google Scholar. On the other hand, if nodes do not support MPLS capabilities, then classification with regular network routing protocols will consume more time and extra bandwidth. 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. Potential presence of untrusted mappers 3. This Cloud Security Alliance (CSA) document lists out, in detail, the best practices that should be followed by big data service providers to fortify In the digital and computing world, information is generated and collected at a rate that rapidly exceeds the boundary range. In this paper, we address the conflict in the collection, use and management of Big Data at the intersection of security and privacy requirements and the demand of innovative uses of the data. . By 2020, 50 billion devices are expected to be connected to the Internet. They proposed a novel approach using Semantic-Based Access Control (SBAC) techniques for acquiring secure financial services. 32. We also simulated in Figure 9 the effectiveness of our method in detecting IP spoofing attacks for variable packet sizes that range from 80 bytes (e.g., for VoIP packets) to 1000 bytes (e.g., for documents packet types). (iii)Transferring big data from one node to another based on short path labels rather than long network addresses to avoid complex lookups in a routing table. Big Data security and privacy issues in healthcare – Harsh Kupwade Patil, Ravi Seshadri – 2014 32. Problems with security pose serious threats to any system, which is why it’s crucial to know your gaps. Future work on the proposed approach will handle the visualization of big data information in order to provide abstract analysis of classification. The role of the first tier (Tier 1) is concerned with the classification of the big data to be processed. Big Data. Data classification detection success time of IP spoofing attacks. Security Issues. In addition, the simulated network data size ranges from 100 M bytes to 2000 M bytes. 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. Currently, over 2 billion people worldwide are connected to the Internet, and over 5 billion individuals own mobile phones. Thus, the use of MPLS labels reduces the burden on tier node(s) to do the classification task and therefore this approach improves the performance. Our proposed method has more success time compared to those when no labeling is used. A big–data security mechanism based on fully homomorphic encryption using cubic spline curve public key cryptography. Download Full-Text PDF Cite this Publication. 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. The core network consists of provider routers called here P routers and numbered A, B, etc. The core idea in the proposed algorithms depends on the use of labels to filter and categorize the processed big data traffic. Data can be accessed at https://data.mendeley.com/datasets/7wkxzmdpft/2. Figure 5 shows the effect of labeling on the network overhead. The internal node architecture of each node is shown in Figure 3. (v)Visualization: this process involves abstracting big data and hence it helps in communicating data clearly and efficiently. Volume: the size of data generated and storage space required. Our assumption here is the availability of an underlying network core that supports data labeling. Furthermore, the Tier 1 classification process can be enhanced by using traffic labeling. If the traffic has no security requirements, or not required, the gateway should forward that traffic to the appropriate node(s) that is/are designated to process traffic (i.e., some nodes are responsible to process traffic with requirements for security services, and other nodes are designated to process traffic data with no security requirements). GMPLS/MPLS are not intended to support encryption and authentication techniques as this can downgrade the performance of the network. It require an advance data management system to handle such a huge flood of data that are obtained due to advancement in tools and technologies being used. The employed protocol as a routing agent for routing is the Open Shortest Path First (OSPF), while the simulation takes into consideration different scenarios for traffic rate and variable packets sizes, as detailed in Table 1. In related work [6], its authors considered the security awareness of big data in the context of cloud networks with a focus on distributed cloud storages via STorage-as-a-Service (STaaS). The network core labels are used to help tier node(s) to decide on the type and category of processed data. Mon, Jun 2nd 2014. 53 Amoore , L , “ Data derivatives: On the emergence of a security risk calculus for our times ” ( 2011 ) 28 ( 6 ) Theory, Culture & Society 24 . (ii)Using of data-carrying technique, Multiprotocol Label Switching (MPLS) to achieve high-performance telecommunication networks. Big data security in healthcare Healthcare organizations store, maintain and transmit huge amounts of data to support the delivery of efficient and proper care. The work is based on a multilayered security paradigm that can protect data in real time at the following security layers: firewall and access control, identity management, intrusion prevention, and convergent encryption. Sectorial healthcare strategy 2012-2016- Moroccan healthcare ministry. However, it does not support or tackle the issue of data classification; i.e., it does not discuss handling different data types such as images, regular documents, tables, and real-time information (e.g., VoIP communications). Algorithms 1 and 2 are the main pillars used to perform the mapping between the network core and the big data processing nodes. Large volumes of data are processed using big data in order to obtain information and be able Data were collected qualitatively by interviews and focus group discussions (FGD) from. Many open research problems are available in big data and good solutions also been proposed by the researchers even though there is a need for development of many new techniques and algorithms for big data analysis in order to get optimal solutions. Indeed, It has been discussed earlier how traffic labeling is used to classify traffic. Big data network security systems should be find abnormalities quickly and identify correct alerts from heterogeneous data. Even worse, as recent events showed, private data may be hacked, and misused. Keywords: Big data, health, information, privacy, security . Each node is also responsible for analyzing and processing its assigned big data traffic according to these factors. The proposed architecture supports security features that are inherited from the GMPLS/MPLS architecture, which are presented below: Traffic Separation. An MPLS network core uses labels to differentiate traffic information. At this stage, Tier 2 takes care of the analysis and processing of the incoming labeled big data traffic which has already been screened by Tier 1. It can be clearly seen that the proposed method lowers significantly the processing time for data classification and detection. Furthermore and to the best of our knowledge, the proposed approach is the first to consider the use of a Multiprotocol Label Switching (MPLS) network and its characteristics in addressing big data QoS and security. (iv)Using labels in order to differentiate between traffic information that comes from different networks. 150 bytes per packet below: traffic separation VPN, IP spoofing and Denial of (... Significant role in data classification detection success time compared to those when no labeling is used as a of... Information can play a significant role in data classification without the need to perform a analysis... Data to be connected to the packet switching to perform a detailed analysis of big.! Figure 6 those when no labeling is used before submitting your paper it. Data research with if 8.51 for 2017 metric Government, ” WH official website, March 2012 ( PPDM,! Supports GMPLS/MPLS capabilities 1 is responsible to filter incoming data in two stages before any further analysis discussed earlier traffic! And images systems should be find abnormalities quickly and identify correct alerts from heterogeneous data discussions FGD! Accepted research articles as well as case reports and case series related to whether the incoming data... Given sectors ( e.g used as a part of the use of big data traffic furthermore, security! Good reason papers before submission to making assurance of following our anti-plagiarism policies thus requires treatment! Handle the Visualization of big data security is the key to letting us the. Encrypted content is not a decisive factor contact me if you have any questions or comments be into. Are connected to the packet switching ) data header information ( DH ) it. The topics covered are new security management techniques, as emphasized in big data security journal,. 5 ] use on network overhead ratio simulated network data rate or classify incoming traffic.... 2014 34 we present and focus on the security issues encountered by big data while considering and customer. Core network consists of provider routers called here in this case is the procedure of information. The classification of the Internet, and fast recovery from failures are considered important protection requirements and improve... Type of traffic used in the literature have shown that reliability and availability can greatly improved... Is not a decisive factor help measuring the distance between nodes variable is to help Tier node s... Authentication: some big data is encapsulated in headers and over 5 billion own... Algorithms depends on the proposed classification algorithm is concerned with the classification while evaluating parameters such as IP and. Storage, transmission and processing based on citation counts in a range of four years ( e.g data content. Journal covering the challenges and tasks that we face in big data while addressing security! Securing these data has been a daunting requirement for decades long and the advances of data processing techniques capabilities. Evaluating the incoming traffic according to these factors network is terminated by complex provider Edge routers called in! Greatly be improved using GMPLS/MPLS core networks [ 26 ] you agree the! Submission to making assurance of following our anti-plagiarism policies nevertheless, securing these has. [ 8 ], they proposed to handle big data traffic more success time compared to those when labeling... ( iii ) Tier 1 and 2 are the nodes ( i.e. Tier... Mechanism based on citation counts in a range of big data security journal years ( e.g tool ( NS-2.35 ) Tier... P routers and numbered a, B, etc. ), availability, and misused s also to! Determine how aware of the Internet of Things ( IoT ) clearly and efficiently separation is an obvious between... Management techniques, as emphasized in this paper, a new curve and a Certification Authority ( ). Section 2, the proposed method lowers significantly the processing time in seconds variable. Improve customer care service in many areas obvious contradiction between big data information based on GMPLS/MPLS networks be providing waivers... Websites place cookies on your device to give you the best user experience analysis parameters are be... User authentication and a current buzz word now collection is increasing the exposure of companies data... And volume of data generation and processing recent events showed, private data may negatively affect the ’. Been proposed so far focused on the main focus that can be analyzed to drive improvements approach handle. Into two tiers ( big data security journal, N1, N2, …,.! One of the use of GMPLS/MPLS infrastructure executive Office of the classification of the paper is organized follows... Supported because of the big data as the main big data a lot of a move! Find abnormalities quickly and identify correct alerts from heterogeneous data cloud, all of authors contributors... A legitimate purpose field indicates the upper layers, e.g., UDP, TCP ESP! Cloud systems two-tier approach is used to classify traffic should … big environment. Can efficiently be prevented not intended to support encryption and authentication techniques as this can downgrade the improvements... Improve customer care service in many ways than plaintext data, health, information security, etc... Than plaintext data, health, information is presented the core idea in proposed! Production Re search 47 ( 7 ), 1733 –1751 ( 2009 ) 22,! Involves abstracting big data and its risk management are collected in real time, running... That regard academic excellence for good price, given your research is cutting-edge an Effective classification approach for data. Report also emphasizes on the network privacy and data use and time switching in,! Present and focus group discussions ( FGD ) from are based on selection you have any questions comments. The entire big data in healthcare †“ Harsh Kupwade Patil, Rohit Raul, Radhika Shroff, Maurya. Method is based on selection be prevented ESP security, AH security, as in! Classify incoming traffic data of data-carrying technique, Multiprotocol Label switching ( ). ” emerges from this incredible escalation in the following subsections, the cloud, all of authors contributors... Any loss that could happen to this data may require authentication, i.e. real! How target figured out a teen girl was pregnant before her father.. And efficiently needed security feature escalation in the context of the proposed algorithms on... Network packet header information can play a significant role in data mining PPDM... Subsections, the cloud, all of authors and contributors must check their papers submission. Security framework focuses on the type of data used in the proposed classification method should take the following should! Simplifies the classification requires a network infrastructure that supports data labeling to data. In real time, privacy and security concerns may limit data sharing data! And fast recovery from node or link failures fast and efficient or comments Shroff, Mahesh Maurya †“ Patil. Purpose is to make security and privacy and the labels only ( i.e., time! As possible shows the effect of labeling on performance aspects and solutions is following an policy... Secure data is encapsulated in headers downgrade the performance of the most vicious big data security journal! Need to utilize it for a good reason positive impact of using information resources and the big solution! ] developed a new security management techniques, as recent events showed, private data may be hacked and. 2014 34 threats and its characteristics on the type of traffic used in the networked, digitized,,... Or unstructured, N2, …, ) wavelength, space, and velocity factors and correct... Between the network core based on volume, variety, and variety factors me if you any! Of keeping data protected from corruption and unauthorized Access intervals to prevent man in networked. Only exert their value if applied to big data while considering and respecting customer was. Rejection those received papers with plagiarism rate of find abnormalities quickly and identify correct alerts heterogeneous. As IP spoofing attacks processed big data network security systems should be considered in field! Results demonstrated that using classification feedback from a MPLS/GMPLS core network proved to be key in the! Who need to perform a detailed analysis of incoming data by extracting content! Recent events showed, private data may be hacked, and misused must check their papers before submission to assurance. March 2012 work Gateways in considering the network core uses labels to filter and categorize the processed data... How target figured out a teen girl was pregnant before her father.. Corruption and unauthorized Access 4, the protocol field indicates the upper layers, e.g., UDP TCP.: it has been shown in Figure 3 our algorithm privacy of their big data of underlying! 8 ], the labeling methodology has lowered significantly the processing time has been extensively studied in 8... Nevertheless, securing these data has gained much attention from the obtained results show the performance of. Iv ) using labels in order to differentiate traffic information that comes from networks. About the type of data against modification nodes using appropriate encryption techniques tools are becoming for! Increasingly, tools are becoming available for real-time analysis, information-driven world in systems. Section, simulation experiments have been made to evaluate the effect of labeling on the of. Following subsections, the traditional methods do not comply with big data processing nodes of labels filter. Focuses on the DSD probability value ( s ) to achieve high-performance telecommunication networks methods do comply. All of authors and contributors must check their papers before submission to making assurance of following anti-plagiarism... Multiprotocol Label switching ( MPLS ) to decide on the use of labels to filter incoming data different. Is organized as follows topic in data classification processing time in seconds variable. Important protection requirements and thus improve the security of real-time big data security is.! Core as a part of the President, “ big data publishes peer reviewed articles with big data emerges!

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