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]. The first tier classifies the data based on its structure and on whether security is required or not. The core idea in the proposed algorithms depends on the use of labels to filter and categorize the processed big data traffic. The IEEE Transactions on Big Data publishes peer reviewed articles with big data as the main focus. 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. In Section 2, the related work that has been carried out on big data in general with a focus on security is presented. 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. 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 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. The obtained results show the performance improvements of the classification while evaluating parameters such as detection, processing time, and overhead. Big Data. 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. Variety: the category of data and its characteristics. The authors declare that they have no conflicts of interest. 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 could not be described just in terms of its size. 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. 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 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. 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. 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. Function for distributing the labeled traffic for the designated data_node(s) with. Review articles are excluded from this waiver policy. (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. So instead of giving generic advice about “security,” I want to show you some ways you can secure yourself and … This Cloud Security Alliance (CSA) document lists out, in detail, the best practices that should be followed by big data service providers to fortify 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. The authors in [4] developed a new security model for accessing distributed big data content within cloud networks. Data classification processing time in seconds for variable data types. Consequently, the gateway is responsible for distributing the labeled traffic to the appropriate node (NK) for further analysis and processing at Tier 2. Thus, you are offered academic excellence for good price, given your research is cutting-edge. Regularly, big data deployment projects put security off till later stages. 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 … Our assumption here is the availability of an underlying network core that supports data labeling. Each node is also responsible for analyzing and processing its assigned big data traffic according to these factors. Big data security technologies mainly include data asset grooming, data encryption, data security operation and maintenance, data desensitization, and data leakage scanning. Indeed, our work is different from others in considering the network core as a part of the big data classification process. 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. 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 current security challenges in big data environment is related to privacy and volume of data. The VPN capability that can be supported in this case is the traffic separation, but with no encryption. As can be noticed from the obtained results, the labeling methodology has lowered significantly the total processing time of big data traffic. 31. So, All of authors and contributors must check their papers before submission to making assurance of following our anti-plagiarism policies. Editor-in-Chief: Zoran Obradovic, PhD. Data Header information (DH): it has been assumed that incoming data is encapsulated in headers. Tier 2 is responsible to process and analyze big data traffic based on Volume, Velocity, and Variety factors. The ratio effect of labeling use on network overhead. Big data security in healthcare Healthcare organizations store, maintain and transmit huge amounts of data to support the delivery of efficient and proper care. Data security is a hot-button issue right now, and for a good reason. 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. 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). 1 journal in Big data research with IF 8.51 for 2017 metric. The core idea in the proposed algorithms depends on the use of labels to filter and categorize the processed big data traffic. As mentioned in previous section, MPLS is our preferred choice as it has now been adopted by most Internet Service Providers (ISPs). Wed, Jun 4th 2014. Data classification detection success time of IP spoofing attacks. At the same time, privacy and security concerns may limit data sharing and data use. 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. 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. This in return implies that the entire big data pipeline needs to be revisited with security and privacy in mind. It can be clearly noticed the positive impact of using labeling in reducing the network overhead ratio. However, more institutions (e.g. The proposed security framework focuses on securing autonomous data content and is developed in the G-Hadoop distributed computing environment. Most Read. Using labels in order to differentiate between traffic information that comes from different networks. 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. Finance, Energy, Telecom). (ii)Tier 1 is responsible to filter incoming data by deciding on whether it is structured or nonstructured. The current security challenges in big data environment is related to privacy and volume of data. 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. The increasing trend of using information resources and the advances of data processing tools lead to extend usage of big data. (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. 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. Sectorial healthcare strategy 2012-2016- Moroccan healthcare ministry. 32. A flow chart of the general architecture for our approach. In contrast, the authors in [12] focused on the big data multimedia content problem within a cloud system. 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. Automated data collection is increasing the exposure of companies to data loss. 51 Aradau, C and Blanke, T, “ The (Big) Data-security assemblage: Knowledge and critique ” (2015) 2 (2) Security Dialogue. 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. This factor is used as a prescanning stage in this algorithm, but it is not a decisive factor. It is also worth noting that analyzing big data information can help in various fields such as healthcare, education, finance, and national security. 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.). 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. Our proposed method has more success time compared to those when no labeling is used. 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. Problems with security pose serious threats to any system, which is why it’s crucial to know your gaps. We are committed to sharing findings related to COVID-19 as quickly as possible. In the proposed approach, big data is processed by two hierarchy tiers. 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. 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. The GMPLS/MPLS simplifies the classification by providing labeling assignments for the processed big data traffic. Authentication: some big data may require authentication, i.e., protection of data against modification. (ii)Using of data-carrying technique, Multiprotocol Label Switching (MPLS) to achieve high-performance telecommunication networks. Mon, Jun 2nd 2014. (iv)Using labels in order to differentiate between traffic information that comes from different networks. Consequently, new big data security and privacy techniques are required to overcome data threats and its risk management. France, Copyright @ 2010 International Journal Of Current Research. 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. 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. Online Now. However, the proposed approach also requires feedback from the network in order to classify the processed data. The type of traffic used in the simulation is files logs. This study aims to determine how aware of the younger generation of security and privacy of their big data. Furthermore, the proposed classification method should take the following factors into consideration [5]. Download Full-Text PDF Cite this Publication. However, in times of a pandemic the use of location data provided by telecom operators and/or technology … Each Tier 2 node applies Algorithms 1 and 2 when processing big data traffic. 33. 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. (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. 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. When considering a big data solution, you can best mitigate the risks through strategies such as employee training and varied encryption techniques. The journal will accept papers on … Misuse of information from big data often results in violations of privacy, security, and cybercrime. Potential presence of untrusted mappers 3. 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. Furthermore, in [9], they considered the security of real-time big data in cloud systems. In the Tier 1 structure shown in Figure 2, the gateway is responsible for categorizing the incoming traffic into labels called labeled traffic (Lm). However, the algorithm uses a controlling feedback for updating. The GMPLS/MPLS network is terminated by complex provider Edge routers called here in this work Gateways. The main components of Tier 2 are the nodes (i.e., N1, N2, …, ). 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. Google Scholar. The key is dynamically updated in short intervals to prevent man in the middle attacks. 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,”. 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. 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. The network core labels are used to help tier node(s) to decide on the type and category of processed data. 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. Specifically, they summarized and analyzed the main results obtained when external integrity verification techniques are used for big data security within a cloud environment. This problem is exaggerated in the context of the Internet of Things (IoT). Data Source and Destination (DSD): data source as well as destination may initially help to guess the structure type of the incoming data. The articles will provide cro. (ii)Data Header information (DH): it has been assumed that incoming data is encapsulated in headers. In addition, the protocol field indicates the upper layers, e.g., UDP, TCP, ESP security, AH security, etc. Classifying big data according to its structure that help in reducing the time of applying data security processes. 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 GMPLS extends the architecture of MPLS by supporting switching for wavelength, space, and time switching in addition to the packet switching. 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. Sensitivities around big data security and privacy are a hurdle that organizations need to overcome. 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]. Reliability and Availability. The type of data used in the simulation is VoIP, documents, and images. The labels can carry information about the type of traffic (i.e., real time, audio, video, etc.). Big Data in Healthcare – Pranav Patil, Rohit Raul, Radhika Shroff, Mahesh Maurya – 2014 34. Therefore, attacks such as IP spoofing and Denial of Service (DoS) can efficiently be prevented. This press … 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. Therefore, with security in mind, big data handling for encrypted content is not a simple task and thus requires different treatment. (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. IEEE websites place cookies on your device to give you the best user experience. Hence, it helps to accelerate data classification without the need to perform a detailed analysis of incoming data. Data provenance difficultie… Even worse, as recent events showed, private data may be hacked, and misused. Thus, the treatment of these different sources of information should not be the same. 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. Figure 5 shows the effect of labeling on the network overhead. Using of data-carrying technique, Multiprotocol Label Switching (MPLS) to achieve high-performance telecommunication networks. These security technologies can only exert their value if applied to big data systems. The technique analyzes big data by extracting valuable content that needs protection. Daily tremendous amount of digital data is being produced. Algorithms 1 and 2 are the main pillars used to perform the mapping between the network core and the big data processing nodes. Having reliable data transfer, availability, and fast recovery from failures are considered important protection requirements and thus improve the security. 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. The challenge to legitimately use big data while considering and respecting customer privacy was interestingly studied in [5]. 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). (2018). 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 … Forbes, Inc. 2012. 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. International Journal of Production Re search 47(7), 1733 –1751 (2009) 22. But it’s also crucial to look for solutions where real security data can be analyzed to drive improvements. An Effective Classification Approach for Big Data Security Based on GMPLS/MPLS Networks. 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. Please feel free to contact me if you have any questions or comments. ISSN: 2167-6461 Online ISSN: 2167-647X Published Bimonthly Current Volume: 8. Troubles of cryptographic protection 4. (iii)Tier 2 is responsible to process and analyze big data traffic based on Volume, Velocity, and Variety factors. At the same time, privacy and security concerns may limit data sharing and data use. In the following subsections, the details of the proposed approach to handle big data security are discussed. 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). However, Virtual Private Networks (VPNs) capabilities can be supported because of the use of GMPLS/MPLS infrastructure. Please feel free to contact me if you have any questions or comments.... Fast Publication/Impact factor Journal (Click), Jean-Marc SABATIER In Section 4, the validation results for the proposed method are shown. Here, our big data expertscover the most vicious security challenges that big data has in stock: 1. In [7], they also addressed big data issues in cloud systems and Internet of Things (IoT). Any loss that could happen to this data may negatively affect the organization’s confidence and might damage their reputation. Using an underlying network core based on a GMPLS/MPLS architecture makes recovery from node or link failures fast and efficient. Traffic that comes from different networks is classified at the gateway of the network responsible to analyze and process big data. The purpose is to make security and privacy communities realize the challenges and tasks that we face in Big Data. Figure 4 illustrates the mapping between the network core, which is assumed here to be a Generalized Multiprotocol Label Switching (GMPLS) or MPLS network. Velocity: the speed of data generation and processing. Communication parameters include traffic engineering-explicit routing for reliability and recovery, traffic engineering- for traffic separation VPN, IP spoofing. All rights reserved, IJCR is following an instant policy on rejection those received papers with plagiarism rate of. Authors in [2] propose an attribute selection technique that protects important 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. Indeed, It has been discussed earlier how traffic labeling is used to classify traffic. Furthermore, the Tier 1 classification process can be enhanced by using traffic labeling. However, to generate a basic understanding, Big Data are datasets which can’t be processed in conventional database ways to their size. This is especially the case when traditional data processing techniques and capabilities proved to be insufficient in that regard. Complicating matters, the healthcare industry continues to be one of the most susceptible to publicly disclosed data breaches. 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. In addition, the. In contrast, the second tier analyzes and processes the data based on volume, variety, and velocity factors. 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. (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. The report also emphasizes on the growth prospects of the global Big Data Network Security Software market for the period 2020-2025. Most Cited. 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). (ii) Real time data are usually assumed less than 150 bytes per packet. 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 internal node architecture of each node is shown in Figure 3. Therefore, header information can play a significant role in data classification. (v)Visualization: this process involves abstracting big data and hence it helps in communicating data clearly and efficiently. Executive Office of the President, “Big Data Across the Federal Government,” WH official website, March 2012. Moreover, Tier 2 is responsible for evaluating the incoming traffic according to the Velocity, Volume, and Variety factors. In general, big data are collected in real time, typically running into the millions of transactions per second for large organizations. 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. 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. While opportunities exist with Big Data, the data can overwhelm traditional 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). By 2020, 50 billion devices are expected to be connected to the Internet. This kind of data accumulation helps improve customer care service in many ways. GMPLS/MPLS are not intended to support encryption and authentication techniques as this can downgrade the performance of the network. Other security factors such as Denial of Service (DoS) protection and Access Control List (ACL) usage will also be considered in the proposed algorithm. Actually, the traffic is forwarded/switched internally using the labels only (i.e., not using IP header 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. Big data security analysis and processing based on volume. So, All of authors and contributors must check their papers before submission to making assurance of following our anti-plagiarism policies. 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. Large volumes of data are processed using big data in order to obtain information and be able 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. Volume: the size of data generated and storage space required. To illustrate more, traffic separation is an essential needed security feature. Among the topics covered are new security management techniques, as well as news, analysis and advice regarding current research. Handlers of big data should … Big data can contain different kinds of information such as text, video, financial data, and logs, as well as secure or insecure information. The network core labels are used to help tier node(s) to decide on the type and category of processed data. The method selectively encodes information using privacy classification methods under timing constraints. Moreover, moving big data within different clouds that have different levels of sensitivity might expose important data to threats. 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). 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.). Thus, security analysis will be more likely to be applied on structured data or otherwise based on selection. Big Data. Therefore, security implementation on big data information is applied at network edges (e.g., network gateways and the big data processing nodes). 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. The MPLS header is four bytes long and the labels are created from network packet header information. The term “big data” refers to the massive amounts of digital information companies and governments collect about human beings and our environment. Forget big brother - big sister's arrived. So far, the node architecture that is used for processing and classifying big data information is presented. Big data security analysis and processing based on velocity and variety. The research on big data has so far focused on the enhancement of data handling and performance. The extensive uses of big data bring different challenges, among them are data analysis, treatment and conversion, searching, storage, visualization, security, and privacy. In addition, the simulated network data size ranges from 100 M bytes to 2000 M bytes. Management topics covered include evaluation of security measures, anti-crime design and planning, staffing, and regulation of the security … The proposed architecture supports security features that are inherited from the GMPLS/MPLS architecture, which are presented below: Traffic Separation. 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. 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. The performance factors considered in the simulations are bandwidth overhead, processing time, and data classification detection success. 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. Transparency is the key to letting us harness the power of big data while addressing its security and privacy challenges. Finance, Energy, Telecom). Big Data has gained much attention from the academia and the IT industry. The type of traffic used in the simulation is files logs. Data Security. 52 ibid. Big data is becoming a well-known buzzword and in active use in many areas. In addition, authentication deals with user authentication and a Certification Authority (CA). In Section 3, the proposed approach for big data security using classification and analysis is introduced. 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. In other words, this tier decides first on whether the incoming big data traffic is structured or unstructured. Furthermore, honestly, this isn’t a lot of a smart move. 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. 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. Besides that, other research studies [14–24] have also considered big data security aspects and solutions. 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. In today’s era of IT world, Big Data is a new curve and a current buzz word now. Just Accepted. Jain, Priyank and Gyanchandani, Manasi and Khare, Nilay, 2016, Big … In [3], the authors investigated the security issues encountered by big data when used in cloud networks. A flow chart for the general architecture of the proposed method is shown in Figure 1. 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. 32. Furthermore, more security analysis parameters are to be investigated such as integrity and real time analysis of big data. Big data, the cloud, all mean bigger IT budgets. 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. Next, the node internal architecture and the proposed algorithm to process and analyze the big data traffic are presented. The main issues covered by this work are network security, information security, and privacy. The proposed method is based on classifying big data into two tiers (i.e., Tier 1 and Tier 2). 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. 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. 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. Big Data and Security. Struggles of granular access control 6. Even worse, as recent events showed, private data may be hacked, and misused. CiteScore: 7.2 ℹ CiteScore: 2019: 7.2 CiteScore measures the average citations received per peer-reviewed document published in this title. The classification requires a network infrastructure that supports GMPLS/MPLS capabilities. On the other hand, handling the security of big data is still evolving and just started to attract the attention of several research groups. 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. 33. 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. 18 Concerns evolve around the commercialization of data, data security and the use of data against the interests of the people providing the data. It can be noticed that the total processing time has been reduced significantly. Big Data security and privacy issues in healthcare – Harsh Kupwade Patil, Ravi Seshadri – 2014 32. A big–data security mechanism based on fully homomorphic encryption using cubic spline curve public key cryptography. Currently, over 2 billion people worldwide are connected to the Internet, and over 5 billion individuals own mobile phones. Therefore, in this section, simulation experiments have been made to evaluate the effect of labeling on performance. Journal of Information and … Future work on the proposed approach will handle the visualization of big data information in order to provide abstract analysis of classification. Hill K. How target figured out a teen girl was pregnant before her father did. Copyright © 2018 Sahel Alouneh et al. In this section, we present and focus on the main big data security related research work that has been proposed so far. (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. IJCR is following an instant policy on rejection those received papers with plagiarism rate of more than 20%. The role of the first tier (Tier 1) is concerned with the classification of the big data to be processed. 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. 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. . 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. 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. Big Data is a term used to describe the large amount of data in the networked, digitized, sensor-laden, information-driven world. 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 the practice of keeping data protected from corruption and unauthorized access. 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. Hiding Network Interior Design and Structure. An MPLS network core uses labels to differentiate traffic information. 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. The initiative aims at exploring proper and efficient ways to use big data in solving problems and threats facing the nation, government, and enterprise. 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. Keywords: Big data, health, information, privacy, security . 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. It can be clearly seen that the proposed method lowers significantly the processing time for data classification and detection. Thus, security analysis will be more likely to be applied on structured data or otherwise based on selection. An emerging research topic in data mining, known as privacy-preserving data mining (PPDM), has been extensively studied in recent years. Big data is becoming a well-known buzzword and in active use in many areas. Big Data Encryption and Authentication. Data were collected qualitatively by interviews and focus group discussions (FGD) from. 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. The growing popularity and development of data mining technologies bring serious threat to the security of individual,'s sensitive information. Such large-scale incursion into privacy and data protection is unthinkable during times of normalcy. INTRODUCTION . It is worth noting that label(s) is built from information available at (DH) and (DSD). Indeed, the purpose of making the distance between nodes variable is to help measuring the distance effect on processing time. This factor is used as a prescanning stage in this algorithm, but it is not a decisive factor. It is the procedure of verifying information are accessible just to the individuals who need to utilize it for a legitimate purpose. Accordingly, we propose to process big data in two different tiers. 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. It mainly extracts information based on the relevance factor. 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. 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. 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. 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. The effect of labeling implementation on the total nodal processing time for big data analysis has been shown in Figure 6. In the digital and computing world, information is generated and collected at a rate that rapidly exceeds the boundary range. 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. This has led human being in big dilemma. The simulations were conducted using the NS2 simulation tool (NS-2.35). Another aspect that is equally important while processing big data is its security, as emphasized in this paper. Possibility of sensitive information mining 5. The security and privacy protection should be considered in all through the storage, transmission and processing of the big data. 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 core network consists of provider routers called here P routers and numbered A, B, etc. They proposed a novel approach using Semantic-Based Access Control (SBAC) techniques for acquiring secure financial services. Total processing time in seconds for variable network data rate. And in our digitized world, remote workers bear a greater risk when it comes to being hacked. 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. 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 two-tier approach is used to filter incoming data in two stages before any further analysis. CiteScore values are based on citation counts in a range of four years (e.g. “Big data” emerges from this incredible escalation in the number of IP-equipped endpoints. All four generations -- millennials, Gen Xers, baby boomers and traditionalists -- share a lack of trust in certain institutions. However, there is an obvious contradiction between Big Data security and privacy and the widespread use of Big Data. 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. Google Scholar. Now think of all the big data security issues that could generate! In [8], they proposed to handle big data security in two parts. The security industry and research institute are paying more attention to the emerging security challenges in big data environment. Nowadays, big data has become unique and preferred research areas in the field of computer science. Hill K. How target figured out a teen girl … 12 Big data are usually analyzed in batch mode, but increasingly, tools are becoming available for real-time analysis. As technology expands, the journal devotes coverage to computer and information security, cybercrime, and data analysis in investigation, prediction and threat assessment. Moreover, the work in [13] focused on the privacy problem and proposed a data encryption method called Dynamic Data Encryption Strategy (D2ES). Share. 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. Data can be accessed at https://data.mendeley.com/datasets/7wkxzmdpft/2. By using our websites, you agree to the placement of these cookies. Chief Scientific Officer and Head of a Research Group Security Issues. Total Downloads: 24; Authors : Loshima Lohi, Greeshma K V; Paper ID : IJERTCONV4IS06016; Volume & … Tier 1 is responsible to filter incoming data by deciding on whether it is structured or nonstructured. Finally, in Section 5, conclusions and future work are provided. However, the traditional methods do not comply with big data security requirements where tremendous data sets are used. Confidentiality: the confidentiality factor is related to whether the data should be encrypted or not. Total processing time in seconds for variable big data size. 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 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. In case encryption is needed, it will be supported at nodes using appropriate encryption techniques. Google Scholar. Hence, it helps to accelerate data classification without the need to perform a detailed analysis of incoming data. Please review the Manuscript Submission Guidelines before submitting your paper. I. Narasimha, A. Sailaja, and S. Ravuri, “Security Issues Associated with Big Data in Cloud Computing,”, S.-H. Kim, N.-U. The analysis focuses on the use of Big Data by private organisations in given sectors (e.g. Since handling secure data is different than plaintext data, the following factors should be taken into consideration in our algorithm. 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. Nevertheless, securing these data has been a daunting requirement for decades. 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. 2018, Article ID 8028960, 10 pages, 2018. https://doi.org/10.1155/2018/8028960. 53 Amoore , L , “ Data derivatives: On the emergence of a security risk calculus for our times ” ( 2011 ) 28 ( 6 ) Theory, Culture & Society 24 . The rest of the paper is organized as follows. Sign up here as a reviewer to help fast-track new submissions. Many recovery techniques in the literature have shown that reliability and availability can greatly be improved using GMPLS/MPLS core networks [26]. The analysis focuses on the use of Big Data by private organisations in given sectors (e.g. 1. In this paper, a new security handling approach was proposed for big data. However, the algorithm uses a controlling feedback for updating. Based on the DSD probability value(s), decision is made on the security service? In Scopus it is regarded as No. Big data network security systems should be find abnormalities quickly and identify correct alerts from heterogeneous data. Big Data is the leading peer-reviewed journal covering the challenges and opportunities in collecting, analyzing, and disseminating vast amounts of data. 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. 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. 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. Vulnerability to fake data generation 2. 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. In other words, Labels (L) can be used to differentiate or classify incoming traffic data. The proposed classification algorithm is concerned with processing secure big data. In terms of its size 2 ) s ) with for reliability and availability can greatly be improved using core... Implementation on the type of traffic used in cloud systems and Internet of Things IoT. 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