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What are you going to propose to solve the problem that is existing in the current/existing solutions; (Mention the initial solution here). You can use a diagram to clarify that with writing text
How you are going to do your research (step by step methodology is needed).
Big Data is an emerging field that involves huge sets of data that cannot be captured or analyzed using traditional or standard software tools. This data keeps increasing day by day, is collected from multiple sources and thus, is mostly unstructured. Big Data can be obtained from social media, images, videos, sensors, surveillance systems, scientific applications, medical records, system logs, business applications, internet search, and so on. Big data is gaining popularity as it cannot just connect to a large number of data sources but these sources can be most advanced like Internet of Things and Cloud Storage.
A huge amount of power can be gained with cloud services and a large amount of data can process parallel. While this gives significant advantages to businesses, it also poses a variety of risks and challenges concerning security of the system infrastructure that support Big Data Applications. Some key topics of discussion have emerged in past few years regarding data security and have been explored by various researchers. These include data management, infrastructure security, data privacy and data integrity. As more and more data is generated everyday and a large number of Big data applications have emerged, security of Big Data has become a primary concern for most organizations [1]. The purpose of this research is to explore past literatures on the Big Data security with aim to identify challenges as well as solutions. Key objectives of this research are:
This research would explore different security challenges that are faced by Big Data technology and the solutions that are used by organizations for their protection. Some of the security challenges that are faced with the use of Big data technologies include fault intolerance, expensive data, quality issues, authentication, confidentiality, and more [1]. Some of the security measures that have been previously studies in the field of Big Data include securing of computations, use of security best practices, securing data storage, end point input validation, real time security monitoring, privacy preserving data mining, data centric security, and granular audits. The research would make an attempt to understand how effective are these methods in their capability to overcome the Big Data security challenges faced by various organizations today.
For this, the research would use a critical analysis of the secondary qualitative data that would be obtained from previous studies done on big data security. The research would be carried out in five stages including research questions, research method, study selection, data analysis, and interpretations.
Research Questions: the questions are already identified in the introduction and these would be refined based on the current knowledge on the subject. These questions will still explore security challenges, Big data characteristics, security dimensions and security solutions for Big data.
Research Method: The data would be collected on the security challenges and security solutions. The data would be collected from an extensive search done on the common libraries have large amount of literature on Big Data security such as IEEE, ScienceDirect, Google Scholar, ACM, and SCOPUS.
Study Selection: Selected papers that involve discussions on Big Data challenges, security dimensions, and security solutions would be reviewed and critically analyzed. The selection would be based on the conformance of the research paper content with the objectives of this research. The data collected on security challenges and solutions would be classified into a set of broad topics of Big data security.
Data Analysis: An assessment would then be made analyzing the cases discussed in past researches to understand how effective the security solutions have been in strengthen the security in Big Data. For this, various security dimensions and Big data characteristics that could be causing security problems would be identified and would be assessed with respect to the available solutions [1]. The data analysis would make use of critical analysis which would be used with an aim to identify if selected solution has the capability to completely address the needs of security dimensions and Big data characteristics that can affect security. For the assessment of the capability of security solutions, some cases of organizations would also be used and an impact based analysis would be conducted. This would be useful in analysis as specific industries would have specific big data application, major concerns faced in the field, and several security measures taken by them for protection. These would include finance, telecommunication and energy.
Interpretations: The data analyzed would be interpreted based on which recommendations would be given on the best practices to be used for strengthening security in Big Data systems.
Upon exploring the past literature, some security topics of concern to Big Data systems were identified. Key topics included infrastructure security, data privacy, data management and data integrity. For each of these topics, some sub-topics were also identified that are listed in the table below:
Security Topics | Security Sub-topics | Challenges (CSA) |
infrastructure security | Hadoop security, communication security, architecture security, authentication, availability [3] | Secure Distributed Processing , Security Best Actions |
data privacy | Access control, cryptography, confidentiality, privacy preserving queries, anonymization, social media privacy, and differential privacy | Data Mining Preserving Data Privacy, Cryptographic security solutions, Granular Access Control |
data management | Storage security, laws and policies, sharing algorithms | Secure Data Storage, secure Transaction Logs, Granular Audits, Data Provenance |
data integrity and reactive security | Integrity, attack detection, recovery [1] | End to end data filtering, data validation, real time security supervision [5] |
A per Cloud Security Alliance, security challenges can be defined for each of these categories or key security topics as shown in the table above. OWASP also identifies some top challenges that can affect big data in presence of Internet of things and these incldue:
Previous researches also revealed specific characteristics of Big data technologies that can affect the security of information and these included variety, value, velocity, volume, veracity, and variability.
Each of these is required to be tackled while designing a security solution for big data. Thus, these can add to the security dimensions that would be used for the assessment of the Big data security solutions.
Considering the cases of different business industries, some challenges of big data were identified for sectors like telecommunications, finance, and energy. Each of these industries has different set of challenges as shown in the table below:
Sector | Big Data Security Challenges |
Finance | Device Trustworthiness for data collection Source validation Data filtering Application software security Access control Authentication Interoperability Distributed systems security |
Energy | Source validation Data filtering Application software security Infrastructure Security Distributed systems security Authentication Access Control |
Telecommunications | Source validation Data filtering Application software security Authentication Access Control Infrastructure Security Supply chain security Secure data management Secure Cloud use [5] |
When exploring security solutions for Big data, a research revealed some components that are needed for a full protection from threat when using Big data as core technologies in an organization. These components include human insight, data organization, malicious behavior prediction, feedback for continuous improvement, complex relationships, threat landscape, community network, user behavior, search algorithms, and more.
The project would be completed in 12 weeks and the timelines that would be followed in the research are given in the table below:
Week | Task |
1 | Read papers on Big Data security and identify some topics and subtopics to study in depth |
2 | Explore the literature on the security challenges of Big Data as per the identified topics |
3 | Explore the literature on the security dimensions affecting the identified challenges from literature study |
4 | Identify security solutions for Big data based on the past researches done in the field |
5 | Assess Big Data security solutions against security dimensions to assess their effectiveness |
6 | Critically analyze the collected literature to come up with the assessment of security solutions and take feedback from tutor to ensure right apraoch is used for analysis |
7 | Write introduction and complete literature review |
8 | Critically analyze each selected research in the data analysis section of the research document |
9 | Come up with recommendations on security solutions based on the explored research data |
10 | Give recommendations and complete the report on Big Data Security Challenges and solutions |
11 | Proofread and edit the complete research paper considering citations, grammar, formats and references |
12 | Finalize the research report, get feedback from tutor and complete the final submission |
[1] | C. Schmitt, "Security and Privacy in the Era of Big Data," Rensi, 2014. |
[2] | G. B. Tarekegn and Y. Y. Munaye, "CHALLENGES AND FUTURE SCOPE CHALLENGES AND FUTURE SCOPE," International Journal of Computer Engineering & Technology (IJCET) , vol. 7, no. 4, p. 12–24, 2016. |
[3] | J. Moura and C. Serrão, "Security and Privacy Issues of Big Data," Instituto Universitário de Lisboa, 2014. |
[4] | Zettaset, "The Big Data Security Gap: Protecting the Hadoop Cluster," Zettaset, 2014. |
[5] | J. Moreno, M. A. Serrano and E. Fernández-Medina, "Main Issues in Big Data Security," Alarcos Research Group, 2016. |
[6] | T. Frankenfield, "Big Data Management and Security Audit Concerns and Business Risks," Mercury Insurance, 2015. |
[7] | Oracle, "Enterprise Security for Big Data Environments," Oracle, 2016. |
[8] | R. Naydeno, D. Liver, L. Dupre, E. Chalvatzi and C. Skouloudi., "Big Data Security: Good Practices and Recommendations on the Security of Big Data Systems," European Union Agency For Network And Information Security, 2015. |
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