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Cloud Computing Assignment On Big Data Security Issues

s The first research question deals with the way in which big data can be processed efficiently alongside secure through incorporation of Amazon Cloud, Mapreduce framework and Hive whereas the second question deals with effectiveness of MetaCloudStorage Framework in processing and securing the data. How can BigData be processed faster and secure integrating Amazon Cloud, Mapreduce framework and Hive? How effective is MetaCloudStorage Framework in processing and securing the data? Methodology The research is based upon finding out the security challenges faced using Big Data in Cloud Computing, the experimental method has been deemed accurate for this study. In this study proposed research, the chosen research method would aid in experiencing certain benefits as well. Firstly, the chosen method is cost-effective as it would use the AWS (Amazon Web Services) for understanding the categorization of the Data into three levels. Furthermore, using MetaCloudDataStorage security architecture can be proposed for the research to protect the Big Data in Cloud Computing. The classification of the Data includes Normal, Critical and Sensitive and each categorized data would be stored in a different data center. The chosen interface of the MetaCloudDataStorage will redirect the user request efficiently towards the appropriate datacenter available in the Cloud that is offered by varied vendors. To process the log files, the AWS CloudTrail has been incorporated in the proposed methodology and the AWS Key Management Service (KMS) is integrated with the former. It aids in delivering the log files into an Amazon S3 bucket. With the help of effective API, the CloudTrail can be integrated with any kind of application. The AWS CloudTrail also helps in maintaining the API call time and the IP address of the caller. In this methodology, the datacenters have been divided in the form of a sequence of n parts and each part is represented by part k (k (1, n)), and m different storage providers will be used to store this and each provider is identified in the form of provider l (l (1, m)). Furthermore, m (number of providers) is always far lesser than n (parts of the datacenter) and belongs to organizations such as Google, Amazon, and Salesforce. Storing Big Data would form a unique storage path- Mapping Storage Path = {Data ((P1(M1, M2 ... Mr.)) (P2(M1, M2 ... Ms.)) ... (Pn (M1, M2 ... Mt))}; where P- storage provider M- physical storage media Due to the large size of Big Data, encrypting is impossible and hence the proposed methodology has suggested a cryptographic value known as cryptographic virtual mapping of the Big Data. Therefore, this proposed research has suggested protecting the mapping of the various data elements to each providers using the MetaCloudDataStorage interface instead of securing the Big Data itself. Figure 3: End User Accessing Applications and Data in a Distributed Cloud (Source: Figure 4: Security Architecture for Meta Cloud Data Storage in Cloud (Source: The Map Reduce refers to a programming framework that processes tasks parallelly across a huge size of the systems. With the help of Map function, the huge size of input data is split into key, value> pairs. Mapper Function Mapper Function public void Map(Long Writable key, Text value, Output Collector output, Reporter reporter) for each key ? value do Emit(term key; count 1) Reducer Function public void reduce(Text key, Iterator values, Output Collector output, Reporter reporter) sum?0 for each v ? value do sum?sum + v Emit(key, sum) The Big Data collected through the Map function would be analyzed and processed using the Apache Hive in Amazon Web Service. The Apache Hive is an open source software running on top of Hadoop in the Amazon EMR. The architecture Hive has been used to process the stored log files that have been stored in the Amazon S3 such as- 05:05:2020,56,address 05:05:2020,67,index 06:05:2020,47,sponsored The data from the AWS would be fetched using the following Hive command- hive> select count(*) from bank details where Time >= 40; Research Constraints The time for completing was less and hence it could not be properly conducted. Furthermore, there was a lack of data availability and accessibility. There was also elasticity and scalability of the data issues and it is yet to be determined whether the proposed framework would work for all sectors or not. Time Plan and Milestones Task Duration (days) Start Finish Conduct the introduction for the study 5 15-10-2020 20-10-2020 Literature review 8 20-10-2020 28-10-2020 Draft the scope of the study 3 28-10-2020 31-10-2020 Determine the methodology 5 31-10-2020 05-11-2020 Use the Amazon Web Services for Data Categorization 5 05-11-2020 10-11-2020 Develop the MetaCloudDataStorage Architecture 10 10-11-2020 20-11-2020 Classify the data into Normal, Critical and Sensitive 7 20-11-2020 27-11-2020 Process the log files and develop the AWS CloudTrail 4 27-11-2020 01-12-2020 Integrate the AWS Key Management Service (KMS) 5 01-12-2020 06-12-2020 Use the Amazon S3 bucket 5 06-12-2020 11-12-2020 Use Mapper Function 8 11-12-2020 19-12-2020 Use Reducer Function 4 19-12-2020 23-12-2020 Use the AWS Apache Hive 3 23-12-2020 26-12-2020 First draft 9 26-12-2020 04-01-2021 Review with lecturer 3 04-01-2021 07-01-2021 Second draft 5 07-01-2021 12-01-2021 Submission 1 12-01-2021 13-01-2021 Conclusion and Further Work This study proposed the MetaCloudDataStorage security Architecture for securing the Big Data in Cloud Computing. The Map Reduce framework has been used to gain information regarding the number of users that were logged on into the cloud data center. It has suggested protecting the mapping of various data elements for each provider with the use of MetaCloudDataStorage security interface. The future work is the extension of the proposed MetaCloudDataStorage security Architecture for real-time processing for the streaming of data. Total word count: 3909? Acknowledgements and References "Cloud Security Alliance", Cloud Security Alliance, 2020. [Online]. Available: https://cloudsecurityalliance.org/articles/csa-releases-the-expanded-top-ten-big-data-security-privacy-challenges/. [Accessed: 11- Oct- 2020]. Abbasi, B.Z. and Shah, M.A., September. Fog computing: Security issues, solutions and robust practices. In 2017 23rd International Conference on Automation and Computing (ICAC) (pp. 1-6).IEEE, 2017. Ahmed, E.S.A. and Saeed, R.A. A survey of Big Data cloud computing security. International Journal of Computer Science and Software Engineering (IJCSSE), 3(1), pp.78-85, 2014.Ahmed, M. and Hossain, M.A. Cloud computing and security issues in the cloud. International Journal of Network Security & Its Applications, 6(1), p.25, 2014. Althagafy, E. and Qureshi, M.R.J., 2017. Novel cloud architecture to decrease problems related to big data. International Journal of Computer Network and Information Security, 9(2), p.53. Cheng, N., Lyu, F., Chen, J., Xu, W., Zhou, H., Zhang, S. and Shen, X., Big Data driven vehicular networks. IEEE Network, 32(6), pp.160-167 2018. Fernandes, D.A., Soares, L.F., Gomes, J.V., Freire, M.M. and Inácio, P.R. Security issues in cloud environments: a survey. International Journal of Information Security, 13(2), pp.113-170, 2014. Gai, K., Qiu, M., Zhao, H. and Xiong, J., June. Privacy-aware adaptive data encryption strategy of Big Data in Cloud Computing. In 2016 IEEE 3rd International Conference on Cyber Security and Cloud Computing (CSCloud) (pp. 273-278). IEEE, 2016 Gholami, A. and Laure, E., Big Data security and privacy issues in the cloud. International Journal of Network Security & Its Applications (IJNSA), Issue January, 2016. Khan, M.A., A survey of security issues for cloud computing. Journal of network and computer applications, 71, pp.11-29, 2016. Li, Y., Gai, K., Qiu, L., Qiu, M. and Zhao, H., Intelligent cryptography approach for secure distributed Big Data storage in Cloud Computing. Information Sciences, 387, pp.103-115, 2017 Satapathy, S.K., Moharana, S.K. and Ojha, A.K., Implication of Security Issues Associated With Big Data In Cloud Computing. International Journal of Recent Trends in Engineering & Research (IJRTER), 2(04), pp.2455-1457, 2016. Simsek, Z., Vaara, E., Paruchuri, S., Nadkarni, S. and Shaw, J.D., New ways of seeing Big Data 2019. Stergiou, C. and Psannis, K.E., Efficient and secure Big Data delivery in Cloud Computing. Multimedia Tools and Applications, 76(21), pp.22803-22822, 2017. Stergiou, C., Psannis, K.E., Kim, B.G. and Gupta, B., Secure integration of IoT and Cloud Computing. Future Generation Computer Systems, 78, pp.964-975 2018. Subramanian, N. and Jeyaraj, A., Recent security challenges in Cloud Computing. Computers & Electrical Engineering, 71, pp.28-42, 2018. Varghese, B. and Buyya, R., Next generation Cloud Computing: New trends and research directions. Future Generation Computer Systems, 79, pp.849-861, 2018. Yang, C., Huang, Q., Li, Z., Liu, K. and Hu, F., Big Data and Cloud Computing: innovation opportunities and challenges. International Journal of Digital Earth, 10(1), pp.13-53, 2017. Zhang, D., October. Big Data security and privacy protection. In 8th International Conference on Management and Computer Science (ICMCS 2018).Atlantis Press 2018.


Subject Name: Computer Science

Level: Undergraduate


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