Enhancing security elements for MapReduce processing with whitelist
Big data requires new ways and technologies of how data is harnessed, managed and applied to create values that offer insights for better decision making. An exploration of MapReduce model which reliably accommodates big data processing requirements reveals that data traversing through nodes inside...
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| Main Authors: | , |
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| Format: | Conference or Workshop Item |
| Language: | English |
| Published: |
2017
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| Subjects: | |
| Online Access: | http://repo.uum.edu.my/22794/1/ICOCI%202016%2049-55.pdf http://repo.uum.edu.my/22794/ http://icoci.cms.net.my/PROCEEDINGS/2017/Pdf_Version_Chap01e/PID154-49-55e.pdf |
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| Summary: | Big data requires new ways and technologies of how data is harnessed, managed and applied to create values that offer insights for better decision making. An exploration of MapReduce model which reliably accommodates big data processing requirements reveals that data traversing through nodes inside clusters during processing are exposed to security and privacy breaches.Further examination identifies elements in security task
that impact MapReduce challenges.This paper concerns with the experimentation on how Whitelist access control element can enhance security
within MapReduce environment using Hadoop platform.Datasets are executed through series of Whitelist coding/scripts.The enhancement is measured on a basis of Whitelist capability and effectiveness of reducing False Positive Rate error in different scenarios, comparing different sizes of applied Whitelists, key strengths used for filtering and the execution time.The results yield reduced False Positive Rate for Whitelist, supporting claim of an enhanced security but the execution time have increased, indicating lower
overall performance. |
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