Journal article Open Access

Exploration of Big Data Security Framework using Machine Learning

K. Rajeshkumar; S. Dhanasekaran; V. Vasudevan

Sponsor(s)
Blue Eyes Intelligence Engineering and Sciences Publication(BEIESP)

As with prior technological advancements, big data technology is growing at present and we have to identify what are the possible threats to overhead the present security systems. Due to the development of recent technical environment like cloud, network connected smartphones and the omnipresent digital conversion of huge volume of all types of data poses more possible threats to sensitive data. Due to the improved vulnerability big data requires increased responsibility. During the last two years, the amount of data that has been created is about 90% of the whole data created. Strengthening the security of sensitive data from unauthorized discovery is the most challenging process in all kind of data processing. Data Leakage Detection offers a set of methods and techniques that can professionally solve the problem arising in particular critical data. The large amounts of existing data is mostly unstructured. To retrieve meaningful information, we have to develop superior analytical method in big data. At present we have more algorithms for security which are not easy to be implement for huge volume of data. We have to protect the sensitive information as well as details related users with the help of security protocols in big data. The sensitive data of the patient, different types of code patterns and set of attributes to be secured by using machine learning tool. Machine learning tools have a lot of library functions to protect the sensitive information about the clients. We recommend the Secure Pattern-Based Data Sensitivity Framework (PBDSF), to protect such sensitive information from big data using Machine Learning. In the proposed framework, HDFS is implemented to analysis the big data, to classify most important information and converting the sensitive data in a secure manner.

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