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IJSTR >> Volume 8 - Issue 10, October 2019 Edition



International Journal of Scientific & Technology Research  
International Journal of Scientific & Technology Research

Website: http://www.ijstr.org

ISSN 2277-8616



Pigeonhole Malicious Attack On Big Data Using Automated Distributed Stochastic Neighbor Embedding (T-Sne)

[Full Text]

 

AUTHOR(S)

M. Prabhavathy, Dr. S. Uma Maheswari

 

KEYWORDS

Malware, Big Data, Security, t-SNE, Classification

 

ABSTRACT

The IT scene has changed so significantly as of late. The steady updates of new server/endpoint virtualization, distributed computing, cell phone and BYOD bolster, Internet of Things, and now the advent of new ways to deal with gadget the board like Unified Endpoint Management – implies expanding difficulties for associations to scale up their digital security foundation. This persistent selection of new advances adds further vulnerability and intricacy to the security foundation. Be that as it may, associations should be proactive and try different things with Big Data security investigation projects and activities to stay aware of the most recent advances. Slowing down out presently is certain to result in an expanded danger of a noteworthy security break.In this paper (t-SNE) t-Distributed Stochastic Neighbor Embedding taken to classify the high dimensional data to identify the malicious information. The proposed technique implies on the pool of data on automated the t-SNE) t-Distributed Stochastic Neighbor Embedding to find the unwanted data attacks. The experimental shows the true positive classification of malicious at 94.5% and false positive of 0.7%.

 

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