Shelbi Joseph
Cochin University of Science and Technology

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Journal : Indonesian Journal of Electrical Engineering and Computer Science

A light weight encryption over big data in information stockpiling on cloud Uma Narayanan; Varghese Paul; Shelbi Joseph
Indonesian Journal of Electrical Engineering and Computer Science Vol 17, No 1: January 2020
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijeecs.v17.i1.pp389-397

Abstract

Data is growing exponentially in the fast Changing World of Information and Communications Technology. Information from sensors, cell phones, social networking sites, logical information and ventures all are adding to this gigantic blast in the information. One of the best mainstream utilities available for dealing with the colossal measure of data is the Hadoop community. Enterprises are progressively depending on Hadoop for preparing their essential information. In any case, Hadoop is still developing. There is much powerlessness found in Hadoop, which can scrutinize the security of the sensitive data that undertakings have entrusted on it. In this paper, security issues related to the system have been discussed. Besides, we have attempted to give a short overview of the currently accessible arrangements and their constraints. Towards the end, a novel strategy, which can be utilized to kill the detected vulnerabilities in the structure, has been introduced. In the cutting edge period, data security has moved towards becoming a basic need for every single person. Be that as it may, not every person can bear the cost of the specific circulations given by various sellers to their Hadoop group. This paper displays an effective system that anybody can use with their Hadoop to secure Data.
A novel approach to big data analysis using deep belief network for the detection of android malware Uma Narayanan; Varghese Paul; Shelbi Joseph
Indonesian Journal of Electrical Engineering and Computer Science Vol 16, No 3: December 2019
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijeecs.v16.i3.pp1447-1454

Abstract

Mobile and tablets are rapidly getting the chance to be basic device in the everyday life. Android has been the most well-known versatile working structure. Regardless, inferable from the open thought of Android, amount of malware is concealed in a broad number of kind applications in Android exhibits that really undermine Android security. Deep learning is another domain of AI explore that has expanded extending thought in artificial information. In this examination, we propose to relate the features from the static examination with features from the dynamic examination of Android applications and depict malware using Deep learning systems. What's more, besides distinguishing sensitive customer data sources is fundamental for security protection in portable applications. So we propose a Novel way to deal with overseeing tremendous information examination utilizing Deep learning for the affirmation of Android malware.