To Explore Dynamic Misuse-ability Score using Machine Learning Model
A.V. S. Asha1, M. Srihari Varma2
1A.V.S.Asha, Department of Computer Science and Engineering S.R.K.R, Engineering College, Bhimavaram, India.
2M. Srihari Varma Department of computer Science and Engineering S.R.K.R, Engineering, College, Bhimavaram, India.
Manuscript received on 24 August 2019. | Revised Manuscript received on 09 September 2019. | Manuscript published on 30 September 2019. | PP: 4013-4017 | Volume-8 Issue-11, September 2019. | Retrieval Number: K21540981119/2019©BEIESP | DOI: 10.35940/ijitee.K2154.0981119
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© The Authors. Blue Eyes Intelligence Engineering and Sciences Publication (BEIESP). This is an open access article under the CC-BY-NC-ND license (http://creativecommons.org/licenses/by-nc-nd/4.0/)
Abstract: Digital behavior change interventions change the inner variations of humans based on discussions international experts relates to different domains publish their data to outsourced users. User’s access data from outsourced organization then organization follow basic state space representation to give data to users. This state space representation helps to users to guide and authorizing to improve measurement of security for users to release their data. So that in this paper we present novel concept i.e. Mis-usability weight measure for estimating risk factor in exploration from digital sources of data to insiders. This theory helps to generate score which representssensitivity of data exposed to users by predict ability of malicious exploits user’s data. Main challenge behind Mis-usability weight measure calculation is acquiring knowledge from different domain experts. Experimental results give better and efficient risk assessment results for different users in digital interventions.
Keywords: Digital behavior interventions, outsourced users data, misusability, security measures, data leakage.
Scope of the Article: Machine Learning