Please use this identifier to cite or link to this item: http://nopr.niscair.res.in/handle/123456789/57475
Title: Cybersecurity by Prediction of Time Synchronization using Bayesian Base Gradient Descent Approach
Authors: Arunachalam, Amutha
Seetharaman, K
Agarwal, Ashish
Keywords: Big data;Cyber-physical systems;Indian standard time;Time dissemination;Timestamp
Issue Date: Apr-2021
Publisher: NISCAIR-CSIR, India
Abstract: Time Commerce tends to struggle, which necessities an improved time framework.Legal escalations for conflicts of time commerce in the digital economy demand a solution that helps to address technology, standards, and policies. To meet the demand, we have to build a system that can understand every domain essential for building an inter-organizational system. "Date" and "Timestamp" reflect the root of the current term "Date Trade" in the cyber world. The threat to these roots has been studied in-depth and proposed solutions specific to UTC NPLI. The electricity grid shifts to the energy network to improve operating efficiency and reliability by developing advanced information and communication technology. However, the Internet also provides a range of entry points dependent on the internet, which produce additional vulnerabilities due to malicious cyber-attacks, thereby threatening Nations' economic health. This paper proposes therefore a new mechanism to protect critical infrastructure against these malicious attacks, based on interval state predictors. This paper uses the prediction-based approach for reducing the impact of such attacks from cyberspace. In prediction, we have used a machine learning approach like Bayesian classifier by Bayesian approach to forecasting time synchronization concerning universal time clock (UTC). In our analysis, we have taken the basic UTC, UTC, and UTC likelihood proposed approach on basis of communication. This work has improved considerably the results to take care of CPS against such cybersecurity threats.
Page(s): 347-353
URI: http://nopr.niscair.res.in/handle/123456789/57475
ISSN: 0975-1084 (Online); 0022-4456 (Print)
Appears in Collections:JSIR Vol.80(04) [April 2021]

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