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IOT-NFC Controlled Remote Access Security and an Exploration through Machine Learning

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dc.contributor.author Khan, Md. Abbas Ali
dc.contributor.author Hanif Ali, Mohammad
dc.contributor.author Haque, A.K.M Fazlul
dc.contributor.author Sharmin, Farah
dc.contributor.author Jabiullah, Md. Ismail
dc.date.accessioned 2021-11-29T04:13:14Z
dc.date.available 2021-11-29T04:13:14Z
dc.date.issued 2020-12-25
dc.identifier.uri http://dspace.daffodilvarsity.edu.bd:8080/handle/123456789/6487
dc.description.abstract Internet of Things (IOT) is a system that allows to connect the computing devices without the help of human-to-human or human-to-computer interaction. This paper proposes an app-based remote access control door lock security system (RACDLS) along with a short-range wireless communication naming Near Field Communication (NFC). The RACDLS system generates two sides' authentication systems rather than one side authentication like conventional systems. In the conventional system, users which are registered, can enter the premises only. In the proposed RACDLS system, users require permission from the room owner either they are registered or unregistered. Moreover, for maintaining the integrity and the confidentiality of data, a cryptographic technique (e.g.) we consider computational 512 bits hash function. In contrast, apply AES-192 for encrypting the hashed data. In addition, machine learning (ML) shows the performance of the employee activities including prediction with model accuracy. A definitive objective of this paper is to ensure the security of remote access control as well as allow notification of both ends, accessibility, usability, and permissibility of a personnel to enter the premises. en_US
dc.language.iso en_US en_US
dc.publisher 2020 18th International Conference on ICT and Knowledge Engineering (ICT&KE), IEEE en_US
dc.subject Cryptography en_US
dc.subject NFC en_US
dc.subject Security en_US
dc.subject NFC device en_US
dc.subject NFC tag en_US
dc.subject IOT en_US
dc.title IOT-NFC Controlled Remote Access Security and an Exploration through Machine Learning en_US
dc.type Article en_US


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