Risk assessment model of Internet of Things based on BP neural network algorithm

Weibin Yan

doi:10.1117/12.2674643

23 May 2023 Risk assessment model of Internet of Things based on BP neural network algorithm

Weibin Yan

Proceedings Volume 12604, International Conference on Computer Graphics, Artificial Intelligence, and Data Processing (ICCAID 2022); 126042I (2023) https://doi.org/10.1117/12.2674643
Event: 2nd International Conference on Computer Graphics, Artificial Intelligence, and Data Processing (ICCAID 2022), 2022, Guangzhou, China

Abstract

The internet of things (IOT) is a novel concept. With the rapid development of wireless communication technology, it has quickly become a hot topic. Its basic concept is to communicate, communicate and cooperate with each other around all kinds of things and objects around us through the unique address mode in the world, so as to achieve the common goal. Through in-depth research and investigation, it is found that at present, most banks in China generally adopt the scoring system for personal credit risk assessment (RBI), and the assessment indicators have not kept pace with the times for many years, and basically there is no big change. Obviously, this evaluation method is highly subjective, and the evaluation indicators are relatively backward, slightly rigid and single. In this regard, this paper studies the relatively mature evaluation criteria and methods in the field of information security, and analyzes the information security status of small IOT systems. Integrate the information security features of the IOT to construct a RBI index system for small IOT systems.

Citation Download Citation

Weibin Yan "Risk assessment model of Internet of Things based on BP neural network algorithm", Proc. SPIE 12604, International Conference on Computer Graphics, Artificial Intelligence, and Data Processing (ICCAID 2022), 126042I (23 May 2023); https://doi.org/10.1117/12.2674643

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KEYWORDS

Internet of things

Information security

Risk assessment

Neural networks

Machine learning

Data modeling

Education and training

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