Threat Artificial Intelligence and Cyber Security in Health Care Institutions

• Autor(a) principal: Fernandes, Ana
• Data de Publicação: 2021
• Outros Autores: Figueiredo, Margarida, Carvalho, Filomena, Neves, José, Vicente, Henrique
• Tipo de documento: Artigo
• Idioma: eng
• Título da fonte: Repositório Científico de Acesso Aberto de Portugal (Repositórios Cientìficos)
• Texto Completo: http://hdl.handle.net/10174/30026; https://doi.org/10.1007/978-3-030-72236-4_13
• topic: Threat Artificial Intelligence; Chaos; Entropy; Security; Logic Programming; Knowledge Representation and Reasoning; Artificial Neural Networks

In this work we go beyond what is called unsupervised learning, a decision- -making method that results in large numbers of false positives and negatives. The study was carried out in cryopreservation laboratories and aims to gain access to the General Data Protection Regulation (GDPR) implementation. Indeed, on the one hand, using Threat Artificial Intelligence, Chaos, Entropy and Security (TAICE&S) based methodology for problem solving one may mimic behaviors that are similar to the best human analysts. With the entry into force of the GDPR in the health institutions of the European Union (EU), stronger rules (TAICE based) on data protection (Security) mean people have more control over their personal data and businesses benefit from a level playing field. To respond to this challenge, a workable tool had to be built exploring the dynamics between TAICE&S and Logic Programming for Knowledge Representation and Reasoning, leading to the implementation of an agency based on TAICE/Cyber Security based techniques for problem solving, which is consistent with an Artificial Neural Network approach to problem definition. It is therefore possible to provide a full-bodied TAICE method to assist in threat identification and evaluation, activity prediction, mitigation, and response strategies. Using TAI procedures, one may identify patterns and matches in the activity of threat players, that combined with the issues of Chaos and Entropy gives us an opportunity to mimic how qualified specialists react in scenarios where models break off.