부산대학교 도서관

Jasleen Kaur,1 Asif Irshad Khan,2 Yoosef B Abushark,2 Md Mottahir Alam,3 Suhel Ahmad Khan,4 Alka Agrawal,1 Rajeev Kumar,1 Raees Ahmad Khan1 1Department of Information Technology, Babasaheb Bhimrao Ambedkar University, Lucknow, UP, India; 2Computer Science Department, Faculty of Computing and Information Technology, King Abdulaziz University, Jeddah, Saudi Arabia; 3Department of Electrical & Computer Engineering, Faculty of Engineering, King Abdulaziz University, Jeddah, Saudi Arabia; 4Department of Computer Science, Indira Gandhi National TribalUniversity, Amarkantak, MP, IndiaCorrespondence: Rajeev Kumar Email rs0414@gmail.comIntroduction: The imperative need for ensuring optimal security of healthcare web applications cannot be overstated. Security practitioners are consistently working at improvising on techniques to maximise security along with the longevity of healthcare web applications. In this league, it has been observed that assessment of security risks through soft computing techniques during the development of web application can enhance the security of healthcare web applications to a great extent.Methods: This study proposes the identification of security risks and their assessment during the development of the web application through adaptive neuro-fuzzy inference system (ANFIS). In this article, firstly, the security risk factors involved during healthcare web application development have been identified. Thereafter, these security risks have been evaluated by using the ANFIS technique. This research also proposes a fuzzy regression model.Results: The results have been compared with those of ANFIS, and the ANFIS model is found to be more acceptable for the estimation of security risks during the healthcare web application development.Conclusion: The proposed approach can be applied by the healthcare web application developers and experts to avoid the security risk factors during healthcare web application development for enhancing the healthcare data security.Keywords: healthcare web application, security risk assessment, fuzzy systems, neural network, adaptive neuro-fuzzy inference system