@inproceedings{74a5ef8dc1914c45bec115c9cd65f9ce,
title = "User Feedback based Trust Analysis in Cloud Computing using Machine Learning Approach",
abstract = "Trust on cloud services is a serious concern regardless of the increasing deployment and utilization of mission critical applications, which continues to obstruct users from complete implementation over cloud platforms. End-users need to be confident of different service parameters on the cloud deployment. Available solutions for trust modelling are either outdated, lack several details or are specific to limited cloud structures. This work presents neural network based approach to classify trust based on customer feedback on any cloud platform to improve selection and adoption of cloud computing resources. The results of the evaluation show that the proposed model offers reliable results for classifying trust of any cloud services based on the feedback of existing customers. This work further propose a generic way that can be used to preprocess multi attributed dataset of trust management and it confirms that consumer feedback is a worthy way to review the overall reliability.",
keywords = "Cloud Computing, Multi Attribute Trust, Neural Network, QoS, User Feedback",
author = "\{Ali Raza\}, Syed and Asma Kanwal and Nair, \{Harish K.G.\} and \{Ishaq Khan\}, Atif and Iftikhar Naseer and Muhammad Amjad and Karamath Ateeq and \{Al Sakhnani\}, Mahmoud",
note = "Publisher Copyright: {\textcopyright} 2023 IEEE.; 2nd International Conference on Business Analytics for Technology and Security, ICBATS 2023 ; Conference date: 07-03-2023 Through 08-03-2023",
year = "2023",
doi = "10.1109/ICBATS57792.2023.10111217",
language = "English",
series = "2nd International Conference on Business Analytics for Technology and Security, ICBATS 2023",
publisher = "Institute of Electrical and Electronics Engineers Inc.",
booktitle = "2nd International Conference on Business Analytics for Technology and Security, ICBATS 2023",
address = "United States",
}