Abstract
Abstract Reputation and trust-based models have been used extensively in different application domains. These include large online communities such as eBay, Amazon, YouTube and ad-hoc and wireless sensor networks. Recently, the use of the models has gained popularity due to their effectiveness in providing trusted systems or networks. These models focus on online and historical data to determine the reputation of domain members. In this paper, we propose a novel approach for obtaining trust values by focusing not only on online and historical data but also possible future scenarios to anticipate events in the next time intervals. The data-driven framework is able to dynamically obtain and inject data to predict the future trust value of every identity in the system. The advantage of this proactive approach compared to other approaches is that informed decisions about the domain can be made before a compromise occurs.
Sign-in/Register to access full text options 
Talk to us
Join us for a 30 min session where you can share your feedback and ask us any queries you have
Schedule a callDisclaimer: All third-party content on this website/platform is and will remain the property of their respective owners and is provided on "as is" basis without any warranties, express or implied. Use of third-party content does not indicate any affiliation, sponsorship with or endorsement by them. Any references to third-party content is to identify the corresponding services and shall be considered fair use under The CopyrightLaw.
