mTrust: Call Behavioral Trust Predictive Analytics Using Unsupervised Learning in Mobile Cloud Computing
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mTrust: Call Behavioral Trust Predictive Analytics Using Unsupervised Learning in Mobile Cloud Computing Arka Bhowmik1 · Debashis De2 Accepted: 29 October 2020 © Springer Science+Business Media, LLC, part of Springer Nature 2020
Abstract Filtering spam voice calls are a still major challenge in today’s technology contrary to SMS or email-based spamming. A numerical measure of the trust between users can help us filter calls based on relevance. Given the abundance of user-generated information available from the huge number of online devices, we can harness the power of this data to develop software adapting to user behavior. Existing research works for trust computation face various challenges when it comes to global applicability and understandability of trust values. Our investigation includes detailed surveillance of user call patterns based on the call data available from mobile devices and proposes a novel approach to filter calls that are of higher relevance to users based on their call-trust values. Our implementation realizes the diversity in call patterns of different people due to varying usage and uses classification and clustering algorithms to generate personalized, accurate numerical, and categorical trust values for every user. Categorical trust makes it easier to apply and understand trust ratings on a global scale. The implementation also incorporates a cloud facility to crowdsource trust values from multiple users, in a single database to generate the global trust of a user which can be used for spam filtering on a global scale. A software named “mTrust” is developed in this work for the future generation of a trustworthy mobile cloud network. Keywords Trust · Unsupervised learning · K-Means · Mobile cloud computing · Android application · Human behavior
* Debashis De [email protected] Arka Bhowmik [email protected] 1
Department of Computer Science and Engineering, Delhi Technological University, New Delhi, Main Bawana Road, New Delhi 110042, India
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Centre of Mobile Cloud Computing, Department of Computer Science and Engineering, Maulana Abul Kalam Azad University of Technology, B.F.‑142, Salt Lake, Sector‑1, Kolkata, West Bengal 700064, India
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A. Bhowmik, D. De
1 Introduction Trust is a measure of reliability and strength of a relationship. Although it is challenging to perfectly measure a person’s trust in another person, with a numerical value, a user’s behavior towards another person may provide us an inference on a value of trust between two individuals. The trust value may be calculated and defined within a given fixed range like 0–1; where 0 indicates low trust and 1. The process of computation and application of trust can be redefined for different scenarios of usage, for example, call-based trust in mobile networks, social-trust in microblogging websites, and document-trust [1] for determining document content relevance. However, all applications serve one common purpose, which is to determine the genuineness of the subject. Recent resea
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