Affiliation:
1. Department of Computer Science Indiana University Purdue University Indianapolis Indiana USA
2. Department of IT, Mathematics and Statistics European University of Tirana Tirana Albania
Abstract
AbstractThis study introduces a comprehensive framework designed for detecting and mitigating fake and potentially threatening user communities within 5G social networks. Leveraging geo‐location data, community trust dynamics, and AI‐driven community detection algorithms, this framework aims to pinpoint users posing potential harm. Including an artificial control model facilitates the selection of suitable community detection algorithms, coupled with a trust‐based strategy to effectively identify and filter potential attackers. A distinctive feature of this framework lies in its ability to consider attributes that prove challenging for malicious users to emulate, such as the established trust within the community, geographical location, and adaptability to diverse attack scenarios. To validate its efficacy, we illustrate the framework using synthetic social network data, demonstrating its ability to distinguish potential malicious users from trustworthy ones.
Subject
Artificial Intelligence,Computational Mathematics
Cited by
2 articles.
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