A geo-location and trust-based framework with community detection algorithms to filter attackers in 5G social networks

We propose a geographical location and trust-based framework combined with community detection algorithms to filter communities of malicious users in 5G social networks. This framework utilizes geo-location information, community trust within the network and AI community detection algorithms to identify users that can cause harm. It has a benefit over some other fake user detection mechanisms because it takes into account the characteristics that a malicious user cannot easily fake like the geographical location and community trust built throughout time. We illustrate the proposed framework on synthetic social network data. Results show this framework can distinguish potential malicious users from trustworthy users based on their location, trust, and structural attributes.