This study presents a framework for detecting and mitigating fake and
potentially attacking user communities within 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. The framework incorporates an artificial control model
to select appropriate community detection algorithms and employs a
trust-based strategy to identify and filter out potential attackers. It
adapts its approach by utilizing user and attack requirement data
through the artificial conscience control model while considering the
dynamics of community trust within the network. What sets this framework
apart from other fake user detection mechanisms is its capacity to
consider attributes challenging for malicious users to mimic. These
attributes include the trust established within the community over time,
the geographical location, and the framework's adaptability to different
attack scenarios. To validate its efficacy, we apply the framework to
synthetic social network data, demonstrating its ability to distinguish
potential malicious users from trustworthy ones.