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AI Control for Trust-based Detection of Attackers in 5G Social Networks
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  • Arjan Durresi,
  • Davinder Kaur,
  • Suleyman Uslu,
  • Mimoza Durresi
Arjan Durresi
Indiana University Purdue University Indianapolis

Corresponding Author:[email protected]

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Davinder Kaur
Indiana University Purdue University Indianapolis
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Suleyman Uslu
Indiana University Purdue University Indianapolis
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Mimoza Durresi
Universiteti Europian i Tiranes
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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.
03 Oct 2023Submitted to Computational Intelligence
03 Oct 2023Assigned to Editor
03 Oct 2023Submission Checks Completed
03 Oct 2023Review(s) Completed, Editorial Evaluation Pending
19 Oct 2023Reviewer(s) Assigned
27 Oct 2023Editorial Decision: Revise Minor