Systems and methods for utilizing graph neural networks for scam website detection

A system for utilizing graph neural networks for scam website detection, the system comprising:

• at least one physical processor;
• physical memory comprising computer-executable instructions and one or more modules that, when executed by the physical processor, cause the physical processor to:
• create, by a dataset module, a dataset of target websites comprising unknown websites and known scam websites with corresponding scam categories;
• extract, by a construct module, a plurality of website constructs utilized for executing scam attacks from the dataset of target websites;
• build, by a graph module, a graph comprising a plurality of nodes for associating each of the target websites with one or more of the corresponding scam categories;
• group, by a grouping module and for each of the nodes, the target websites based on sharing a common construct within the plurality of website constructs;
• and
• perform, by a security module, a security action that trains a graph neural network (GNN) for identifying the unknown websites as potential scam websites based on a similarity score determined from the plurality of website constructs.

A non-transitory computer-readable medium comprising one or more computer-executable instructions that, when executed by at least one processor of a computing device, cause the computing device to:

• create a dataset of target websites comprising unknown websites and known scam websites with corresponding scam categories;
• extract a plurality of website constructs utilized for executing scam attacks from the dataset of target websites;
• build a graph comprising a plurality of nodes for associating each of the target websites with one or more of the corresponding scam categories;
• group, for each of the nodes, the target websites based on sharing a common construct within the plurality of website constructs;
• and
• perform a security action that trains a graph neural network (GNN) for identifying the unknown websites as potential scam websites based on a similarity score determined from the plurality of website constructs.