How the Graph Construction Technique Shapes Performance in IoT Botnet Detection
The increasing incidence of IoT-based botnet attacks has driven interest in advanced learning models for detection. Recent efforts have focused on leveraging attention mechanisms to model long-range feature dependencies and Graph Neural Networks (GNNs) to capture relationships between data instances. Since GNNs require graph-structured input, tabular NetFlow data must be transformed accordingly. This study evaluates how the choice of the method for constructing the graph-structured dataset impacts the classification performance of a GNN model. Five methods--k-Nearest Neighbors, Mutual Nearest Neighbors, Shared Nearest Neighbor, Gabriel Graph, and epsilon-radius Graph--were evaluated in th...
| Authors | Hassan Wasswa, Hussein Abbass, Timothy Lynar |
|---|---|
| Primary category | cs.NI |
| Published | 28 February 2026 |
| Last updated at source | 28 February 2026 |
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