Structural Node Representation Learning for Detecting Botnet Nodes

Private consumers, small businesses, and even large enterprises are all more at risk from botnets. These botnets are known for spearheading Distributed Denial-Of-Service (DDoS) attacks, spamming large populations of users, and causing critical harm to major organizations. The development of Internet-of-Things (IoT) devices led to the use of these devices for cryptocurrency mining, in transit data interception, and sending logs containing private data to the master botnet. Different techniques have been developed to identify these botnet activities, but only a few use Graph Neural Networks (GNNs) to analyze host activity by representing their communications with a directed graph. Although GNNs are intended to extract structural graph properties, they risk to cause overfitting, which leads to failure when attempting to do so from an unidentified network. In this study, we test the notion that structural graph patterns might be used for efficient botnet detection. In this study, we also present SIR-GN, a structural iterative representation learning methodology for graph nodes. Our approach is built to work well with untested data, and our model is able to provide a vector representation for every node that captures its structural information. Finally, we demonstrate that, when the collection of node representation vectors is incorporated into a neural network classifier, our model outperforms the state-of-the-art GNN based algorithms in the detection of bot nodes within unknown networks.