Collaborative Botnet Detection With Partial Communication Graph Information

Botnets have long been used for malicious purposes with huge economic costs to the society. With the proliferation of cheap but non-secure Internet-of-Things (IoT) devices generating large amounts of data, the potential for damage from botnets has increased manifold. There are several approaches to detect bots or botnets, though many traditional techniques are becoming less effective as botnets with centralized command & control structure are being replaced by peer-to-peer (P2P) botnets which are harder to detect. Several algorithms have been proposed in literature that use graph analysis or machine learning techniques to detect the overlay structure of P2P networks in communication graphs. Many of these algorithms however, depend on the availability of a universal communication graph or a communication graph aggregated from several ISPs, which is not likely to be available in reality. In real world deployments, significant gaps in communication graphs are expected and any solution proposed should be able to work with partial information. In this paper, we analyze the effectiveness of some community detection algorithms in detecting P2P botnets, especially with partial information. We show that the approach can work with only about half of the nodes reporting their communication graphs, with only small increase in detection errors.