On Botnet Detection with Genetic Programming under Streaming Data, Label Budgets and Class Imbalance

Botnets represent a widely deployed framework for remotely infecting and controlling hundreds of networked computing devices for malicious ends. Traditionally detection of Botnets from network data using machine learning approaches is framed as an offline, supervised learning activity. However, in practice both normal behaviours and Botnet behaviours represent non-stationary processes in which there are continuous developments to both as new services/applications and malicious behaviours appear. This work formulates the task of Botnet detection as a streaming data task in which finite label budgets, class imbalance and incremental/online learning predominate. We demonstrate that effective Botnet detection is possible for label budgets as low as 0.5% when an active learning approach is adopted for genetic programming (GP) streaming data analysis. The full article appears as S. Khanchi et al., (2018) "On Botnet Detection with Genetic Programming under Streaming Data, Label Budgets and Class Imbalance" in Swarm and Evolutionary Computation, 39:139--140. https://doi.org/10.1016/j.swevo.2017.09.008