  Machine learning is becoming ubiquitous. From finance to medicine, machine
learning models are boosting decision-making processes and even outperforming
humans in some tasks. This huge progress in terms of prediction quality does
not however find a counterpart in the security of such models and corresponding
predictions, where perturbations of fractions of the training set (poisoning)
can seriously undermine the model accuracy. Research on poisoning attacks and
defenses received increasing attention in the last decade, leading to several
promising solutions aiming to increase the robustness of machine learning.
Among them, ensemble-based defenses, where different models are trained on
portions of the training set and their predictions are then aggregated, provide
strong theoretical guarantees at the price of a linear overhead. Surprisingly,
ensemble-based defenses, which do not pose any restrictions on the base model,
have not been applied to increase the robustness of random forest models. The
work in this paper aims to fill in this gap by designing and implementing a
novel hash-based ensemble approach that protects random forest against
untargeted, random poisoning attacks. An extensive experimental evaluation
measures the performance of our approach against a variety of attacks, as well
as its sustainability in terms of resource consumption and performance, and
compares it with a traditional monolithic model based on random forest. A final
discussion presents our main findings and compares our approach with existing
poisoning defenses targeting random forests.
