Machine learning systems are vulnerable to data poisoning, a
coordinated attack where a fraction of the training dataset is manipulated
by an attacker to subvert learning. In this paper we first formulate an optimal attack strategy against online learning classifiers to assess worst-case
scenarios. We also propose two defence mechanisms to mitigate the effect
of online poisoning attacks by analysing the impact of the data points in
the classifier and by means of an adaptive combination of machine learning classifiers with different learning rates. Our experimental evaluation
supports the usefulness of our proposed defences to mitigate the effect of
poisoning attacks in online learning settings.