  Machine learning has become an important component for many systems and
applications including computer vision, spam filtering, malware and network
intrusion detection, among others. Despite the capabilities of machine learning
algorithms to extract valuable information from data and produce accurate
predictions, it has been shown that these algorithms are vulnerable to attacks.
Data poisoning is one of the most relevant security threats against machine
learning systems, where attackers can subvert the learning process by injecting
malicious samples in the training data. Recent work in adversarial machine
learning has shown that the so-called optimal attack strategies can
successfully poison linear classifiers, degrading the performance of the system
dramatically after compromising a small fraction of the training dataset. In
this paper we propose a defence mechanism to mitigate the effect of these
optimal poisoning attacks based on outlier detection. We show empirically that
the adversarial examples generated by these attack strategies are quite
different from genuine points, as no detectability constrains are considered to
craft the attack. Hence, they can be detected with an appropriate pre-filtering
of the training dataset.
