  Data poisoning is one of the most relevant security threats against machine
learning and data-driven technologies. Since many applications rely on
untrusted training data, an attacker can easily craft malicious samples and
inject them into the training dataset to degrade the performance of machine
learning models. As recent work has shown, such Denial-of-Service (DoS) data
poisoning attacks are highly effective. To mitigate this threat, we propose a
new approach of detecting DoS poisoned instances. In comparison to related
work, we deviate from clustering and anomaly detection based approaches, which
often suffer from the curse of dimensionality and arbitrary anomaly threshold
selection. Rather, our defence is based on extracting information from the
training data in such a generalized manner that we can identify poisoned
samples based on the information present in the unpoisoned portion of the data.
We evaluate our defence against two DoS poisoning attacks and seven datasets,
and find that it reliably identifies poisoned instances. In comparison to
related work, our defence improves false positive / false negative rates by at
least 50%, often more.
