  Data poisoning is a type of adversarial attack on training data where an
attacker manipulates a fraction of data to degrade the performance of machine
learning model. Therefore, applications that rely on external data-sources for
training data are at a significantly higher risk. There are several known
defensive mechanisms that can help in mitigating the threat from such attacks.
For example, data sanitization is a popular defensive mechanism wherein the
learner rejects those data points that are sufficiently far from the set of
training instances. Prior work on data poisoning defense primarily focused on
offline setting, wherein all the data is assumed to be available for analysis.
Defensive measures for online learning, where data points arrive sequentially,
have not garnered similar interest.
  In this work, we propose a defense mechanism to minimize the degradation
caused by the poisoned training data on a learner's model in an online setup.
Our proposed method utilizes an influence function which is a classic technique
in robust statistics. Further, we supplement it with the existing data
sanitization methods for filtering out some of the poisoned data points. We
study the effectiveness of our defense mechanism on multiple datasets and
across multiple attack strategies against an online learner.
