By Topic

Hardening adversarial prediction with anomaly tracking

Sign In

Cookies must be enabled to login.After enabling cookies , please use refresh or reload or ctrl+f5 on the browser for the login options.

Formats Non-Member Member
$33 $13
Learn how you can qualify for the best price for this item!
Become an IEEE Member or Subscribe to
IEEE Xplore for exclusive pricing!
close button

puzzle piece

IEEE membership options for an individual and IEEE Xplore subscriptions for an organization offer the most affordable access to essential journal articles, conference papers, standards, eBooks, and eLearning courses.

Learn more about:

IEEE membership

IEEE Xplore subscriptions

2 Author(s)
M. A. J. Bourassa ; Department of Mathematics and Computer Science, Royal Military College of Canada, Canada ; D. B. Skillicorn

Predictors are often regarded as black boxes that treat all incoming records exactly the same, regardless of whether or not they resemble those from which the predictor was built. This is inappropriate, especially in adversarial settings where rare but unusual records are of critical importance and some records might occur because of deliberate attempts to subvert the entire process. We suggest that any predictor can, and should, be hardened by including three extra functions that watch for different forms of anomaly: input records that are unlike those previously seen (novel records); records that imply that the predictor is not accurately modelling reality (interesting records); and trends in predictor behavior that imply that reality is changing and the predictor should be updated. Detecting such anomalies prevents silent poor predictions, and allows for responses, such as: human intervention, using a variant process for some records, or triggering a predictor update.

Published in:

Intelligence and Security Informatics, 2009. ISI '09. IEEE International Conference on

Date of Conference:

8-11 June 2009