Enabling Fair ML Evaluations for Security
Published: Oct 15, 2018
Abstract
Machine learning is widely used in security research to classify malicious activity, ranging from malware to malicious URLs and network traffic. However, published performance numbers often seem to leave little room for improvement and, due to a wide range of datasets and configurations, cannot be used to directly compare alternative approaches; moreover, most evaluations have been found to suffer from experimental bias which positively inflates...
Paper Details
Title
Enabling Fair ML Evaluations for Security
Published Date
Oct 15, 2018
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