Profit: Detecting and Quantifying Side Channels in Networked Applications

Abstract

We present a black-box, dynamic technique to detect and quantify side-channel information leaks in networked applications that communicate through a TLS-encrypted stream. Given a user-supplied profiling-input suite in which some aspect of the inputs is marked as secret, we run the application over the inputs and capture a collection of variable-length network packet traces. The captured traces give rise to a vast side-channel feature space, including the size and timestamp of each individual packet as well as their aggregations (such as total time, median size, etc.) over every possible subset of packets. Finding the features that leak the most information is a difficult problem.