Nicolás Rosner (University of California, Santa Barbara), Ismet Burak Kadron (University of California, Santa Barbara), Lucas Bang (Harvey Mudd College), Tevfik Bultan (University of California, Santa Barbara)

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.

Our approach addresses this problem in three steps: 1) Global analysis of traces for their alignment and identification of emph{phases} across traces; 2) Feature extraction using the identified phases; 3) Information leakage quantification and ranking of features via estimation of probability distribution.

We embody this approach in a tool called Profit and experimentally evaluate it on a benchmark of applications from the DARPA STAC program, which were developed to assess the effectiveness of side-channel analysis techniques. Our experimental results demonstrate that, given suitable profiling-input suites, Profit is successful in automatically detecting information-leaking features in applications, and correctly ordering the strength of the leakage for differently-leaking variants of the same application.

View More Papers

IoTGuard: Dynamic Enforcement of Security and Safety Policy in...

Z. Berkay Celik (Penn State University), Gang Tan (Penn State University), Patrick McDaniel (Penn State University)

Read More

PeriScope: An Effective Probing and Fuzzing Framework for the...

Dokyung Song (University of California, Irvine), Felicitas Hetzelt (Technical University of Berlin), Dipanjan Das (University of California, Santa Barbara), Chad Spensky (University of California, Santa Barbara), Yeoul Na (University of California, Irvine), Stijn Volckaert (University of California, Irvine and KU Leuven), Giovanni Vigna (University of California, Santa Barbara), Christopher Kruegel (University of California, Santa Barbara),…

Read More

ML-Leaks: Model and Data Independent Membership Inference Attacks and...

Ahmed Salem (CISPA Helmholtz Center for Information Security), Yang Zhang (CISPA Helmholtz Center for Information Security), Mathias Humbert (Swiss Data Science Center, ETH Zurich/EPFL), Pascal Berrang (CISPA Helmholtz Center for Information Security), Mario Fritz (CISPA Helmholtz Center for Information Security), Michael Backes (CISPA Helmholtz Center for Information Security)

Read More

Fine-Grained and Controlled Rewriting in Blockchains: Chameleon-Hashing Gone Attribute-Based

David Derler (DFINITY), Kai Samelin (TÜV Rheinland i-sec GmbH), Daniel Slamanig (AIT Austrian Institute of Technology), Christoph Striecks (AIT Austrian Institute of Technology)

Read More