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FuzzBomb: Autonomous Cyber Vulnerability Detection and Repair

Authors:
David Musliner
Scott Friedman
Michael Boldt
Jay Benton
Max Schuchard
Peter Keller
Stephen McCamant

Keywords: autonomous cyber defense; symbolic analysis; protocol learning; binary rewriting

Abstract:
Beginning just over one year ago, Smart Information Flow Technologies (SIFT) and the University of Minnesota teamed up to create a fully autonomous Cyber Reasoning System (CRS) to compete in the Defense Advanced Research Projects Agency (DARPA) Cyber Grand Challenge (CGC). Starting from our prior work on autonomous cyber defense and symbolic analysis of binary programs, we developed numerous new components to create FUZZBOMB. In this paper, we outline several of the major advances we developed for FUZZBOMB, and review its performance in the first phase of the CGC competition.

Pages: 10 to 15

Copyright: Copyright (c) IARIA, 2015

Publication date: November 15, 2015

Published in: conference

ISSN: 2326-9286

ISBN: 978-1-61208-444-2

Location: Barcelona, Spain

Dates: from November 15, 2015 to November 20, 2015