Improving Automated Cybersecurity by Generalizing Faults and Quantifying Patch Performance

Keywords: cyber defense, adaptive security, security metrics, filter generation.

Abstract: We are developing the FUZZBUSTER system to automatically identify software vulnerabilities and create adaptations that shield or repair those vulnerabilities before attackers can exploit them. FUZZBUSTER’s goal is to minimize the time that vulnerabilities exist, while also preserving software functionality as much as possible. This paper presents new adaptive cybersecurity tools that we have integrated into FUZZBUSTER, as well as new metrics that FUZZBUSTER uses to assess their performance. FUZZBUSTER’s new tools increase the efficiency of its diagnosis and adaptation operations, they produce more general, accurate adaptations, and they effectively preserve software functionality. We present FUZZBUSTER’s analysis of 16 fault-injected commandline binaries and six previously known bugs in the Apache web server. We compare FUZZBUSTER’s results for different adaptation strategies and tool settings, to characterize their benefits.

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