Ron Marcovich, Orna Grumberg, Gabi Nakibly (Technion, Israel Institute of Technology)

protocol from a binary code that implements it. This process is useful in cases such as extraction of the command and control protocol of a malware, uncovering security vulnerabilities in a network protocol implementation or verifying conformance to the protocol’s standard. Protocol inference usually involves time-consuming work to manually reverse engineer the binary code.

We present a novel method to automatically infer state machine of a network protocol and its message formats directly from the binary code. To the best of our knowledge, this is the first method to achieve this solely based on a binary code of a single peer. We do not assume any of the following: access to a remote peer, access to captures of the protocol’s traffic, and prior knowledge of message formats. The method leverages extensions to symbolic execution and novel modifications to automata learning. We validate the proposed method by inferring real-world protocols including the C&C protocol of Gh0st RAT, a well-known malware

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Lightning Community Shout-Outs to:

(1) Jonathan Petit, Secure ML Performance Benchmark (Qualcomm) (2) David Balenson, The Road to Future Automotive Research Datasets: PIVOT Project and Community Workshop (USC Information Sciences Institute) (3) Jeremy Daily, CyberX Challenge Events (Colorado State University) (4) Mert D. Pesé, DETROIT: Data Collection, Translation and Sharing for Rapid Vehicular App Development (Clemson University) (5) Ning…

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A Robust Counting Sketch for Data Plane Intrusion Detection

Sian Kim (Ewha Womans University), Changhun Jung (Ewha Womans University), RhongHo Jang (Wayne State University), David Mohaisen (University of Central Florida), DaeHun Nyang (Ewha Womans University)

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HeteroScore: Evaluating and Mitigating Cloud Security Threats Brought by...

Chongzhou Fang (University of California, Davis), Najmeh Nazari (University of California, Davis), Behnam Omidi (George Mason University), Han Wang (Temple University), Aditya Puri (Foothill High School, Pleasanton, CA), Manish Arora (LearnDesk, Inc.), Setareh Rafatirad (University of California, Davis), Houman Homayoun (University of California, Davis), Khaled N. Khasawneh (George Mason University)

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