Jared Chandler (Tufts University)

Reverse engineering message formats from static network traces is a difficult and time consuming security task, critical for a variety of purposes: bug-finding via fuzz testing, automatic exploit generation, understanding the communications of hostile systems, and recovering specifications that are proprietary or have been lost. In this talk we describe our experiences evaluating BinaryInferno, a tool for automatically reverse engineering binary message formats from network traces. We discuss considerations for selecting protocols to evaluate, determining message format ground truth, and assembling representative datasets. Two issues we examine are the availability of real-world captures for malware protocols, and the need to validate that individual protocol messages actually conform to their ground truth specifications. We detail the engineering aspects of comparing BinaryInferno against related tools, the issues which arose, and how we address them. We examine different evaluation metrics and their tradeoffs as related to uncovering unknown message formats. We discuss how we handled the different representations of message format produced by each related tool. Finally, we conclude with a set of recommendations for future experiments involving protocol reverse engineering.

Speaker’s Biography

Jared Chandler is a PhD candidate studying Computer Science at Tufts University. His research focuses on computer security with an emphasis on automatic methods to reverse engineer unknown binary protocols, human computer interaction, and cyber deception.

View More Papers

PPA: Preference Profiling Attack Against Federated Learning

Chunyi Zhou (Nanjing University of Science and Technology), Yansong Gao (Nanjing University of Science and Technology), Anmin Fu (Nanjing University of Science and Technology), Kai Chen (Chinese Academy of Science), Zhiyang Dai (Nanjing University of Science and Technology), Zhi Zhang (CSIRO's Data61), Minhui Xue (CSIRO's Data61), Yuqing Zhang (University of Chinese Academy of Science)

Read More

PISE: Protocol Inference using Symbolic Execution and Automata Learning

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

Read More

Parakeet: Practical Key Transparency for End-to-End Encrypted Messaging

Harjasleen Malvai (UIUC/IC3), Lefteris Kokoris-Kogias (IST Austria), Alberto Sonnino (Mysten Labs), Esha Ghosh (Microsoft Research), Ercan Oztürk (Meta), Kevin Lewi (Meta), Sean Lawlor (Meta)

Read More

Augmented Reality’s Potential for Identifying and Mitigating Home Privacy...

Stefany Cruz (Northwestern University), Logan Danek (Northwestern University), Shinan Liu (University of Chicago), Christopher Kraemer (Georgia Institute of Technology), Zixin Wang (Zhejiang University), Nick Feamster (University of Chicago), Danny Yuxing Huang (New York University), Yaxing Yao (University of Maryland), Josiah Hester (Georgia Institute of Technology)

Read More