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Angora: efficient fuzzing by principled search

Oct 2016 ─ Present

We propose Angora, a new mutation-based fuzzer that outperforms the state-of-the-art fuzzers by a wide margin. The main goal of Angora is to increase branch coverage by solving path constraints without symbolic execution. To solve path constraints efficiently, we introduce several key techniques: scalable byte-level taint tracking, context-sensitive branch count, search based on gradient descent, and input length exploration.

Security analysis of unmanned aerial vehicles

Mar 2015 ─ Sep 2015

We studied the risks of UAVs and conducted an empirical analysis of three popular DJI UAVs. We discovered a series of vulnerabilities, including insecure communication channels, misuse of cryptography, and insecure UAV activation and developer authorization.

Source code author deanonymization

Nov 2015 ─ Jan 2016

In this work, we explored neural network based approaches (Recurrent neural network) towards the source code author deanonymization problem. With a dataset extracted from Google Code Jam, our char-level model performs competitively on normal size dataset comparing to previous state-of-art work.