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.
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.
We propose Angora, a new mutation-based fuzzer that outperforms the state-of-the-art fuzzers by a wide margin. We define a measurable objective, branch coverage, and design several key techniques towards this objective, such as scalable, efficient byte-level taint tracking, context-sensitive branch count, and selection of conditional statements that are easier to fuzz.
Yet another AFL instrumentation tool implemented by Intel Pin. [code]