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Found 2 papers, 2 papers with code
Learning Type Inference for Enhanced Dataflow Analysis
Comparing our approach against recent neural type inference systems, our model outperforms the current state-of-the-art by 7. 85% on the ManyTypes4TypeScript benchmark, achieving 71. 27% accuracy overall.
Ranked #1 on
Type prediction
on ManyTypes4TypeScript
BaseSAFE: Baseband SAnitized Fuzzing through Emulation
In this paper, we discuss a novel cellular fuzzing framework that aims to help security researchers find critical bugs in cellular basebands and similar embedded systems.
Cryptography and Security
