45 citations · 54 across the 4 of their papers we have counts for
8 papers
Comment on Revisiting Neural Program Smoothing for Fuzzing
Dongdong She, Kexin Pei, Junfeng Yang +2
MLFuzz, a work accepted at ACM FSE 2023, revisits the performance of a machine learning-based fuzzer, NEUZZ. We demonstrate that its main conclusion is entirely wrong due to severa…
NeuDep: Neural Binary Memory Dependence Analysis
Kexin Pei, Dongdong She, Michael Wang +6
Determining whether multiple instructions can access the same memory location is a critical task in binary analysis. It is challenging as statically computing precise alias informa…
Effective Seed Scheduling for Fuzzing with Graph Centrality Analysis
Dongdong She, Abhishek Shah, Suman Jana
Seed scheduling, the order in which seeds are selected, can greatly affect the performance of a fuzzer. Existing approaches schedule seeds based on their historical mutation data,…
MTFuzz: Fuzzing with a Multi-Task Neural Network
Dongdong She, Rahul Krishna, Lu Yan +2
Fuzzing is a widely used technique for detecting software bugs and vulnerabilities. Most popular fuzzers generate new inputs using an evolutionary search to maximize code coverage.…
Fine Grained Dataflow Tracking with Proximal Gradients
Gabriel Ryan, Abhishek Shah, Dongdong She +2
Dataflow tracking with Dynamic Taint Analysis (DTA) is an important method in systems security with many applications, including exploit analysis, guided fuzzing, and side-channel…
Neutaint: Efficient Dynamic Taint Analysis with Neural Networks
Dongdong She, Yizheng Chen, Abhishek Shah +2
Dynamic taint analysis (DTA) is widely used by various applications to track information flow during runtime execution. Existing DTA techniques use rule-based taint-propagation, wh…