30 citations · 66 across the 7 of their papers we have counts for
7 papers
DeepGo: Predictive Directed Greybox Fuzzing
Peihong Lin, Pengfei Wang, Xu Zhou +3
The state-of-the-art DGF techniques redefine and optimize the fitness metric to reach the target sites precisely and quickly. However, optimizations for fitness metrics are mainly…
INSTILLER: Towards Efficient and Realistic RTL Fuzzing
Gen Zhang, Pengfei Wang, Tai Yue +3
Bugs exist in hardware, such as CPU. Unlike software bugs, these hardware bugs need to be detected before deployment. Previous fuzzing work in CPU bug detection has several disadva…
MobFuzz: Adaptive Multi-objective Optimization in Gray-box Fuzzing
Gen Zhang, Pengfei Wang, Tai Yue +4
Coverage-guided gray-box fuzzing (CGF) is an efficient software testing technique. There are usually multiple objectives to optimize in CGF. However, existing CGF methods cannot su…
HyperGo: Probability-based Directed Hybrid Fuzzing
Peihong Lin, Pengfei Wang, Xu Zhou +3
Directed grey-box fuzzing (DGF) is a target-guided fuzzing intended for testing specific targets (e.g., the potential buggy code). Despite numerous techniques proposed to enhance d…
CoG: a Two-View Co-training Framework for Defending Adversarial Attacks on Graph
Xugang Wu, Huijun Wu, Xu Zhou +1
Graph neural networks exhibit remarkable performance in graph data analysis. However, the robustness of GNN models remains a challenge. As a result, they are not reliable enough to…
UltraFuzz: Towards Resource-saving in Distributed Fuzzing
Xu Zhou, Pengfei Wang, Chenyifan Liu +6
Recent research has sought to improve fuzzing performance via parallel computing. However, researchers focus on improving efficiency while ignoring the increasing cost of testing r…
The Progress, Challenges, and Perspectives of Directed Greybox Fuzzing
Pengfei Wang, Xu Zhou, Tai Yue +3
Greybox fuzzing is a scalable and practical approach for software testing. Most greybox fuzzing tools are coverage-guided as reaching high code coverage is more likely to find bugs…