Wang Pengfei

Wang Pengfei

Associate Professor of Computer Security

National University of Defense Technology

Biography

Wang Pengfei is an associate professor of computer security at the Department of Network and Cyber Security, National University of Defense Technology. His research interests include system security, program analysis, vulnerability detection, and fuzzing test. He is looking for self-motivated students for collaborations on computer security research.

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Interests
  • System Security
  • Program Analysis
  • Vulnerability Detection
  • Fuzzing Test.
Education
  • PhD in Computer Science and Technology, 2018

    National University of Defense Technology

  • Visiting Scholar, 2016

    University College London

Recent News

[2024-6] Our paper “Efficiently Rebuilding Coverage in Hardware-Assisted Greybox Fuzzing” has been accepted by RAID 2024.

[2024-4] Our paper “HyperGo: Probability-based directed hybrid fuzzing” has been accepted by Computer & Security.

[2024-2] Our paper “ARMOR: Protecting Software Against Hardware Tracing Techniques” has been accepted by IEEE TIFS.

[2024-1] Our paper “INSTILLER: Towards Efficient and Realistic RTL Fuzzing” has been accepted by IEEE TCAD.

[2023-11] Our paper “The Progress, Challenges, and Perspectives of Directed Greybox Fuzzing” has been accepted by STVR.

[2023-09] Our paper “DeepGo: Predictive Directed Greybox Fuzzing” has been accepted by NDSS 2024.

Experience

 
 
 
 
 
Research Service
Reviewer
Jan 2018 – Present
  • ACM TOSEM 2024
  • IEEE TIFS 2023
  • Computer & Security 2022
  • Journal of Supercomputing 2024
  • Journal of Neurocomputing 2024
  • Scientific Report 2024
  • Software Quality Journal 2018/2021

Publications

(2024). Efficiently Rebuilding Coverage in Hardware-Assisted Greybox Fuzzing. RAID 2024.

PDF

(2024). HyperGo: Probability-based directed hybrid fuzzing. COSE.

PDF DOI

(2024). ARMOR: Protecting Software Against Hardware Tracing Techniques. IEEE TIFS.

PDF DOI

(2024). Instiller: Towards Efficient and Realistic RTL Fuzzing. IEEE TCAD.

PDF DOI

(2023). DeepGo: Predictive Directed Greybox Fuzzing. NDSS 2024.

PDF

(2023). The progress, challenges, and perspectives of directed greybox fuzzing. STVR.

PDF DOI

(2022). VulHawk: Cross-architecture Vulnerability Detection with Entropy-based Binary Code Search. NDSS 2023.

PDF

(2022). From Release to Rebirth: Exploiting Thanos Objects in Linux Kernel. IEEE TIFS.

PDF DOI

(2022). UltraFuzz: Towards Resource-saving in Distributed Fuzzing. IEEE TSE.

PDF DOI

(2022). MobFuzz: Adaptive Multi-objective Optimization in Gray-box Fuzzing. NDSS 2022.

PDF Slides

(2021). MEBS: Uncovering Memory Life-Cycle Bugs in Operating System Kernels. JCST.

PDF DOI

(2021). ARGUS: Assessing Unpatched Vulnerable Devices on the Internet via Efficient Firmware Recognition. AsiaCCS 2021.

PDF DOI

(2021). HashMTI: Scalable Mutation-based Taint Inference with Hash Records. SANER 2021.

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(2020). EcoFuzz: Adaptive Energy-Saving Greybox Fuzzing as a Variant of the Adversarial Multi-Armed Bandit. USENIX Security ‘20.

PDF Slides

(2019). Poster: Fuzzing IoT Firmware via Multi-stage Message Generation. CCS ‘19.

PDF DOI

(2018). DFTinker: Detecting and Fixing Double-fetch Bugs in an Automated Way. WASA 2018.

PDF DOI

(2018). A Survey of the Double-Fetch Vulnerabilities. CCPE.

PDF DOI

(2017). How Double-Fetch Situations turn into Double-Fetch Vulnerabilities: A Study of Double Fetches in the Linux Kernel. USENIX Security ‘17.

PDF Slides

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