Guofei Gu

About

Guofei Gu is a Professor in the Department of Computer Science & Engineering at Texas A&M University, holding the Eppright Professor in Engineering title and serving as a Presidential Impact Fellow. He earned his Ph.D. in Computer Science from Georgia Institute of Technology in 2008, his M.S. in Computer Science from Fudan University in 2003, and his B.E. in Computer Science from Nanjing University of Posts and Telecommunications in 2000. His research interests encompass network and system security, including internet malware, botnet and APT detection, defense, and analysis, as well as software-defined programmable security in SDN, NFV, cloud, and edge environments. He also focuses on mobile and IoT security, AI security, web and social networking security, intrusion detection, and anomaly detection. Guofei Gu has received numerous awards and honors for his contributions, including the TEES Research Impact Award, Dean of Engineering Excellence Award, and the NSF CAREER Award, among others.

Research topics

• Computer Science
• Computer Security
• Computer network
• Operating system
• Distributed computing

Selected publications

• Poseidon: Mitigating Volumetric DDoS Attacks with Programmable Switches

  2020 · 219 citations

  Distributed Denial-of-Service (DDoS) attacks have become a critical threat to the Internet. Due to the increasing number of vulnerable Internet of Things (IoT) devices, attackers can easily compromise a large set of nodes and launch highvolume DDoS attacks from the botnets. State-of-the-art DDoS defenses, however, have not caught up with the fast development of the attacks. Middlebox-based defenses can achieve high performance with specialized hardware; however, these defenses incur a high cost,…

  DOI

• SODA: A Generic Online Detection Framework for Smart Contracts

  2020 · 115 citations

  DOI

• LLMs in Software Security: A Survey of Vulnerability Detection Techniques and Insights

  ACM Computing Surveys · 2025-09-23 · 22 citations

  reviewOpen access

  Large Language Models (LLMs) are emerging as transformative tools for software vulnerability detection. Traditional methods, including static and dynamic analysis, face limitations in efficiency, false-positive rates, and scalability with modern software complexity. Through code structure analysis, pattern identification, and repair suggestion generation, LLMs demonstrate a novel approach to vulnerability mitigation. This survey examines LLMs in vulnerability detection, analyzing problem formula…

  PublisherDOI

• Control Plane Reflection Attacks and Defenses in Software-Defined Networks

  IEEE/ACM Transactions on Networking · 2020 · 21 citations

  Software-Defined Networking (SDN) continues to be deployed spanning from enterprise data centers to cloud computing with the proliferation of various SDN-enabled hardware switches and dynamic control plane applications. However, state-of-the-art SDN-enabled hardware switches have rather limited downlink message processing capability, especially for Flow-Mod and Statistic Query, which may not suffice the huge need of dynamic control plane applications. In this paper, we systematically study the i…

  DOI

• Cerberus: Enabling Efficient and Effective In-Network Monitoring on Programmable Switches

  2024-05-19 · 8 citations

  articleSenior author

  With the increasing volume of network traffic and the emergence of new types of attacks, traditional network monitoring is facing significant challenges in ensuring network security and performance. In-network monitoring (INM) systems based on programmable switches, e.g., P4-based INM systems, have emerged as a more promising approach for high-performance and real-time network monitoring. However, existing P4-based INM systems have resource limitations in handling diverse and high-volume INM tas…

  PublisherDOI

Recent grants

• SaTC: CORE: Small: Adversarial Learning via Modeling Interpretation

  NSF · $500k · 2018–2023

• TWC: Medium: Collaborative: HIMALAYAS: Hierarchical Machine Learning Stack for Fine-Grained Analysis of Malware Domain Groups

  NSF · $250k · 2013–2018

• Collaborative Research: CICI: Secure and Resilient Architecture: S3D: A New SDN-Based Security Framework for the Science DMZ

  NSF · $350k · 2016–2021

Frequent coauthors

• Vinod Yegneswaran

  119 shared

• Phillip Porras

  SRI International

  118 shared

• Seungsoo Lee

  PricewaterhouseCoopers (South Korea)

  101 shared

• Jinwoo Kim

  Kwangwoon University

  100 shared

• Jae Hyun Nam

  University of Minnesota

  98 shared

• Seungwon Shin

  Hongik University

  98 shared

• Minjae Seo

  Gachon University

  98 shared

• Seungwon Shin

  Korea Advanced Institute of Science and Technology

  25 shared

Awards & honors

• TEES Research Impact Award, TAMU, 2017-2018
• Dean of Engineering Excellence Award, TAMU, 2017-2018
• College of Engineering Charles H. Barclay Jr. '45 Faculty Fe…
• Finalist (top 10) for CSAW 2016 Best Applied Security Paper…
• Best Paper Award, The 35th IEEE International Conference on…

Similar researchers at Texas A&M University

• Thomas R. Ioergerh-60
• Shuiwang Jih-57
• Jianer Chenh-49