Skip to menu Skip to content Skip to footer
Associate Professor Guangdong Bai
Associate Professor

Guangdong Bai

Email: 
Phone: 
+61 7 336 51187

Overview

Background

Dr Guangdong Bai is an Associate Professor at School of Electrical Engineering and Computer Science, The University of Queensland, Australia. His research interests include Cyber Security, Software Engineering and Formal Methods.

He obtained his PhD degree from National University of Singapore, Singapore, and M.S. and B.S. degrees from Peking University, China.

Please visit his webpage for latest update: https://baigd.github.io/

Availability

Associate Professor Guangdong Bai is:
Available for supervision

Qualifications

  • Doctor of Philosophy, National University of Singapore

Research interests

  • Mobile Security, Software Security, Internet of Things (IoT) Security

  • Protocol Verification

  • Software Engineering

Works

Search Professor Guangdong Bai’s works on UQ eSpace

102 works between 2010 and 2024

1 - 20 of 102 works

2024

Conference Publication

Exploring ChatGPT App Ecosystem: Distribution, Deployment and Security

Yan, Chuan, Ren, Ruomai, Meng, Mark Huasong, Wan, Liuhuo, Ooi, Tian Yang and Bai, Guangdong (2024). Exploring ChatGPT App Ecosystem: Distribution, Deployment and Security. New York, NY, USA: ACM. doi: 10.1145/3691620.3695510

Exploring ChatGPT App Ecosystem: Distribution, Deployment and Security

2024

Conference Publication

Unveiling Intellectual Property Vulnerabilities of GAN-Based Distributed Machine Learning through Model Extraction Attacks

Ma, Mengyao, Liu, Shuofeng, M.A.P. Chamikara, , Baruwal Chhetri, Mohan and Bai, Guangdong (2024). Unveiling Intellectual Property Vulnerabilities of GAN-Based Distributed Machine Learning through Model Extraction Attacks. New York, NY, USA: ACM. doi: 10.1145/3627673.3679850

Unveiling Intellectual Property Vulnerabilities of GAN-Based Distributed Machine Learning through Model Extraction Attacks

2024

Conference Publication

Large Language Models Can Connect the Dots: Exploring Model Optimization Bugs with Domain Knowledge-Aware Prompts

Guan, Hao, Bai, Guangdong and Liu, Yepang (2024). Large Language Models Can Connect the Dots: Exploring Model Optimization Bugs with Domain Knowledge-Aware Prompts. New York, NY, USA: ACM. doi: 10.1145/3650212.3680383

Large Language Models Can Connect the Dots: Exploring Model Optimization Bugs with Domain Knowledge-Aware Prompts

2024

Journal Article

DeeBBAA: a benchmark deep black box adversarial attack against cyber-physical power systems

Bhattacharjee, Arnab, Bai, Guangdong, Tushar, Wayes, Verma, Ashu, Mishra, Sukumar and Saha, Tapan K. (2024). DeeBBAA: a benchmark deep black box adversarial attack against cyber-physical power systems. IEEE Internet of Things Journal, 1-1. doi: 10.1109/jiot.2024.3454257

DeeBBAA: a benchmark deep black box adversarial attack against cyber-physical power systems

2024

Journal Article

Investigating Documented Privacy Changes in Android OS

Yan, Chuan, Meng, Mark Huasong, Xie, Fuman and Bai, Guangdong (2024). Investigating Documented Privacy Changes in Android OS. Proceedings of the ACM on Software Engineering, 1 (FSE), 2701-2724. doi: 10.1145/3660826

Investigating Documented Privacy Changes in Android OS

2024

Conference Publication

Universal adversarial perturbations for vision-language pre-trained models

Zhang, Peng-Fei, Huang, Zi and Bai, Guangdong (2024). Universal adversarial perturbations for vision-language pre-trained models. 47th International ACM SIGIR Conference on Research and Development in Information Retrieval, Washington, DC, United States, 14-18 July 2024. New York, NY, United States: ACM. doi: 10.1145/3626772.3657781

Universal adversarial perturbations for vision-language pre-trained models

2024

Conference Publication

AuSSE: a novel framework for security and safety evaluation for autonomous vehicles

Nguyen, Nhung H., Cho, Jin-Hee, Moore, Terrence J., Yoon, Seunghyun, Lim, Hyuk, Nelson, Frederica, Bai, Guangdong and Kim, Dan Dongseong (2024). AuSSE: a novel framework for security and safety evaluation for autonomous vehicles. 2024 54th Annual IEEE/IFIP International Conference on Dependable Systems and Networks, Brisbane, QLD, Australia, 24-27 June 2024. Piscataway, NJ, United States: IEEE. doi: 10.1109/dsn-s60304.2024.00012

AuSSE: a novel framework for security and safety evaluation for autonomous vehicles

2024

Conference Publication

PANDA: Practical Adversarial Attack Against Network Intrusion Detection

Swain, Subrat Kumar, Kumar, Vireshwar, Bai, Guangdong and Kim, Dan Dongseong (2024). PANDA: Practical Adversarial Attack Against Network Intrusion Detection. 54th Annual IEEE/IFIP International Conference on Dependable Systems and Networks (DSN), Brisbane, QLD, Australia, 24-27 June 2024. Piscataway, NJ, United States: Institute of Electrical and Electronics Engineers. doi: 10.1109/dsn-s60304.2024.00017

PANDA: Practical Adversarial Attack Against Network Intrusion Detection

2024

Journal Article

Beyond Fidelity: Explaining Vulnerability Localization of Learning-Based Detectors

Cheng, Baijun, Zhao, Shengming, Wang, Kailong, Wang, Meizhen, Bai, Guangdong, Feng, Ruitao, Guo, Yao, Ma, Lei and Wang, Haoyu (2024). Beyond Fidelity: Explaining Vulnerability Localization of Learning-Based Detectors. ACM Transactions on Software Engineering and Methodology, 33 (5) 127, 1-33. doi: 10.1145/3641543

Beyond Fidelity: Explaining Vulnerability Localization of Learning-Based Detectors

2024

Journal Article

Tryptophan metabolism and piglet diarrhea: Where we stand and the challenges ahead

Zhao, Xuan, Pang, Jiaman, Zhang, Wanghong, Peng, Xie, Yang, Zhenguo, Bai, Guangdong and Xia, Yaoyao (2024). Tryptophan metabolism and piglet diarrhea: Where we stand and the challenges ahead. Animal Nutrition, 17, 123-133. doi: 10.1016/j.aninu.2024.03.005

Tryptophan metabolism and piglet diarrhea: Where we stand and the challenges ahead

2024

Conference Publication

CORELOCKER: neuron-level usage control

Wang, Zihan, Ma, Zhongkui, Feng, Xinguo, Sun, Ruoxi, Wang, Hu, Xue, Minhui and Bai, Guangdong (2024). CORELOCKER: neuron-level usage control. 2024 IEEE Symposium on Security and Privacy (SP), San Francisco, CA, United States, 19-23 May 2024. Piscataway, NJ, United States: IEEE. doi: 10.1109/sp54263.2024.00233

CORELOCKER: neuron-level usage control

2024

Conference Publication

Is it safe to share your files? An empirical security analysis of Google workspace

Wan, Liuhuo, Wang, Kailong, Wang, Haoyu and Bai, Guangdong (2024). Is it safe to share your files? An empirical security analysis of Google workspace. ACM Web Conference 2024, Singapore, Singapore, 13-17 May 2024. New York, NY, United States: ACM. doi: 10.1145/3589334.3645697

Is it safe to share your files? An empirical security analysis of Google workspace

2024

Conference Publication

Don't bite off more than you can chew: investigating excessive permission requests in trigger-action integrations

Wan, Liuhuo, Wang, Kailong, Mahadewa, Kulani, Wang, Haoyu and Bai, Guangdong (2024). Don't bite off more than you can chew: investigating excessive permission requests in trigger-action integrations. WWW '24: ACM Web Conference 2024, Singapore, Singapore, 13–17 May 2024. New York, NY, United States: ACM. doi: 10.1145/3589334.3645721

Don't bite off more than you can chew: investigating excessive permission requests in trigger-action integrations

2024

Conference Publication

Privacy-preserving and fairness-aware federated learning for critical infrastructure protection and resilience

Zhang, Yanjun, Sun, Ruoxi, Shen, Liyue, Bai, Guangdong, Xue, Minhui, Meng, Mark Huasong, Li, Xue, Ko, Ryan and Nepal, Surya (2024). Privacy-preserving and fairness-aware federated learning for critical infrastructure protection and resilience. WWW '24: ACM Web Conference 2024, Singapore, Singapore, 13-17 May 2024. New York, NY, United States: ACM. doi: 10.1145/3589334.3645545

Privacy-preserving and fairness-aware federated learning for critical infrastructure protection and resilience

2024

Conference Publication

Are your requests your true needs? Checking excessive data collection in VPA App

Xie, Fuman, Yan, Chuan, Meng, Mark Huasong, Teng, Shaoming, Zhang, Yanjun and Bai, Guangdong (2024). Are your requests your true needs? Checking excessive data collection in VPA App. ICSE '24: IEEE/ACM 46th International Conference on Software Engineering, Lisbon, Portugal, 14-20 April 2024. New York, NY, United States: ACM. doi: 10.1145/3597503.3639107

Are your requests your true needs? Checking excessive data collection in VPA App

2024

Conference Publication

Symbolic verification of mesh commissioning protocol of thread

Upadhyay, Pankaj, Sharma, Subodh and Bai, Guangdong (2024). Symbolic verification of mesh commissioning protocol of thread. 17th Innovations in Software Engineering Conference (ISEC), Bangalore, India, 22-24 February 2024. New York, NY United States: Association for Computing Machinery. doi: 10.1145/3641399.3641446

Symbolic verification of mesh commissioning protocol of thread

2024

Conference Publication

The Benefits of Non-Fungible Token (NFT) Technology in Music Copyright

Dong, Jie, Dong, Naipeng and Bai, Guangdong (2024). The Benefits of Non-Fungible Token (NFT) Technology in Music Copyright. 7th International Symposium on Distributed Ledger Technology SDLT 2023, Brisbane, QLD Australia, 30 November – 1 December 2023. Singapore: Springer. doi: 10.1007/978-981-97-0006-6_7

The Benefits of Non-Fungible Token (NFT) Technology in Music Copyright

2024

Journal Article

ReLU hull approximation

Ma, Zhongkui, Li, Jiaying and Bai, Guangdong (2024). ReLU hull approximation. Proceedings of the ACM on Programming Languages, 8 (POPL) ARTN 75, 2260-2287. doi: 10.1145/3632917

ReLU hull approximation

2024

Journal Article

Effective and Robust Adversarial Training Against Data and Label Corruptions

Zhang, Peng-Fei, Huang, Zi, Xu, Xin-Shun and Bai, Guangdong (2024). Effective and Robust Adversarial Training Against Data and Label Corruptions. IEEE Transactions on Multimedia, 26, 1-12. doi: 10.1109/tmm.2024.3394677

Effective and Robust Adversarial Training Against Data and Label Corruptions

2024

Book

Distributed Ledger Technology: 7th International Symposium, SDLT 2023, Brisbane, QLD, Australia, November 30 – December 1, 2023, Revised Selected Papers

Naipeng Dong, Babu Pillai, Guangdong Bai and Mark Utting eds. (2024). Distributed Ledger Technology: 7th International Symposium, SDLT 2023, Brisbane, QLD, Australia, November 30 – December 1, 2023, Revised Selected Papers. Communications in Computer and Information Science, Heidelberg, Germany: Springer. doi: 10.1007/978-981-97-0006-6

Distributed Ledger Technology: 7th International Symposium, SDLT 2023, Brisbane, QLD, Australia, November 30 – December 1, 2023, Revised Selected Papers

Funding

Current funding

  • 2024 - 2027
    Rigorous Privacy Compliance in Modern Application Ecosystems
    ARC Discovery Projects
    Open grant
  • 2023 - 2026
    Responsible modelling respecting privacy, data quality, and green computing
    ARC Discovery Projects
    Open grant
  • 2023 - 2027
    Secure and Ethical XR Based HSE Training for First Responders in Crisis Situations
    CSIRO
    Open grant
  • 2023 - 2026
    Directed and Incremental Analysis for DevSecOps
    Oracle Corporation Australia Pty Limited
    Open grant
  • 2020 - 2025
    Dynamic Analysis of Java-based Authentication Systems
    Oracle Labs
    Open grant

Past funding

  • 2022
    Quantitative analysis of Consensus Protocols
    SupraOracles
    Open grant
  • 2022
    User Privacy Protection for Credit Data on Mobile Devices
    Zhejiang E-Commerce Bank Co Ltd
    Open grant
  • 2021
    Development of quality assurance technologies for meat processing plants
    Innovation Connections
    Open grant
  • 2020 - 2023
    Massive static and real-time datasets for cyber security research and digital competitiveness
    Data 61 - University Collaboration Agreement (DUCA)
    Open grant

Supervision

Availability

Associate Professor Guangdong Bai is:
Available for supervision

Before you email them, read our advice on how to contact a supervisor.

Supervision history

Current supervision

  • Doctor Philosophy

    UQIDAR00265: Security Analytics of Internet of Things (IoT)

    Principal Advisor

  • Doctor Philosophy

    Unified Cyber Security Framework for Distributed Learning

    Principal Advisor

    Other advisors: Professor Ryan Ko

  • Doctor Philosophy

    Analysing Internet of Thing Applications with Big Data Technique

    Principal Advisor

  • Doctor Philosophy

    Nudge4Cyber: Framework for Designing Accessible and Effective Cyber Security Nudges

    Principal Advisor

  • Doctor Philosophy

    Robustness Verification of Neural Network

    Principal Advisor

    Other advisors: Dr Naipeng Dong

  • Doctor Philosophy

    Security of Internet of Things (IoT) Integration

    Principal Advisor

    Other advisors: Professor Ryan Ko

  • Doctor Philosophy

    Automation of cyber software security targeting on software and mobile analysis

    Principal Advisor

  • Doctor Philosophy

    IoT security analytics

    Principal Advisor

    Other advisors: Professor Ryan Ko, Dr Naipeng Dong

  • Doctor Philosophy

    Privacy attacks and defences in cross-cyber physical domains

    Principal Advisor

  • Doctor Philosophy

    Auditing Privacy Policy Compliance of IoT Applications

    Principal Advisor

  • Doctor Philosophy

    Multimodal Membership Inference: A Causal Perspective

    Principal Advisor

  • Doctor Philosophy

    Distributed data collection infrastructure for IT and OT networks for automated early warning detection

    Associate Advisor

    Other advisors: Dr Naipeng Dong, Professor Ryan Ko

  • Doctor Philosophy

    Distributed data collection infrastructure for IT and OT networks for automated early warning detection

    Associate Advisor

    Other advisors: Dr Naipeng Dong, Professor Ryan Ko

  • Doctor Philosophy

    Secure, Accountable and Provenance-Centric File System

    Associate Advisor

    Other advisors: Professor Ryan Ko

  • Doctor Philosophy

    AI based intrusion detection and response system

    Associate Advisor

    Other advisors: Associate Professor Dan Kim

  • Doctor Philosophy

    Adversarial AI Attacks and Defenses in Intrusion Detection System for IoT

    Associate Advisor

    Other advisors: Associate Professor Dan Kim

  • Doctor Philosophy

    Automated Penetration Testing using Advanced AI Techniques

    Associate Advisor

    Other advisors: Associate Professor Dan Kim

  • Doctor Philosophy

    Fairness in Graph Representation Learning Models

    Associate Advisor

    Other advisors: Professor Helen Huang

  • Doctor Philosophy

    Towards Privacy-Preserving and Fairness-Aware Federated Recommendation Systems

    Associate Advisor

    Other advisors: Dr Ruihong Qiu, Professor Helen Huang

  • Doctor Philosophy

    Continuous Code Analysis for Rapidly Evolving Software

    Associate Advisor

    Other advisors: Associate Professor Mark Utting, Dr Guowei Yang

  • Doctor Philosophy

    Analysis of Machine Learning Systems

    Associate Advisor

    Other advisors: Dr Naipeng Dong

  • Doctor Philosophy

    Adversarial AI Attacks and Defenses in Intrusion Detection System for IoT

    Associate Advisor

    Other advisors: Associate Professor Dan Kim

  • Doctor Philosophy

    Adaptive, secure and resilient programmable logic controllers (PLCs) and data transfer protocols

    Associate Advisor

    Other advisors: Professor Tapan Saha, Professor Ryan Ko

  • Doctor Philosophy

    A Distributed Data Collection Infrastructure For Automating Industrial Control Systems Security

    Associate Advisor

    Other advisors: Dr Naipeng Dong, Professor Ryan Ko

  • Doctor Philosophy

    A comprehensive framework for automated cybersecurity assessment, mitigation, and education using graphical security models

    Associate Advisor

    Other advisors: Associate Professor Dan Kim

  • Doctor Philosophy

    Exploring the Trustworthiness of Information Retrieval in the Era of Large Language Models

    Associate Advisor

    Other advisors: Dr Ruihong Qiu, Professor Helen Huang

  • Doctor Philosophy

    Developing inclusive and culturally sensitive design guidelines for AI-enabled smart homes for people with disabilities in developing countries, based on local needs, preferences, and values

    Associate Advisor

    Other advisors: Dr Dhaval Vyas

Completed supervision

Media

Enquiries

For media enquiries about Associate Professor Guangdong Bai's areas of expertise, story ideas and help finding experts, contact our Media team:

communications@uq.edu.au