• I am a PHD candidate from Department of Computer Science at Rutgers University, advised by Prof. Shiqing Ma.
• My research interests are AI for system security and AI security. More specifically, I am interested in:
  • Collection, compression, storage and analysis of large-scale security data (e.g., provenance graphs and logs).
  • Rule/AI based intrusion detection and investigation.
  • AI security and explainable AI.
• I was a machine learning and AI research intern at Bell Lab where I focused on data mining on large-scale industry logs in Summer 2023. Before joining Rutgers, I obtained my B.E. from Central South University in 2020.

News

• [2024-04] One paper is accepted by ASPLOS 2024.
• [2023-09] I am invited as a reviewer of NeurIPS BUGs 2023, ICLR 2024, ICML 2024.
• [2023-06] I joined Bell Lab as a Machine Learning and AI intern (2023 summer).
• [2023-05] Two papers are accepted to Usenix Security 2023.
• [2023-04] I am invited as a reviewer of NeurIPS 2023.
• [2022-09] Two papers are accepted to NeurIPS 2022.
• [2022-01] I am invited as a reviewer of ICML 2022.
• [2022-01] I am a member of Usenix Security 2022 Artifact Evaluation Committee.
• [2021-09] One paper is accepted to Usenix Security 2021.

Publication

• Merlin: Multi-tier Optimization of eBPF Code for Performance and Compactness
  Jinsong Mao, Hailun Ding, Juan Zhai, Shiqing Ma
  Architectural Support for Programming Languages and Operating Systems 2024 (ASPLOS 2024)

• AirTag: Towards Automated Attack Investigation by Unsupervised Learning with Log Texts
  Hailun Ding, Juan Zhai, Yuhong Nan, Shiqing Ma
  USENIX Security Symposium 2023 (Usenix security 2023)

• The Case for Learned Provenance Graph Storage Systems
  Hailun Ding, Juan Zhai, Dong Deng, Shiqing Ma
  USENIX Security Symposium 2023 (Usenix security 2023)

• Rethinking the Reverse-engineering of Trojan Triggers
  Zhenting Wang, Kai Mei, Hailun Ding, Juan Zhai, Shiqing Ma
  Proceedings of Neural Information Processing Systems 2022 (NeurIPS 2022)

• Training with More Confidence: Mitigating Injected and Natural Backdoors During Training
  Zhenting Wang, Hailun Ding, Juan Zhai, Shiqing Ma
  Proceedings of Neural Information Processing Systems 2022 (NeurIPS 2022)

• ELISE: A Storage Efficient Logging System Powered by Redundancy Reduction and Representation Learning
  Hailun Ding, Shenao Yan, Juan Zhai, Shiqing Ma
  USENIX Security Symposium 2021 (Usenix security 2021)

• Procedural Learning With Robust Visual Features via Low Rank Prior
  Haifeng Li, Li Chen, Hailun Ding, Qi Li, Bingyu Sun, Guohua Wu
  IEEE Access 2019

• A Data-driven Adversarial Examples Recognition Framework via Adversarial Feature Genome
  Li Chen, Hailun Ding, Qi Li, Jiawei Zhu, Jian Peng, Haifeng Li
  Arxiv 2019

• Understanding the Importance of Single Directions via Representative Substitution
  Li Chen, Hailun Ding, Qi Li, Zhuo Li, Jian Peng, Haifeng Li
  AAAI 2019 Workshop on Network Interpretability for Deep Learning**

Service

• Reviewer, TIFS
• SubReviewer, CODASPY 2024
• Reviewer, ICLR 2024
• Reviewer, NeurIPS Workshop on Backdoors in Deep Learning: The Good, the Bad, and the Ugly (BUGS) 2023
• Reviewer, NeurIPS 2023
• Reviewer, International Conference on Machine Learning (ICML), 2022, 2024
• Artifact Evaluation Committee, USENIX Security Symposium, 2022

Award

• National Scholarship (2017, 2019, 0.2% of Chinese undergraduate students)
• First Level Scholarship of Central South University (2017, 2018, 2019, Top 3%)
• Diamond Scholarship (2017, 15 person in 30,000 undergraduate students)
• First Prize in The Hack, Hack Shanghai (2018)