Publications

2025

1. USENIX Security

   

   Robustifying ML-powered Network Classifiers with PANTS

   Minhao Jin ,  and  Maria Apostolaki

   In 34rd USENIX Security Symposium (USENIX Security 25) , 2025

   Abs PDF Code

   Multiple network management tasks, from resource allocation to intrusion detection, rely on some form of ML-based network traffic classification (MNC). Despite their potential, MNCs are vulnerable to adversarial inputs, which can lead to outages, poor decision-making, and security violations, among other issues. The goal of this paper is to help network operators assess and enhance the robustness of their MNC against adversarial inputs. The most critical step for this is generating inputs that can fool the MNC while being realizable under various threat models. Compared to other ML models, finding adversarial inputs against MNCs is more challenging due to the existence of non-differentiable components e.g., traffic engineering and the need to constrain inputs to preserve semantics and ensure reliability. These factors prevent the direct use of well-established gradient-based methods developed in adversarial ML (AML). To address these challenges, we introduce PANTS, a practical white-box framework that uniquely integrates AML techniques with Satisfiability Modulo Theories (SMT) solvers to generate adversarial inputs for MNCs. We also embed PANTS into an iterative adversarial training process that enhances the robustness of MNCs against adversarial inputs. PANTS is 70% and 2x more likely in median to find adversarial inputs against target MNCs compared to state-of-the-art baselines, namely Amoeba and BAP. PANTS improves the robustness of the target MNCs by 52.7% (even against attackers outside of what is considered during robustification) without sacrificing their accuracy.

2023

1. MS Thesis

   

   Improving Managed Network Services Using Cooperative Synthetic Data Augmentation

   Minhao Jin

   May 2023

   Abs PDF

   https://www.proquest.com/docview/2759307887?pq-origsite=gscholar&fromopenview=true&sourcetype=Dissertations%20&%20Theses

2022

1. SIGCOMM

   

   Practical GAN-Based Synthetic IP Header Trace Generation Using NetShare

   Yucheng Yin ,  Zinan Lin ,  Minhao Jin , and 2 more authors

   In Proceedings of the ACM SIGCOMM 2022 Conference , May 2022

   Abs PDF Code

   We explore the feasibility of using Generative Adversarial Networks (GANs) to automatically learn generative models to generate synthetic packet- and flow header traces for networking tasks (e.g., telemetry, anomaly detection, provisioning). We identify key fidelity, scalability, and privacy challenges and tradeoffs in existing GAN-based approaches. By synthesizing domain-specific insights with recent advances in machine learning and privacy, we identify design choices to tackle these challenges. Building on these insights, we develop an end-to-end framework, NetShare. We evaluate NetShare on six diverse packet header traces and find that: (1) across all distributional metrics and traces, it achieves 46% more accuracy than baselines and (2) it meets users’ requirements of downstream tasks in evaluating accuracy and rank ordering of candidate approaches.

2021

1. IJCARS

   

   Towards accurate and interpretable surgical skill assessment: a video-based method for skill score prediction and guiding feedback generation

   Tianyu Wang ,  Minhao Jin ,  and  Mian Li

   International Journal of Computer Assisted Radiology and Surgery, Sep 2021

   Abs

   Recently, automatic surgical skill assessment has received the attention given the increasingly important role of surgical training. The assessment usually involves skill score prediction and further feedback generation. Existing work on skill score prediction is limited with several challenges and deserves more promising outcomes. For the feedback, most work identifies the flaws on the granularity of video frames or clips. It thus remains to be explored how to identify poorly performed gestures (segments) and further how to provide good references for improvement.

2020

1. SAC

   

   Towards a Data-Driven Method for RGB Video-Based Hand Action Quality Assessment in Real Time

   Tianyu Wang ,  Minhao Jin ,  Jingying Wang , and 2 more authors

   In Proceedings of the 35th Annual ACM Symposium on Applied Computing , Sep 2020

   Abs PDF

   In recent years, the research community has begun to explore Video-Based Action Quality Assessment on Human Body (VB-AQA), while few work focuses on Video-Based Action Quality Assessment on Human Hand (VH-AQA) yet. The current work on VB-AQA fails to deal with the inconsistency between captured features and the reality due to the changing angles of the camera, leaving a huge gap between VB-AQA and VH-AQA, while the computational efficiency is another critical problem. In this paper, a novel data-driven method for real-time VH-AQA is proposed. Features are formulated as spatio-temporal hand poses in this method and extracted via four steps: hand segmentation, 2D hand pose estimation, 3D hand pose estimation and hand pose organization. Based on the extracted features an assessment model is applied to evaluate the performance of actions and indicate the most promising adjustment as the feedback. We demonstrate the evaluation accuracy and computational efficiency of our method using our own Origami Video Dataset. For the latter, two new metrics are designed. It turns out that our method provides opportunities for real-time digital reconstruction of physical world activities and timely assessment.