arXiv subjects

Phuong Cao

Publications and source records attributed to Phuong Cao.

11 recordsLinked to original sources

arXiv2026

Network Impact of Post-Quantum Certificate Chain sizes on Time to First Byte in TLS Deployments

Post-Quantum Cryptography (PQC) is a rapidly growing deployment challenge as cryptographically relevant quantum computers (CRQC) continue to advance, leaving traditional cryptographic algorithms used in X.509 vulnerable to attack. However, PQC introduces significant deployment challenges in real-world networks, with handshake sizes increasing from 5x to over 20x compared to classical algorithms. In this work, we evaluate the time to first byte (TTFB) under CDN-focused TLS conditions to characterize the latency cost of transitioning existing internet infrastructure to quantum-safe certificate schemes. We observe discrete increases in TTFB as certificate chain sizes exceed transport layer data flight limits. To isolate the impact of certificate chains, we evaluate both ECDSA and ML-DSA-based certificate schemes, generating similarly sized certificate chains through controlled addition of certificate extensions. We additionally examine how CDN properties such as session resumption, certificate size optimizations, and geographical distribution reduce latency penalties. We utilize Zeek-monitored TLS traffic through a High-Performance Computing System (NCSA) with terabyte network connectivity across the nation to quantify real-world session resumption rates. We compare CDN-driven size optimization with Merkle Tree Certificates (MTC) to examine how size reductions allow certificate chains to remain under the flight limit threshold. We find that MTC allows for 2x-3x increase in supportable certificate chain size, whereas CDN-based optimizations yield more limited reductions, supporting up to approximately 1.6x certificate chain size increase.

cs.CR↗

arXiv2026

On the Extreme Variance of Certified Local Robustness Across Model Seeds

Robustness verification of neural networks, referring to formally proving that neural networks satisfy robustness properties, is of crucial importance in safety-critical applications, where model failures can result in loss of human life or million-dollar damages. However, the dependability of verification results may be questioned due to sources of randomness in machine learning, and although this has been widely investigated for accuracy, its impact on robustness verification remains unknown. In this paper, we demonstrate a concerning result: Models that differ only in random seeds during training exhibit extreme variance in their certified robustness, with a standard deviation that is statistically larger than the marginal robustness improvements reported in recent machine learning papers. In addition, we also show that certified robustness generalization to unseen data varies significantly across datasets, falling short of the dependability expectations for safety-critical tasks. Our findings are major concerns because: (i) machine learning results in certified robustness are likely unconvincing due to extreme variance in certified robustness, and (ii) a ``lucky'' model seed in a test set cannot be guaranteed to maintain its higher certified robustness under a different test set. In light of these results, we urge researchers to increase the reporting of confidence intervals for certified robustness, and we urge those verifying neural networks to be more comprehensive in verification by using large-scale, diverse, and unseen data.

cs.LG↗

arXiv2025

Story of Two GPUs: Characterizing the Resilience of Hopper H100 and Ampere A100 GPUs

This study characterizes GPU resilience in Delta, a large-scale AI system that consists of 1,056 A100 and H100 GPUs, with over 1,300 petaflops of peak throughput. We used 2.5 years of operational data (11.7 million GPU hours) on GPU errors. Our major findings include: (i) H100 GPU memory resilience is worse than A100 GPU memory, with 3.2x lower per-GPU MTBE for memory errors, (ii) The GPU memory error-recovery mechanisms on H100 GPUs are insufficient to handle the increased memory capacity, (iii) H100 GPUs demonstrate significantly improved GPU hardware resilience over A100 GPUs with respect to critical hardware components, (iv) GPU errors on both A100 and H100 GPUs frequently result in job failures due to the lack of robust recovery mechanisms at the application level, and (v) We project the impact of GPU node availability on larger-scales and find that significant overprovisioning of 5% is necessary to handle GPU failures.

cs.DC↗

arXiv2025

Generative Active Adaptation for Drifting and Imbalanced Network Intrusion Detection

Machine learning has shown promise in network intrusion detection systems, yet its performance often degrades due to concept drift and imbalanced data. These challenges are compounded by the labor-intensive process of labeling network traffic, especially when dealing with evolving and rare attack types, which makes preparing the right data for adaptation difficult. To address these issues, we propose a generative active adaptation framework that minimizes labeling effort while enhancing model robustness. Our approach employs density-aware dataset prior selection to identify the most informative samples for annotation, and leverages deep generative models to conditionally synthesize diverse samples, thereby augmenting the training set and mitigating the effects of concept drift. We evaluate our end-to-end framework \NetGuard on both simulated IDS data and a real-world ISP dataset, demonstrating significant improvements in intrusion detection performance. Our method boosts the overall F1-score from 0.60 (without adaptation) to 0.86. Rare attacks such as Infiltration, Web Attack, and FTP-BruteForce, which originally achieved F1 scores of 0.001, 0.04, and 0.00, improve to 0.30, 0.50, and 0.71, respectively, with generative active adaptation in the CIC-IDS 2018 dataset. Our framework effectively enhances rare attack detection while reducing labeling costs, making it a scalable and practical solution for intrusion detection.

cs.NI↗

arXiv2025

Building Machine Learning Challenges for Anomaly Detection in Science

Scientific discoveries are often made by finding a pattern or object that was not predicted by the known rules of science. Oftentimes, these anomalous events or objects that do not conform to the norms are an indication that the rules of science governing the data are incomplete, and something new needs to be present to explain these unexpected outliers. The challenge of finding anomalies can be confounding since it requires codifying a complete knowledge of the known scientific behaviors and then projecting these known behaviors on the data to look for deviations. When utilizing machine learning, this presents a particular challenge since we require that the model not only understands scientific data perfectly but also recognizes when the data is inconsistent and out of the scope of its trained behavior. In this paper, we present three datasets aimed at developing machine learning-based anomaly detection for disparate scientific domains covering astrophysics, genomics, and polar science. We present the different datasets along with a scheme to make machine learning challenges around the three datasets findable, accessible, interoperable, and reusable (FAIR). Furthermore, we present an approach that generalizes to future machine learning challenges, enabling the possibility of large, more compute-intensive challenges that can ultimately lead to scientific discovery.

cs.LG↗

arXiv2024

Security Testbed for Preempting Attacks against Supercomputing Infrastructure

Securing HPC has a unique threat model. Untrusted, malicious code exploiting the concentrated computing power may exert an outsized impact on the shared, open-networked environment in HPC, unlike well-isolated VM tenants in public clouds. Therefore, preempting attacks targeting supercomputing systems before damage remains the top security priority. The main challenge is that noisy attack attempts and unreliable alerts often mask \emph{real attacks}, causing permanent damages such as system integrity violations and data breaches. This paper describes a security testbed embedded in live traffic of a supercomputer at the National Center for Supercomputing Applications (NCSA). The objective is to demonstrate attack \textit{preemption}, i.e., stopping system compromise and data breaches at petascale supercomputers. Deployment of our testbed at NCSA enables the following key contributions: 1) Insights from characterizing unique \textit{attack patterns} found in real security logs of more than 200 security incidents curated in the past two decades at NCSA. 2) Deployment of an attack visualization tool to illustrate the challenges of identifying real attacks in HPC environments and to support security operators in interactive attack analyses. 3) Demonstrate the utility of the testbed by running novel models, such as Factor-Graph-based models, to preempt a real-world ransomware family.

cs.CR↗

arXiv2024

Jupyter Notebook Attacks Taxonomy: Ransomware, Data Exfiltration, and Security Misconfiguration

Open-science collaboration using Jupyter Notebooks may expose expensively trained AI models, high-performance computing resources, and training data to security vulnerabilities, such as unauthorized access, accidental deletion, or misuse. The ubiquitous deployments of Jupyter Notebooks (~11 million public notebooks on Github have transformed collaborative scientific computing by enabling reproducible research. Jupyter is the main HPC's science gateway interface between AI researchers and supercomputers at academic institutions, such as the National Center for Supercomputing Applications (NCSA), national labs, and the industry. An impactful attack targeting Jupyter could disrupt scientific missions and business operations. This paper describes the network-based attack taxonomy of Jupyter Notebooks, such as ransomware, data exfiltration, security misconfiguration, and resource abuse for cryptocurrency mining. The open nature of Jupyter (direct data access, arbitrary code execution in multiple programming languages kernels) and its vast attack interface (terminal, file browser, untrusted cells) also attract attacks attempting to misuse supercomputing resources and steal state-of-the-art research artifacts. Jupyter uses encrypted datagrams of rapidly evolving WebSocket protocols that challenge even the most state-of-the-art network observability tools, such as Zeek. We envisage even more sophisticated AI-driven attacks can be adapted to target Jupyter, where defenders have limited visibility. In addition, Jupyter's cryptographic design should be adapted to resist emerging quantum threats. On balance, this is the first paper to systematically describe the threat model against Jupyter Notebooks and lay out the design of auditing Jupyter to have better visibility against such attacks.

cs.CR↗

arXiv2024

Dependable Classical-Quantum Computer Systems Engineering

Quantum Computing (QC) offers the potential to enhance traditional High-Performance Computing (HPC) workloads by leveraging the unique properties of quantum computers, leading to the emergence of a new paradigm: HPC-QC. While this integration presents new opportunities, it also brings novel challenges, particularly in ensuring the dependability of such hybrid systems. This paper aims to identify integration challenges, anticipate failures, and foster a diverse co-design for HPC-QC systems by bringing together QC, cloud computing, HPC, and network security. The focus of this emerging inter-disciplinary effort is to develop engineering principles that ensure the dependability of hybrid systems, aiming for a more prescriptive co-design cycle. Our framework will help to prevent design pitfalls and accelerate the maturation of the QC technology ecosystem. Key aspects include building resilient HPC-QC systems, analyzing the applicability of conventional techniques to the quantum domain, and exploring the complexity of scaling in such hybrid systems. This underscores the need for performance-reliability metrics specific to this new computational paradigm.

quant-ph↗

arXiv2024

Post-Quantum Cryptography (PQC) Network Instrument: Measuring PQC Adoption Rates and Identifying Migration Pathways

The problem of adopting quantum-resistant cryptographic network protocols or post-quantum cryptography (PQC) is critically important to democratizing quantum computing. The problem is urgent because practical quantum computers will break classical encryption in the next few decades. Past encrypted data has already been collected and can be decrypted in the near future. The main challenges of adopting post-quantum cryptography lie in algorithmic complexity and hardware/software/network implementation. The grand question of how existing cyberinfrastructure will support post-quantum cryptography remains unanswered. This paper describes: i) the design of a novel Post-Quantum Cryptography (PQC) network instrument placed at the National Center for Supercomputing Applications (NCSA) at the University of Illinois at Urbana-Champaign and a part of the FABRIC testbed; ii) the latest results on PQC adoption rate across a wide spectrum of network protocols (Secure Shell -- SSH, Transport Layer Security -- TLS, etc.); iii) the current state of PQC implementation in key scientific applications (e.g., OpenSSH or SciTokens); iv) the challenges of being quantum-resistant; and v) discussion of potential novel attacks. This is the first large-scale measurement of PQC adoption at national-scale supercomputing centers and FABRIC testbeds. Our results show that only OpenSSH and Google Chrome have successfully implemented PQC and achieved an initial adoption rate of 0.029% (6,044 out of 20,556,816) for OpenSSH connections at NCSA coming from major Internet Service Providers or Autonomous Systems (ASes) such as OARNET, GTT, Google Fiber Webpass (U.S.) and Uppsala Lans Landsting (Sweden), with an overall increasing adoption rate year-over-year for 2023-2024. Our analyses identify pathways to migrate current applications to be quantum-resistant.

cs.NI↗

arXiv2024

Deep Generative Attacks and Countermeasures for Data-Driven Offline Signature Verification

This study investigates the vulnerabilities of data-driven offline signature verification (DASV) systems to generative attacks and proposes robust countermeasures. Specifically, we explore the efficacy of Variational Autoencoders (VAEs) and Conditional Generative Adversarial Networks (CGANs) in creating deceptive signatures that challenge DASV systems. Using the Structural Similarity Index (SSIM) to evaluate the quality of forged signatures, we assess their impact on DASV systems built with Xception, ResNet152V2, and DenseNet201 architectures. Initial results showed False Accept Rates (FARs) ranging from 0% to 5.47% across all models and datasets. However, exposure to synthetic signatures significantly increased FARs, with rates ranging from 19.12% to 61.64%. The proposed countermeasure, i.e., retraining the models with real + synthetic datasets, was very effective, reducing FARs between 0% and 0.99%. These findings emphasize the necessity of investigating vulnerabilities in security systems like DASV and reinforce the role of generative methods in enhancing the security of data-driven systems.

cs.CV↗

arXiv2019

On Preempting Advanced Persistent Threats Using Probabilistic Graphical Models

This paper presents PULSAR, a framework for pre-empting Advanced Persistent Threats (APTs). PULSAR employs a probabilistic graphical model (specifically a Factor Graph) to infer the time evolution of an attack based on observed security events at runtime. PULSAR (i) learns the statistical significance of patterns of events from past attacks; (ii) composes these patterns into FGs to capture the progression of the attack; and (iii) decides on preemptive actions. PULSAR's accuracy and its performance are evaluated in three experiments at SystemX: (i) a study with a dataset containing 120 successful APTs over the past 10 years (PULSAR accurately identifies 91.7%); (ii) replaying of a set of ten unseen APTs (PULSAR stops 8 out of 10 replayed attacks before system integrity violation, and all ten before data exfiltration); and (iii) a production deployment of PULSAR (during a month-long deployment, PULSAR took an average of one second to make a decision).

cs.CR↗