arXiv subjects

Zhou Li

Publications and source records attributed to Zhou Li.

At least 37 records · Page 2Linked to original sources

arXiv2025

Proving DNSSEC Correctness: A Formal Approach to Secure Domain Name Resolution

The Domain Name System Security Extensions (DNSSEC) are critical for preventing DNS spoofing, yet its specifications contain ambiguities and vulnerabilities that elude traditional "break-and-fix" approaches. A holistic, foundational security analysis of the protocol has thus remained an open problem. This paper introduces DNSSECVerif, the first framework for comprehensive, automated formal security analysis of the DNSSEC protocol suite. Built on the SAPIC+ symbolic verifier, our high-fidelity model captures protocol-level interactions, including cryptographic operations and stateful caching with fine-grained concurrency control. Using DNSSECVerif, we formally prove four of DNSSEC's core security guarantees and uncover critical ambiguities in the standards--notably, the insecure coexistence of NSEC and NSEC3. Our model also automatically rediscovers three classes of known attacks, demonstrating fundamental weaknesses in the protocol design. To bridge the model-to-reality gap, we validate our findings through targeted testing of mainstream DNS software and a large-scale measurement study of over 2.2 million open resolvers, confirming the real-world impact of these flaws. Our work provides crucial, evidence-based recommendations for hardening DNSSEC specifications and implementations.

cs.CR↗

arXiv2025

The Capacity of Collusion-Resilient Decentralized Secure Aggregation with Groupwise Keys

This paper investigates the information-theoretic decentralized secure aggregation (DSA) problem under practical groupwise secret keys and collusion resilience. In DSA, $K$ users are interconnected through error-free broadcast channels. Each user holds a private input and aims to compute the sum of all other users' inputs, while satisfying the security constraint that no user, even when colluding with up to $T$ other users, can infer any information about the inputs beyond the recovered sum. To ensure security, users are equipped with secret keys to mask their inputs. Motivated by recent advances in efficient group-based key generation protocols, we consider the symmetric groupwise key setting, where every subset of $G$ users shares a group key that is independent of all other group keys. The problem is challenging because the recovery and security constraints must hold simultaneously for all users, and the structural constraints on the secret keys limit the flexibility of key correlations. We characterize the optimal rate region consisting of all achievable pairs of per-user broadcast communication rate and groupwise key rate. In particular, we show that DSA with groupwise keys is infeasible when $G=1$ or $G\ge K-T$. Otherwise, when $2\le G<K-T$, to securely compute one symbol of the desired sum, each user must broadcast at least one symbol, and each group key must contain at least $(K-T-2)/\binom{K-T-1}{G}$ independent symbols. Our results establish the fundamental limits of DSA with groupwise keys and provide design insights for communication- and key-efficient secure aggregation in decentralized learning systems.

cs.IT↗

arXiv2025

Dynamic Risk Assessments for Offensive Cybersecurity Agents

Foundation models are increasingly becoming better autonomous programmers, raising the prospect that they could also automate dangerous offensive cyber-operations. Current frontier model audits probe the cybersecurity risks of such agents, but most fail to account for the degrees of freedom available to adversaries in the real world. In particular, with strong verifiers and financial incentives, agents for offensive cybersecurity are amenable to iterative improvement by would-be adversaries. We argue that assessments should take into account an expanded threat model in the context of cybersecurity, emphasizing the varying degrees of freedom that an adversary may possess in stateful and non-stateful environments within a fixed compute budget. We show that even with a relatively small compute budget (8 H100 GPU Hours in our study), adversaries can improve an agent's cybersecurity capability on InterCode CTF by more than 40\% relative to the baseline -- without any external assistance. These results highlight the need to evaluate agents' cybersecurity risk in a dynamic manner, painting a more representative picture of risk.

cs.CR↗

arXiv2025

Entente: Cross-silo Intrusion Detection on Network Log Graphs with Federated Learning

Graph-based Network Intrusion Detection Systems (GNIDS) have gained significant momentum in detecting sophisticated cyber-attacks, such as Advanced Persistent Threats (APTs), within and across organizational boundaries. Though achieving satisfying detection accuracy and demonstrating adaptability to ever-changing attacks and normal patterns, existing GNIDS predominantly assume a centralized data setting. However, flexible data collection is not always realistic or achievable due to increasing constraints from privacy regulations and operational limitations. We argue that the practical development of GNIDS requires accounting for distributed collection settings and we leverage Federated Learning (FL) as a viable paradigm to address this prominent challenge. We observe that naively applying FL to GNIDS is unlikely to be effective, due to issues like graph heterogeneity over clients and the diverse design choices taken by different GNIDS. We address these issues with a set of novel techniques tailored to the graph datasets, including reference graph synthesis, graph sketching and adaptive contribution scaling, eventually developing a new system Entente. By leveraging the domain knowledge, Entente can achieve effectiveness, scalability and robustness simultaneously. Empirical evaluation on the large-scale LANL, OpTC and Pivoting datasets shows that Entente outperforms the SOTA FL baselines. We also evaluate Entente under FL poisoning attacks tailored to the GNIDS setting, showing the robustness by bounding the attack success rate to low values. Overall, our study suggests a promising direction to build cross-silo GNIDS.

cs.CR↗

arXiv2025

Network-Aware Differential Privacy

Differential privacy (DP) is a privacy-enhancement technology (PET) that receives prominent attention from the academia, industry, and government. One main development over the past decade has been the decentralization of DP, including local DP and shuffle DP. Despite that decentralized DP heavily relies on network communications for data collection,we found that: 1) no systematic study has surveyed the research opportunities at the intersection of networking and DP; 2) nor have there been significant efforts to develop DP mechanisms that are explicitly tailored for network environments. In this paper, we seek to address this gap by initiating a new direction of network-aware DP. We identified two focus areas where the network research can offer substantive contributions to the design and deployment of DP, related to network security and topology. Through this work, we hope to encourage more research that adapt/optimize DP's deployment in various network environments.

cs.CR↗

arXiv2025

From Alerts to Intelligence: A Novel LLM-Aided Framework for Host-based Intrusion Detection

Host-based intrusion detection system (HIDS) is a key defense component to protect the organizations from advanced threats like Advanced Persistent Threats (APT). By analyzing the fine-grained logs with approaches like data provenance, HIDS has shown successes in capturing sophisticated attack traces. Despite the progresses embarked by the research community and industry, HIDS still frequently encounters backlash from their operators in the deployed environments, due to issues like high false-positive rate, inconsistent outcomes across environments and human-unfriendly detection results. Large Language Models (LLMs) have great potentials to advance the state of HIDS, given their extensive knowledge of attack techniques and their ability to detect anomalies through semantic analysis, anchored by recent studies. Yet, our preliminary analysis indicates that building an HIDS by naively prompting an LLM is unlikely to succeed. In this work, we explore the direction of building a customized LLM pipeline for HIDS and develop a system named SHIELD. SHIELD addresses challenges related to LLM's token limits, confusion of background noises, etc., by integrating a variety of techniques like event-level Masked Autoencoder (MAE) for attack window detection, attack evidence identification and expansion, Deterministic Data Augmentation (DDA) for profiling normal activities, and multi-purpose prompting that guides the LLM to conduct precise and interpretable attack investigations. Extensive experiments on three log datasets (DARPA-E3, NodLink-simulated-data and ATLASv2) show that SHIELD consistently achieves outstanding performance in comparison with 5 representative HIDS. These findings highlight the potential of LLMs as powerful tools for intrusion detection and pave the way for future research in this domain.

cs.CR↗

arXiv2025

Hunting the Ghost: Towards Automatic Mining of IoT Hidden Services

In this paper, we proposes an automatic firmware analysis tool targeting at finding hidden services that may be potentially harmful to the IoT devices. Our approach uses static analysis and symbolic execution to search and filter services that are transparent to normal users but explicit to experienced attackers. A prototype is built and evaluated against a dataset of IoT firmware, and The evaluation shows our tool can find the suspicious hidden services effectively.

cs.CR↗

arXiv2025

FetalFlex: Anatomy-Guided Diffusion Model for Flexible Control on Fetal Ultrasound Image Synthesis

Fetal ultrasound (US) examinations require the acquisition of multiple planes, each providing unique diagnostic information to evaluate fetal development and screening for congenital anomalies. However, obtaining a comprehensive, multi-plane annotated fetal US dataset remains challenging, particularly for rare or complex anomalies owing to their low incidence and numerous subtypes. This poses difficulties in training novice radiologists and developing robust AI models, especially for detecting abnormal fetuses. In this study, we introduce a Flexible Fetal US image generation framework (FetalFlex) to address these challenges, which leverages anatomical structures and multimodal information to enable controllable synthesis of fetal US images across diverse planes. Specifically, FetalFlex incorporates a pre-alignment module to enhance controllability and introduces a repaint strategy to ensure consistent texture and appearance. Moreover, a two-stage adaptive sampling strategy is developed to progressively refine image quality from coarse to fine levels. We believe that FetalFlex is the first method capable of generating both in-distribution normal and out-of-distribution abnormal fetal US images, without requiring any abnormal data. Experiments on multi-center datasets demonstrate that FetalFlex achieved state-of-the-art performance across multiple image quality metrics. A reader study further confirms the close alignment of the generated results with expert visual assessments. Furthermore, synthetic images by FetalFlex significantly improve the performance of six typical deep models in downstream classification and anomaly detection tasks. Lastly, FetalFlex's anatomy-level controllable generation offers a unique advantage for anomaly simulation and creating paired or counterfactual data at the pixel level. The demo is available at: https://dyf1023.github.io/FetalFlex/.

eess.IV↗

arXiv2025

Entanglement-Assisted Concatenated Quantum Codes: Parameters and Asymptotic Performance

Entanglement-assisted concatenated quantum codes (EACQCs) are constructed by concatenating two entanglement-assisted quantum error-correcting codes (EAQECCs). By selecting the inner and outer component codes carefully, it is able to construct state-of-the-art EACQCs with parameters better than previous quantum codes. In this work, we use almost maximum-distance-separable (MDS) codes and $\hbar$-MDS codes as the outer codes to construct EACQCs. Because the range of code length of almost MDS and $\hbar$-MDS codes is much more free than that of the commonly used MDS codes. We derive several families of new EACQCs with parameters better than the previously best known EAQECCs and standard quantum error-correcting codes (QECCs) of the same length and net transmissions. Moreover, we demonstrate that EACQCs are with maximal entanglement if both the inner and outer component codes are with maximal entanglement. As a result, we construct three new maximal-entanglement EACQCs which have optimal parameters. In addition, we present several new maximal-entanglement EACQCs whose minimum distance is only one less than the minimum distance of the optimal codes. In particular, we propose two new families of asymptotically good maximal-entanglement EACQCs with explicit constructions by using entanglement-assisted quantum algebraic geometry codes as the outer codes. At last, we prove that EACQCs can attain the quantum Gilbert-Varshamov bound for EAQECCs asymptotically.

quant-ph↗

arXiv2024

NetDPSyn: Synthesizing Network Traces under Differential Privacy

As the utilization of network traces for the network measurement research becomes increasingly prevalent, concerns regarding privacy leakage from network traces have garnered the public's attention. To safeguard network traces, researchers have proposed the trace synthesis that retains the essential properties of the raw data. However, previous works also show that synthesis traces with generative models are vulnerable under linkage attacks. This paper introduces NetDPSyn, the first system to synthesize high-fidelity network traces under privacy guarantees. NetDPSyn is built with the Differential Privacy (DP) framework as its core, which is significantly different from prior works that apply DP when training the generative model. The experiments conducted on three flow and two packet datasets indicate that NetDPSyn achieves much better data utility in downstream tasks like anomaly detection. NetDPSyn is also 2.5 times faster than the other methods on average in data synthesis.

cs.CR↗

arXiv2024

Design principles of nonlinear optical materials for Terahertz lasers

We have investigated both inter-band and intra-band second order nonlinear optical conductivity based on the velocity correlation formalism and the spectral expansion technique. We propose a scenario in which the second order intra-band process is nonzero while the inter-band process is zero. This occurs for a band structure with momentum asymmetry in the Brillouin zone. Very low-energy photons are blocked by the Pauli exclusion principle from participating in the inter-band process; however, they are permitted to participate in the intra-band process, with the band smeared by some impurity scattering. We establish a connection between the inter-band nonlinear optical conductivity in the velocity gauge and the shift vector in the length gauge for a two-band model. Using a quasiclassical kinetic approach, we demonstrate the importance of intra-band transitions in high harmonic generations for the single tilted Dirac cone model and hexagonal warping model. We confirm that the Kramers-Kronig relations break down for the limit case of ($ω$, $-ω$) in the nonlinear optical conductivity. Finally, we calculate the superconducting transition temperature of NbN and the dielectric function of AlN, and the resistance of the NbN/AlN junction. The natural non-linearity of the Josephson junction brings a Josephson plasma with frequency in the Terahertz region.

cond-mat.mtrl-sci↗

arXiv2024

Non-perturbative dynamics of flat-band systems with correlated disorder

We develop a numerical method for the time evolution of Gaussian wave packets on flat-band lattices in the presence of correlated disorder. To achieve this, we introduce a method to generate random on-site energies with prescribed correlations. We verify this method with a one-dimensional (1D) cross-stitch model, and find good agreement with analytical results obtained from the disorder-dressed evolution equations. This allows us to reproduce previous findings, that disorder can mobilize 1D flat-band states which would otherwise remain localized. As explained by the corresponding disorder-dressed evolution equations, such mobilization requires an asymmetric disorder-induced coupling to dispersive bands, a condition that is generically not fulfilled when the flat-band is resonant with the dispersive bands at a Dirac point-like crossing. We exemplify this with the 1D Lieb lattice. While analytical expressions are not available for the two-dimensional (2D) system due to its complexity, we extend the numerical method to the 2D $α-T_3$ model, and find that the initial flat-band wave packet preserves its localization when $α= 0$, regardless of disorder and intersections. However, when $α\neq 0$, the wave packet shifts in real space. We interpret this as a Berry phase controlled, disorder-induced wave-packet mobilization. In addition, we present density functional theory calculations of candidate materials, specifically $\rm Hg_{1-x}Cd_xTe$. The flat-band emerges near the $Γ$ point ($\bf{k}=$0) in the Brillouin zone.

cond-mat.dis-nn↗

arXiv2024

Thermal conductivity of macroporous graphene aerogel measured using high resolution comparative infrared thermal microscopy

Graphene aerogel (GA) is a promising material for thermal management applications across many fields due to its lightweight and thermally insulative properties. However, standard values for important thermal properties, such as thermal conductivity, remain elusive due to the lack of reliable characterization techniques for highly porous materials. Comparative infrared thermal microscopy (CITM) is an attractive technique to obtain thermal conductance values of porous materials like GA, due to its non-invasive character, which requires no probing of, or contact with, the often-delicate structures and frameworks. In this study, we improve upon CITM by utilizing a higher resolution imaging setup and reducing the need for pore-filling coating of the sample (previously used to adjust for emissivity). This upgraded setup, verified by characterizing porous silica aerogel, allows for a more accurate confirmation of the fundamental thermal conductivity value of GA while still accounting for the thermal resistance at material boundaries. Using this improved method, we measure a thermal conductivity below 0.036 W/m$\cdot$K for commercial GA using multiple reference materials. These measurements demonstrate the impact of higher resolution thermal imaging to improve accuracy in low density, highly porous materials characterization. This study also reports thermal conductivity for much lower density (less than 15 mg/cm$^3$) GA than previously published studies while maintaining the robustness of the CITM technique.

physics.app-ph↗

arXiv2024

AutoAttacker: A Large Language Model Guided System to Implement Automatic Cyber-attacks

Large language models (LLMs) have demonstrated impressive results on natural language tasks, and security researchers are beginning to employ them in both offensive and defensive systems. In cyber-security, there have been multiple research efforts that utilize LLMs focusing on the pre-breach stage of attacks like phishing and malware generation. However, so far there lacks a comprehensive study regarding whether LLM-based systems can be leveraged to simulate the post-breach stage of attacks that are typically human-operated, or "hands-on-keyboard" attacks, under various attack techniques and environments. As LLMs inevitably advance, they may be able to automate both the pre- and post-breach attack stages. This shift may transform organizational attacks from rare, expert-led events to frequent, automated operations requiring no expertise and executed at automation speed and scale. This risks fundamentally changing global computer security and correspondingly causing substantial economic impacts, and a goal of this work is to better understand these risks now so we can better prepare for these inevitable ever-more-capable LLMs on the horizon. On the immediate impact side, this research serves three purposes. First, an automated LLM-based, post-breach exploitation framework can help analysts quickly test and continually improve their organization's network security posture against previously unseen attacks. Second, an LLM-based penetration test system can extend the effectiveness of red teams with a limited number of human analysts. Finally, this research can help defensive systems and teams learn to detect novel attack behaviors preemptively before their use in the wild....

cs.CR↗

arXiv2023

Continuous Release of Data Streams under both Centralized and Local Differential Privacy

In this paper, we study the problem of publishing a stream of real-valued data satisfying differential privacy (DP). One major challenge is that the maximal possible value can be quite large; thus it is necessary to estimate a threshold so that numbers above it are truncated to reduce the amount of noise that is required to all the data. The estimation must be done based on the data in a private fashion. We develop such a method that uses the Exponential Mechanism with a quality function that approximates well the utility goal while maintaining a low sensitivity. Given the threshold, we then propose a novel online hierarchical method and several post-processing techniques. Building on these ideas, we formalize the steps into a framework for private publishing of stream data. Our framework consists of three components: a threshold optimizer that privately estimates the threshold, a perturber that adds calibrated noises to the stream, and a smoother that improves the result using post-processing. Within our framework, we design an algorithm satisfying the more stringent setting of DP called local DP (LDP). To our knowledge, this is the first LDP algorithm for publishing streaming data. Using four real-world datasets, we demonstrate that our mechanism outperforms the state-of-the-art by a factor of 6-10 orders of magnitude in terms of utility (measured by the mean squared error of answering a random range query).

cs.CR↗

arXiv2023

Fractional quantum Hall interface induced by geometric singularity

The geometric response of quantum Hall liquids is an important aspect to understand their topological characteristics in addition to the electromagnetic response. According to the Wen-Zee theory, the topological spin is coupled to the curvature of the space in which the electrons reside. The presence of conical geometry provides a local isolated geometric singularity, making it suitable for exploring the geometric response. In the context of two-dimensional electrons in a perpendicular magnetic field, each Landau orbit occupies the same area. The cone geometry naturally provides a structure in which the distances between two adjacent orbits gradually change and can be easily adjusted by altering the tip angle. The presence of a cone tip introduces a geometric singularity that affects the electron density and interacts with the motion of electrons, which has been extensively studied. Furthermore, this type of geometry can automatically create a smooth interface or crossover between the crystalline charge-density-wave state and the liquid-like fractional quantum Hall state. In this work, the properties of this interface are studied from multiple perspectives, shedding light on the behavior of quantum Hall liquids in such geometric configurations.

cond-mat.mes-hall↗

arXiv2023

A Comprehensive Study of Privacy Risks in Curriculum Learning

Training a machine learning model with data following a meaningful order, i.e., from easy to hard, has been proven to be effective in accelerating the training process and achieving better model performance. The key enabling technique is curriculum learning (CL), which has seen great success and has been deployed in areas like image and text classification. Yet, how CL affects the privacy of machine learning is unclear. Given that CL changes the way a model memorizes the training data, its influence on data privacy needs to be thoroughly evaluated. To fill this knowledge gap, we perform the first study and leverage membership inference attack (MIA) and attribute inference attack (AIA) as two vectors to quantify the privacy leakage caused by CL. Our evaluation of nine real-world datasets with attack methods (NN-based, metric-based, label-only MIA, and NN-based AIA) revealed new insights about CL. First, MIA becomes slightly more effective when CL is applied, but the impact is much more prominent to a subset of training samples ranked as difficult. Second, a model trained under CL is less vulnerable under AIA, compared to MIA. Third, the existing defense techniques like DP-SGD, MemGuard, and MixupMMD are still effective under CL, though DP-SGD has a significant impact on target model accuracy. Finally, based on our insights into CL, we propose a new MIA, termed Diff-Cali, which exploits the difficulty scores for result calibration and is demonstrated to be effective against all CL methods and the normal training method. With this study, we hope to draw the community's attention to the unintended privacy risks of emerging machine-learning techniques and develop new attack benchmarks and defense solutions.

cs.CR↗

arXiv2023

ResolverFuzz: Automated Discovery of DNS Resolver Vulnerabilities with Query-Response Fuzzing

Domain Name System (DNS) is a critical component of the Internet. DNS resolvers, which act as the cache between DNS clients and DNS nameservers, are the central piece of the DNS infrastructure, essential to the scalability of DNS. However, finding the resolver vulnerabilities is non-trivial, and this problem is not well addressed by the existing tools. To list a few reasons, first, most of the known resolver vulnerabilities are non-crash bugs that cannot be directly detected by the existing oracles (or sanitizers). Second, there lacks rigorous specifications to be used as references to classify a test case as a resolver bug. Third, DNS resolvers are stateful, and stateful fuzzing is still challenging due to the large input space. In this paper, we present a new fuzzing system termed ResolverFuzz to address the aforementioned challenges related to DNS resolvers, with a suite of new techniques being developed. First, ResolverFuzz performs constrained stateful fuzzing by focusing on the short query-response sequence, which has been demonstrated as the most effective way to find resolver bugs, based on our study of the published DNS CVEs. Second, to generate test cases that are more likely to trigger resolver bugs, we combine probabilistic context-free grammar (PCFG) based input generation with byte-level mutation for both queries and responses. Third, we leverage differential testing and clustering to identify non-crash bugs like cache poisoning bugs. We evaluated ResolverFuzz against 6 mainstream DNS software under 4 resolver modes. Overall, we identify 23 vulnerabilities that can result in cache poisoning, resource consumption, and crash attacks. After responsible disclosure, 19 of them have been confirmed or fixed, and 15 CVE numbers have been assigned.

cs.CR↗