arXiv subjects

Hui Wen

Publications and source records attributed to Hui Wen.

12 recordsLinked to original sources

arXiv2026

Orbital Hall Effect Enables Field-Free Magnetization Reversal in Ferrimagnets without Additional Conversion Layer

The spin Hall effect provides a well-established route for electrical magnetization control, while the orbital Hall effect offers a powerful yet less explored source of angular momentum. Achieving field-free deterministic switching in straightforward orbital-torque architectures remains challenging. Here, we demonstrate orbital-Hall-current-driven switching in a Mo/CoGd bilayer without the need for a separate orbital-to-spin conversion layer across a wide temperature range. In this simplified geometry, Mo serves as both an orbital and spin current source. However, the spin contribution is insufficient due to weak spin-orbit coupling, which is consistent with first-principles calculations predicting a large orbital Hall conductivity. The adjacent ferrimagnetic CoGd layer provides both orbital-to-spin conversion and the perpendicular switching medium. Planar Hall and current-induced loop-shift measurements reveal a substantial unconventional z-polarized damping-like torque originating from interfacial symmetry breaking. Increasing the Mo thickness from 0.2 to 2 nm increases torque efficiency by approximately 31% (y-polarized) and 71% (z-polarized) components. This enhancement enables field-free deterministic switching with a critical current density down to 2.51 x 10^6 A cm^-2. Our results establish Mo/CoGd bilayers as a compact platform for orbital-current switching and point toward low-power orbitronic memory devices.

cond-mat.mtrl-sci↗

arXiv2025

Dynamic Vulnerability Patching for Heterogeneous Embedded Systems Using Stack Frame Reconstruction

Existing dynamic vulnerability patching techniques are not well-suited for embedded devices, especially mission-critical ones such as medical equipment, as they have limited computational power and memory but uninterrupted service requirements. Those devices often lack sufficient idle memory for dynamic patching, and the diverse architectures of embedded systems further complicate the creation of patch triggers that are compatible across various system kernels and hardware platforms. To address these challenges, we propose a hot patching framework called StackPatch that facilitates patch development based on stack frame reconstruction. StackPatch introduces different triggering strategies to update programs stored in memory units. We leverage the exception-handling mechanisms commonly available in embedded processors to enhance StackPatch's adaptability across different processor architectures for control flow redirection. We evaluated StackPatch on embedded devices featuring three major microcontroller (MCU) architectures: ARM, RISC-V, and Xtensa. In the experiments, we used StackPatch to successfully fix 102 publicly disclosed vulnerabilities in real-time operating systems (RTOS). We applied patching to medical devices, soft programmable logic controllers (PLCs), and network services, with StackPatch consistently completing each vulnerability remediation in less than 260 MCU clock cycles.

cs.CR↗

arXiv2025

Orbital Hall Effect Enables Field-Free Magnetization Reversal in Ferrimagnets without Additional Conversion Layer

The spin Hall effect (SHE) enables efficient electrical manipulation of magnetization through the spin Hall current \left(\mathbit{J}_{\mathbit{SHE}}\right), advancing energy-efficient spintronics. In parallel, the orbital Hall effect (OHE) offers an alternative pathway to SHE for converting charge current into an angular momentum flow. In this study, we demonstrate field-free current-induced perpendicular ferrimagnetic deterministic switching within a Mo/CoGd device without an additional orbital-to-spin conversion layer. This is achieved by harnessing localized orbital Hall currents \left(\mathbit{J}_{\mathbit{OHE}}\right) generated in the Mo layer. The in-plane symmetry breaking at the Mo/CoGd surface-interface layer, validated by a pronounced planar Hall effect, gives rise to a substantial unconventional z-polarized damping-like torque. The CoGd serves a dual role: not only as a converter that transforms the significant \mathbit{J}_{\mathbit{OHE}} into \mathbit{J}_{\mathbit{SHE}} but also as a ferrimagnetic self-switching mechanism. This dual functionality enables highly efficient field-free current-induced magnetization switching with a critical current density as low as \mathbf{2}.\mathbf{51}\ \times{\mathbf{10}}^\mathbf{6} A cm-2. Our work highlights the potential of orbital Hall currents for energy-efficient magnetization switching, making a notable contribution to the burgeoning field of orbitronics.

cond-mat.mtrl-sci↗

arXiv2025

Unconventional bias-dependent tunneling magnetoresistance in van der Waals ferromagnetic/semiconductor heterojunctions

Two-dimensional van der Waals (vdW) ferromagnetic/semiconductor heterojunctions represent an ideal platform for studying and exploiting tunneling magnetoresistance (TMR) effects due to the versatile band structure of semiconductors and their high-quality interfaces. In the all-vdW magnetic tunnel junction (MTJ) devices, both the magnitude and sign of the TMR can be tuned by an applied voltage. Typically, as the bias voltage increases, first the amplitude of the TMR decreases, then the sign of the TMR reverses and/or oscillates. Here, we report on an unconventional bias-dependent TMR in the all-vdW Fe3GaTe2/GaSe/Fe3GaTe2 MTJs, where the TMR first increases, then decreases, and finally undergoes a sign reversal as the bias voltage increases. This dependence cannot be explained by traditional models of MTJs. We propose an in-plane electron momentum (k//) resolved tunneling model that considers both the coherent degree of k// and the decay of the electron wave function through the semiconductor spacer layer. This can explain well the conventional and unconventional bias-dependent TMR. Our results thus provide a deeper understanding of the bias-dependent spin-transport in semiconductor-based MTJs and offer new insights into semiconductor spintronics.

cond-mat.mtrl-sci↗

arXiv2024

Tactics, Techniques, and Procedures (TTPs) in Interpreted Malware: A Zero-Shot Generation with Large Language Models

Nowadays, the open-source software (OSS) ecosystem suffers from security threats of software supply chain (SSC) attacks. Interpreted OSS malware plays a vital role in SSC attacks, as criminals have an arsenal of attack vectors to deceive users into installing malware and executing malicious activities. In this paper, we introduce tactics, techniques, and procedures (TTPs) proposed by MITRE ATT\&CK into the interpreted malware analysis to characterize different phases of an attack lifecycle. Specifically, we propose GENTTP, a zero-shot approach to extracting a TTP of an interpreted malware package. GENTTP leverages large language models (LLMs) to automatically generate a TTP, where the input is a malicious package, and the output is a deceptive tactic and an execution tactic of attack vectors. To validate the effectiveness of GENTTP, we collect two datasets for evaluation: a dataset with ground truth labels and a large dataset in the wild. Experimental results show that GENTTP can generate TTPs with high accuracy and efficiency. To demonstrate GENTTP's benefits, we build an LLM-based Chatbot from 3,700+ PyPI malware's TTPs. We further conduct a quantitative analysis of malware's TTPs at a large scale. Our main findings include: (1) many OSS malicious packages share a relatively stable TTP, even with the increasing emergence of malware and attack campaigns, (2) a TTP reflects characteristics of a malware-based attack, and (3) an attacker's intent behind the malware is linked to a TTP.

cs.CR↗

arXiv2024

TimeTravel: Real-time Timing Drift Attack on System Time Using Acoustic Waves

Real-time Clock (RTC) has been widely used in various real-time systems to provide precise system time. In this paper, we reveal a new security vulnerability of the RTC circuit, where the internal storage time or timestamp can be arbitrarily modified forward or backward. The security threat of dynamic modifications of system time caused by this vulnerability is called TimeTravel. Based on acoustic resonance and piezoelectric effects, TimeTravel applies acoustic guide waves to the quartz crystal, thereby adjusting the characteristics of the oscillating signal transmitted into the RTC circuit. By manipulating the parameters of acoustic waves, TimeTravel can accelerate or decelerate the timing speed of system time at an adjustable rate, resulting in the relative drift of the timing, which can pose serious safety threats. To assess the severity of TimeTravel, we examine nine modules and two commercial devices under the RTC circuit. The experimental results show that TimeTravel can drift system time forward and backward at a chosen speed with a maximum 93% accuracy. Our analysis further shows that TimeTravel can maintain an attack success rate of no less than 77% under environments with typical obstacle items.

cs.CR↗

arXiv2024

When LLMs Meet Cybersecurity: A Systematic Literature Review

The rapid development of large language models (LLMs) has opened new avenues across various fields, including cybersecurity, which faces an evolving threat landscape and demand for innovative technologies. Despite initial explorations into the application of LLMs in cybersecurity, there is a lack of a comprehensive overview of this research area. This paper addresses this gap by providing a systematic literature review, covering the analysis of over 300 works, encompassing 25 LLMs and more than 10 downstream scenarios. Our comprehensive overview addresses three key research questions: the construction of cybersecurity-oriented LLMs, the application of LLMs to various cybersecurity tasks, the challenges and further research in this area. This study aims to shed light on the extensive potential of LLMs in enhancing cybersecurity practices and serve as a valuable resource for applying LLMs in this field. We also maintain and regularly update a list of practical guides on LLMs for cybersecurity at https://github.com/tmylla/Awesome-LLM4Cybersecurity.

cs.CR↗

arXiv2024

Orbital Hall effect assisted field-free perpendicular magnetization switching

Spin-orbit torques (SOTs) generated through the conventional spin Hall effect (SHE) and/or Rashba-Edelstein effect offer potential for magnetization manipulation. However, deterministic switching of perpendicular ferromagnets via SOTs requires a strong symmetry-breaking perturbation, typically an external magnetic field. Here, we demonstrate that field-free SOT switching of perpendicular magnetization can be facilitated with the assistance of the orbital Hall effect (OHE). Using a representative Co/PtGd bilayer SOT device, we find that while the planar Hall effect (PHE) generates a finite out-of-plane damping-like torque, representing a lateral symmetry breaking, the SHE-induced torque achievable at practical current density is insufficient to switch the perpendicular magnetization. Incorporating a Mo underlayer and exploiting its strong OHE can amplify the in-plane damping-like torque via orbital-to-spin conversion, enabling efficient field-free deterministic switching without complex device geometries or low symmetric spin sources, providing a straightforward and scalable strategy for achieving high-speed and low-power spintronics.

cond-mat.mes-hall↗

arXiv2021

Firmware Re-hosting Through Static Binary-level Porting

The rapid growth of the Industrial Internet of Things (IIoT) has brought embedded systems into focus as major targets for both security analysts and malicious adversaries. Due to the non-standard hardware and diverse software, embedded devices present unique challenges to security analysts for the accurate analysis of firmware binaries. The diversity in hardware components and tight coupling between firmware and hardware makes it hard to perform dynamic analysis, which must have the ability to execute firmware code in virtualized environments. However, emulating the large expanse of hardware peripherals makes analysts have to frequently modify the emulator for executing various firmware code in different virtualized environments, greatly limiting the ability of security analysis. In this work, we explore the problem of firmware re-hosting related to the real-time operating system (RTOS). Specifically, developers create a Board Support Package (BSP) and develop device drivers to make that RTOS run on their platform. By providing high-level replacements for BSP routines and device drivers, we can make the minimal modification of the firmware that is to be migrated from its original hardware environment into a virtualized one. We show that an approach capable of offering the ability to execute firmware at scale through patching firmware in an automated manner without modifying the existing emulators. Our approach, called static binary-level porting, first identifies the BSP and device drivers in target firmware, then patches the firmware with pre-built BSP routines and drivers that can be adapted to the existing emulators. Finally, we demonstrate the practicality of the proposed method on multiple hardware platforms and firmware samples for security analysis. The result shows that the approach is flexible enough to emulate firmware for vulnerability assessment and exploits development.

cs.CR↗

arXiv2020

Transferring Inter-Class Correlation

The Teacher-Student (T-S) framework is widely utilized in the classification tasks, through which the performance of one neural network (the student) can be improved by transferring knowledge from another trained neural network (the teacher). Since the transferring knowledge is related to the network capacities and structures between the teacher and the student, how to define efficient knowledge remains an open question. To address this issue, we design a novel transferring knowledge, the Self-Attention based Inter-Class Correlation (ICC) map in the output layer, and propose our T-S framework, Inter-Class Correlation Transfer (ICCT).

cs.CV↗

arXiv2020

H-VGRAE: A Hierarchical Stochastic Spatial-Temporal Embedding Method for Robust Anomaly Detection in Dynamic Networks

Detecting anomalous edges and nodes in dynamic networks is critical in various areas, such as social media, computer networks, and so on. Recent approaches leverage network embedding technique to learn how to generate node representations for normal training samples and detect anomalies deviated from normal patterns. However, most existing network embedding approaches learn deterministic node representations, which are sensitive to fluctuations of the topology and attributes due to the high flexibility and stochasticity of dynamic networks. In this paper, a stochastic neural network, named by Hierarchical Variational Graph Recurrent Autoencoder (H-VGRAE), is proposed to detect anomalies in dynamic networks by the learned robust node representations in the form of random variables. H-VGRAE is a semi-supervised model to capture normal patterns in training set by maximizing the likelihood of the adjacency matrix and node attributes via variational inference. Comparing with existing methods, H-VGRAE has three main advantages: 1) H-VGRAE learns robust node representations through stochasticity modeling and the extraction of multi-scale spatial-temporal features; 2) H-VGRAE can be extended to deep structure with the increase of the dynamic network scale; 3) the anomalous edge and node can be located and interpreted from the probabilistic perspective. Extensive experiments on four real-world datasets demonstrate the outperformance of H-VGRAE on anomaly detection in dynamic networks compared with state-of-the-art competitors.

cs.SI↗

arXiv2017

Joint RNN Model for Argument Component Boundary Detection

Argument Component Boundary Detection (ACBD) is an important sub-task in argumentation mining; it aims at identifying the word sequences that constitute argument components, and is usually considered as the first sub-task in the argumentation mining pipeline. Existing ACBD methods heavily depend on task-specific knowledge, and require considerable human efforts on feature-engineering. To tackle these problems, in this work, we formulate ACBD as a sequence labeling problem and propose a variety of Recurrent Neural Network (RNN) based methods, which do not use domain specific or handcrafted features beyond the relative position of the sentence in the document. In particular, we propose a novel joint RNN model that can predict whether sentences are argumentative or not, and use the predicted results to more precisely detect the argument component boundaries. We evaluate our techniques on two corpora from two different genres; results suggest that our joint RNN model obtain the state-of-the-art performance on both datasets.

cs.CL↗