arXiv subjects

Sizhe Huang

Publications and source records attributed to Sizhe Huang.

6 recordsLinked to original sources

arXiv2026

SoK: Where Do Flow Labels Come From? Auditing Label Provenance in Encrypted Traffic Benchmarks

Encrypted traffic classification infers semantics beyond the flow record from transport-layer observables, and supervised training rests on labels that hold for the individual flow they are attached to. Recent systematizations scrutinize model in- puts and data splits; we systematize the complementary label side. Across 14 audited benchmark entries, we identify two recurring label-side strategies: coarse inheritance, which risks labelling flows the evidence does not cover, and overstrict filtering, which keeps only self-attesting flows and risks dis- carding relevant ones. No audited entry exposes a countable pre-selection population, and the task objects downstream papers attach to the same labels disagree with the recovered record in 8 of 23 referenced cells. Under strict side-channel features we derive a representation-relative ceiling on bal- anced accuracy for any classifier restricted to those features: on the public benchmarks that inherit, it ranges from 0.56 to 0.76. On the filtering side, only 24.95% of connections in our fully captured corpus carry an observable SNI of their own; yet the discarded connections raise macro accuracy from 0.44 to 0.65 through same-run co-occurrence features. We end with recommendations for benchmark builders and users.

cs.NI↗

arXiv2026

Where Do Flow Semantics Reside? A Protocol-Native Tabular Pretraining Paradigm for Encrypted Traffic Classification

Self-supervised masked modeling shows promise for encrypted traffic classification by masking and reconstructing raw bytes. Yet recent work reveals these methods fail to reduce reliance on labeled data despite costly pretraining: under frozen encoder evaluation, accuracy drops from greater than 0.9 to less than 0.47. We argue the root cause is inductive bias mismatch: flattening traffic into byte sequences destroys protocol-defined semantics. We identify three specific issues: 1) field unpredictability, random fields like ip.id are unlearnable yet treated as reconstruction targets; 2) embedding confusion, semantically distinct fields collapse into a unified embedding space; 3) metadata loss, capture-time metadata essential for temporal analysis is discarded. To address this, we propose a protocol-native paradigm that treats protocol-defined field semantics as architectural priors, reformulating the task to align with the data's intrinsic tabular modality rather than incrementally adapting sequence-based architectures. Instantiating this paradigm, we introduce FlowSem-MAE, a tabular masked autoencoder built on Flow Semantic Units (FSUs). It features predictability-guided filtering that focuses on learnable FSUs, FSU-specific embeddings to preserve field boundaries, and dual-axis attention to capture intra-packet and temporal patterns. FlowSem-MAE significantly outperforms state-of-the-art across datasets. With only half labeled data, it outperforms most existing methods trained on full data.

cs.NI↗

arXiv2026

TASER: Task-Aware Spectral Energy Refine for Backdoor Suppression in UAV Swarms Decentralized Federated Learning

As backdoor attacks in UAV-based decentralized federated learning (DFL) grow increasingly stealthy and sophisticated, existing defenses have likewise escalated in complexity. Yet these defenses, which rely heavily on outlier detection, remain vulnerable to carefully crafted backdoors. In UAV-DFL, the lack of global coordination and limited resources further render outlier-based defenses impractical. Against this backdrop, gradient spectral analysis offers a promising alternative. While prior work primarily leverages low-frequency coefficients for pairwise comparisons, it neglects to analyze the intrinsic spectral characteristics of backdoor gradients. Through empirical analysis of existing stealthy attacks, we reveal a key insight: the more effort attackers invest in mimicking benign behaviors, the more distinct the spectral concentration becomes. Motivated by this, we propose Task-Aware Spectral Energy Refine (TASER) -- a decentralized defense framework. To our knowledge, this is the first efficient backdoor defense that utilizes spectral concentration instead of complex outlier detection, enabling mitigation of stealthy attacks by structurally disrupting the backdoor task. To suppress the backdoor task, TASER preserves main-task-relevant frequency coefficients and discards others. We provide theoretical guarantees and demonstrate through experiments that TASER remains effective against stealthy backdoor attacks that bypass outlier-based defenses, achieving attack success rate below 20% and accuracy loss under 5%.

cs.CR↗

arXiv2026

Decoupling effects of the resistive-switching behavior on the polarization reversal in ultrathin ferroelectric Hf0.5Zr0.5O2 films

HfO2-based ferroelectric films have attracted considerable attention as their nanoscale ferroelectricity and compatibility with cmos technology, fulfilling demands of emerging memory technologies. However, as films scale down, resistive-switching behavior becomes increasingly pronounced, intricately intertwining with the polarization-switching process and affecting ferroelectric switching factors often overlooked yet crucial for device performance optimization. By characterizing resistive-switching behavior and oxygen vacancy motion using tailored electric pulse schemes, we decouple the resistive-switching behavior from the overall switching process in ultrathin ferroelectric HZO films, which would otherwise erroneously inflate polarization values and increase coercive fields. Building on this, we elucidate endurance degradation mechanisms from dual perspectives of resistive switching and defect migration. Furthermore, we demonstrate the mitigated resistive switching activity by designing HfO2-based devices with symmetric oxide electrodes, achieving reduced coercive fields and improved cycling performances. This work provides crucial insights into the origins of inflated polarizations and reliability challenges in HfO2-based devices while offering a viable strategy to enhance ferroelectric properties for advanced memory applications.

cond-mat.mtrl-sci↗

arXiv2025

Enhancing ferroelectric stability: Wide-range of adaptive control in epitaxial HfO2/ZrO2 superlattices

The metastability of the polar phase in HfO2, despite its excellent compatibility with the complementary metal-oxide-semiconductor process, remains a key obstacle for its industrial applications. Traditional stabilization approaches, such as doping, often induce crystal defects and impose constraints on the thickness of ferroelectric HfO2 thin films. These limitations render the ferroelectric properties vulnerable to degradation, particularly due to phase transitions under operational conditions. Here, we demonstrate robust ferroelectricity in high-quality epitaxial (HfO2)n/(ZrO2)n superlattices, which exhibit significantly enhanced ferroelectric stability across an extended thickness range. Optimized-period superlattices maintain stable ferroelectricity from up to 100 nm, excellent fatigue resistance exceeding 109 switching cycles, and a low coercive field of ~0.85 MV/cm. First-principles calculations reveal that the kinetic energy barrier of phase transition and interfacial formation energy are crucial factors in suppressing the formation of non-polar phases. This work establishes a versatile platform for exploring high-performance fluorite-structured superlattices and advances the integration of HfO2-based ferroelectrics into a broader range of applications.

cond-mat.mtrl-sci↗

arXiv2025

Fatigue-free ferroelectricity in Hf0.5Zr0.5O2 ultrathin films via interfacial design

Due to traits of CMOS compatibility and scalability, HfO2-based ferroelectrics are promising candidates for next-generation memory devices. However, their commercialization has been greatly hindered by reliability issues, with fatigue being a major impediment. We report the fatigue-free behavior in interface-designed Hf0.5Zr0.5O2-based heterostructures. A coherent CeO2-x/Hf0.5Zr0.5O2 heterointerface is constructed, wherein CeO2-x acts as an oxygen sponge, capable of reversibly accepting and releasing oxygen vacancies. This design effectively alleviates defect aggregation at the electrode-ferroelectric interface, enabling improved switching characteristics. Further, a symmetric capacitor architecture is designed to minimize the imprint, thereby suppressing the cycling-induced oriented defect drift. The two-pronged technique mitigates oxygen-voltammetry-generated chemical/energy fluctuations, suppressing the formation of paraelectric phase and polarization degradation. The design ensures a fatigue-free feature exceeding 10^11 switching cycles and an endurance lifetime surpassing 10^12 cycles for Hf0.5Zr0.5O2-based capacitors, along with excellent temperature stability and retention. These findings pave the way for developing ultra-stable hafnia-based ferroelectric devices.

cond-mat.mtrl-sci↗