High-Confidence Computing最新文献

TACKLE: Time-based Access Control and Key delegation for Letter of credit Ecosystems 解决：基于时间的访问控制和信用证生态系统的密钥委托

IF 9

High-Confidence Computing Pub Date : 2026-09-01 Epub Date: 2025-11-01 DOI: 10.1016/j.hcc.2025.100369

Phat T. Tran-Truong, Ha X. Son, Vo H. Khanh, Nguyen M. Triet, Nguyen T. Anh, Le K. Bang, Nguyen T.K. Ngan

{"title":"TACKLE: Time-based Access Control and Key delegation for Letter of credit Ecosystems","authors":"Phat T. Tran-Truong, Ha X. Son, Vo H. Khanh, Nguyen M. Triet, Nguyen T. Anh, Le K. Bang, Nguyen T.K. Ngan","doi":"10.1016/j.hcc.2025.100369","DOIUrl":"10.1016/j.hcc.2025.100369","url":null,"abstract":"<div><div>Letters of Credit (L/Cs) are essential for international trade, but traditional systems struggle with security, efficiency, and access control. To address these challenges, we propose TACKLE, a new framework that combines identity-based encryption (IBE) with blockchain technology. TACKLE offers advanced features like time-based access control and hierarchical key delegation, making it ideal for complex international trade workflows. Our framework significantly improves document verification and access management while maintaining strong security. We tested TACKLE’s performance and found it to be efficient, with quick key generation times (68–74 ms) and fast encryption (3.933 to 8.333 s). Network latency was stable at 2–8 ms across different data sizes. Compared to RSA, TACKLE generated keys 34.2% faster while using similar system resources. Our results show that TACKLE effectively balances security with efficiency, making it suitable for typical L/C documents up to 10MB. With its strong security features and performance, TACKLE is a promising solution for modernizing international trade finance.</div></div>","PeriodicalId":100605,"journal":{"name":"High-Confidence Computing","volume":"6 3","pages":"Article 100369"},"PeriodicalIF":9.0,"publicationDate":"2026-09-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"148951490","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

A unified cross-task recommendation paradigm with mixture-of-experts routing mechanism 具有混合专家路由机制的统一跨任务推荐范例

IF 3

High-Confidence Computing Pub Date : 2026-06-01 Epub Date: 2026-06-04 DOI: 10.1016/j.hcc.2026.100404

Shiguo Sun , Jianlong Chen , Bo Zhang , Xuerui Jiao , Lei Guo , Zhumin Chen

{"title":"A unified cross-task recommendation paradigm with mixture-of-experts routing mechanism","authors":"Shiguo Sun , Jianlong Chen , Bo Zhang , Xuerui Jiao , Lei Guo , Zhumin Chen","doi":"10.1016/j.hcc.2026.100404","DOIUrl":"10.1016/j.hcc.2026.100404","url":null,"abstract":"<div><div>Unified Large Language Models (LLMs) have transformed diverse recommendation tasks into a shared “text-to-text” paradigm. However, existing methods primarily face two challenges: 1) joint fine-tuning across highly heterogeneous tasks (e.g., discriminative sequential recommendation versus generative explanation generation) often suffers from the “seesaw effect” due to severe gradient conflicts; and 2) parameter-efficient methods struggle to isolate these task-specific conflicts while preserving the collaborative sharing of underlying general recommendation knowledge. To address these limitations, we propose Rec-MoELoRA, a hybrid fine-tuning framework that seamlessly integrates the mixture-of-experts architecture with low-rank adaptation. Specifically, we devise a soft-decoupling strategy that maintains the updates of most backbone network layers to continuously absorb globally shared knowledge, while exclusively introducing task-motivated low-rank experts into the self-attention layers for physical task isolation. Furthermore, to effectively overcome the prevalent “expert collapse” dilemma, we incorporate a dual regularization strategy of diversity and entropy. Combined with an asymmetric structural prior (e.g., allocating three experts for two tasks), this forces the model to spontaneously evolve a “Shared-Specific” representation pattern. Extensive experiments on real-world e-commerce datasets (Amazon Sports and Beauty) demonstrate that Rec-MoELoRA significantly outperforms the full fine-tuning baseline across core ranking and text generation metrics while introducing only a marginal number of additional parameters (e.g., HR@10 and BLEU-4 improve by 4.3% and 3.7% on the Sports dataset, respectively).</div></div>","PeriodicalId":100605,"journal":{"name":"High-Confidence Computing","volume":"6 2","pages":"Article 100404"},"PeriodicalIF":3.0,"publicationDate":"2026-06-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"148241605","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

What breaks embodied AI security: LLM vulnerabilities, CPS flaws, or something else? 是什么破坏了人工智能的安全性：LLM漏洞、CPS缺陷，还是其他什么？

IF 3

High-Confidence Computing Pub Date : 2026-06-01 Epub Date: 2026-05-28 DOI: 10.1016/j.hcc.2026.100403

Boyang Ma , Hechuan Guo , Peizhuo Lv , Minghui Xu , Xuelong Dai , Yechao Zhang , Yijun Yang , Yue Zhang

{"title":"What breaks embodied AI security: LLM vulnerabilities, CPS flaws, or something else?","authors":"Boyang Ma , Hechuan Guo , Peizhuo Lv , Minghui Xu , Xuelong Dai , Yechao Zhang , Yijun Yang , Yue Zhang","doi":"10.1016/j.hcc.2026.100403","DOIUrl":"10.1016/j.hcc.2026.100403","url":null,"abstract":"<div><div>Embodied AI systems (e.g., autonomous vehicles, service robots, and LLM-driven interactive agents) are rapidly transitioning from controlled environments to safety-critical real-world deployments. Unlike disembodied AI, failures in embodied intelligence lead to irreversible physical consequences, raising fundamental questions about security, safety, and reliability. While existing research predominantly analyzes embodied AI through the lenses of Large Language Model (LLM) vulnerabilities or classical Cyber–Physical System (CPS) failures, this survey argues that these perspectives are individually insufficient to explain many observed breakdowns in modern embodied systems. We posit that a significant class of failures arises from embodiment-induced system-level mismatches, rather than from isolated model flaws or traditional CPS attacks. Specifically, we identify four core insights that explain why embodied AI is fundamentally harder to secure: (i) semantic correctness does not imply physical safety, as language-level reasoning abstracts away geometry, dynamics, and contact constraints; (ii) identical actions can lead to drastically different outcomes across physical states due to nonlinear dynamics and state uncertainty; (iii) small errors propagate and amplify across tightly coupled perception–decision–action loops; and (iv) safety is not compositional across time or system layers, enabling locally safe decisions to accumulate into globally unsafe behavior. These insights suggest that securing embodied AI requires moving beyond component-level defenses toward system-level reasoning about physical risk, uncertainty, and failure propagation.</div></div>","PeriodicalId":100605,"journal":{"name":"High-Confidence Computing","volume":"6 2","pages":"Article 100403"},"PeriodicalIF":3.0,"publicationDate":"2026-06-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"148185146","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

A survey of decentralized key management 分散式密钥管理综述

IF 3

High-Confidence Computing Pub Date : 2026-06-01 Epub Date: 2026-04-15 DOI: 10.1016/j.hcc.2026.100393

Shuqi An , Xutong Chen , Shiyuan Xu , Yan Zhao , Xue Chen , Qiang Li , Zihao Zhuang , Yu Guo

{"title":"A survey of decentralized key management","authors":"Shuqi An , Xutong Chen , Shiyuan Xu , Yan Zhao , Xue Chen , Qiang Li , Zihao Zhuang , Yu Guo","doi":"10.1016/j.hcc.2026.100393","DOIUrl":"10.1016/j.hcc.2026.100393","url":null,"abstract":"<div><div>Traditional Centralized Key Management Systems (KMS) fundamentally conflict with the core philosophies advocated by decentralized storage systems, such as decentralization, censorship resistance, and user autonomy. Their inherent single points of failure and trust bottlenecks severely constrain overall system security. To systematically explore secure key management pathways without trusted third parties, this paper conducts a systematic review of research progress in the field of Decentralized Key Management (DKM). We propose an analytical framework covering four key stages: key generation, distributed distribution, dynamic rotation, and revocation verification. Furthermore, we distill four core evaluation dimensions supporting this framework: security, efficiency, dynamic adaptability, and hierarchical access control. Through the classification, comparison, and in-depth analysis of representative technical schemes, this paper aims to provide clear theoretical references and design guidelines for key security management in decentralized storage systems. Finally, this paper summarizes the major technical challenges currently facing the field and provides an outlook on future research directions.</div></div>","PeriodicalId":100605,"journal":{"name":"High-Confidence Computing","volume":"6 2","pages":"Article 100393"},"PeriodicalIF":3.0,"publicationDate":"2026-06-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"148241604","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

Pluggable AI-based real-time stragglers detection framework in Hadoop Hadoop中可插入的基于人工智能的实时掉队检测框架

IF 3

High-Confidence Computing Pub Date : 2026-03-01 Epub Date: 2025-07-03 DOI: 10.1016/j.hcc.2025.100341

Xinyuan Liu, Yinhao Li, Rajiv Ranjan, Devki Nandan Jha

{"title":"Pluggable AI-based real-time stragglers detection framework in Hadoop","authors":"Xinyuan Liu, Yinhao Li, Rajiv Ranjan, Devki Nandan Jha","doi":"10.1016/j.hcc.2025.100341","DOIUrl":"10.1016/j.hcc.2025.100341","url":null,"abstract":"<div><div>The growing reliance on big data frameworks such as Hadoop has revolutionized data processing across various domains, enabling large-scale storage and distributed computation. Hadoop is widely employed in real-world applications such as high-performance computation tasks, e-commerce and data analysis in healthcare. However, the efficiency of Hadoop systems is often hampered by faults and anomalies, with stragglers emerging as one of the most prevalent issues. Stragglers disrupt workflows, waste resources and degrade system performance. While existing anomaly detection models employ methods like median analysis or static thresholds, they often struggle with issues such as high false positives, lack of adaptability and poor handling of complex heterogeneous environments. To address these challenges, this paper presents <span>Plabs</span>, a flexible stragglers detection framework for Hadoop. The framework comprises two core components: (1) a Monitoring Module providing real-time tracking of cluster resources and task progress and (2) a Pluggable AI-based straggler detection module, designed for precise straggler task identification. By leveraging advanced monitoring and AI-driven analysis, <span>Plabs</span> offers an automated, flexible and scalable solution for detecting stragglers at run-time in Hadoop clusters. We evaluated <span>Plabs</span> exhaustively with three Machine Learning (ML), two Deep Learning (DL) and two Large Language Models (LLMs) on five different applications in a real testbed environment. Our experiment evaluation shows that DL models outperform others in identifying Hadoop stragglers, achieving superior accuracy and reliability for all the applications.</div></div>","PeriodicalId":100605,"journal":{"name":"High-Confidence Computing","volume":"6 1","pages":"Article 100341"},"PeriodicalIF":3.0,"publicationDate":"2026-03-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"146026197","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

CHPFL: Clustered adaptive hierarchical federated learning for edge-level personalization 边缘个性化的聚类自适应分层联邦学习

IF 3

High-Confidence Computing Pub Date : 2026-03-01 Epub Date: 2025-07-08 DOI: 10.1016/j.hcc.2025.100343

Lihua Song , Jing Li , Honglu Jiang , Shuhua Wei , Yufei Guo

{"title":"CHPFL: Clustered adaptive hierarchical federated learning for edge-level personalization","authors":"Lihua Song , Jing Li , Honglu Jiang , Shuhua Wei , Yufei Guo","doi":"10.1016/j.hcc.2025.100343","DOIUrl":"10.1016/j.hcc.2025.100343","url":null,"abstract":"<div><div>Federated learning faces challenges with non-IID data distributions, often resulting in suboptimal performance for individual clients with the global model. To address this issue, we propose a clustered hierarchical personalized federated learning (CHPFL) framework, which provides edge-level personalization to effectively overcomes non-IID data and alleviates the overfitting in the personalization process. The three-layer framework makes the learning and personalization process more feasible compared to traditional two-layer federated learning, as edge servers typically offer greater computing power and more efficient communication with the cloud server. Specifically, we use the K-Means++ clustering algorithm to group local clients based on their model updates, ensuring that clients with similar data distributions are clustered together and assigned to the same edge server. Each edge server then generates a personalized model by blending the global model with the edge model, which is adaptively updated and optimized through multiple iterations. Additionally, we introduce a novel aggregation rule on the cloud server to produce a global model with improved performance. Experiments on the MNIST, FMNIST, and KMNIST datasets demonstrate that CHPFL effectively overcomes non-IID data distribution and outperforms HPFL, APFL, and FedALA in non-IID settings.</div></div>","PeriodicalId":100605,"journal":{"name":"High-Confidence Computing","volume":"6 1","pages":"Article 100343"},"PeriodicalIF":3.0,"publicationDate":"2026-03-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"147394839","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

A novel zero-day ransomware detection approach based on CVAE and 1D-CNN 一种基于CVAE和1D-CNN的零日勒索软件检测方法

IF 3

High-Confidence Computing Pub Date : 2026-03-01 Epub Date: 2025-07-01 DOI: 10.1016/j.hcc.2025.100338

Bohan Cui , Yan Hu , Tianheng Qu , Yunhua He , Limin Sun

{"title":"A novel zero-day ransomware detection approach based on CVAE and 1D-CNN","authors":"Bohan Cui , Yan Hu , Tianheng Qu , Yunhua He , Limin Sun","doi":"10.1016/j.hcc.2025.100338","DOIUrl":"10.1016/j.hcc.2025.100338","url":null,"abstract":"<div><div>Ransomware has emerged as one of the most prevalent and destructive cyber attacks confronting global organizations. By locking critical devices or encrypting essential data and then demanding payment for restoration, ransomware attacks disrupt operations, result in significant financial losses, and damage organizational reputations. In particular, zero-day ransomware attacks, which attempt to exploit previously unknown vulnerabilities, pose a severe threat to existing cyber security solutions. Due to the lack of training data, detection of zero-day ransomware attacks remains a significant challenge. This paper proposes a novel zero-day ransomware detection framework that integrates a refined Conditional Variational Autoencoder (CVAE) with a 1D Convolutional Neural Network (1D-CNN). The encoder of the CVAE model comprises a posterior network and a parallel prior network. Using variational coding, the posterior network maps behavioral features of software samples from known families into a latent space, represented by a fixed multivariate Gaussian distribution with a diagonal covariance matrix. Simultaneously, the prior network eliminates dependency on class labels while maintaining distributional consistency with the posterior network via Kullback–Leibler (KL) divergence minimization. This dual-network structure enables unified latent space mapping for both labeled and unlabeled samples, effectively narrowing distributional discrepancies between software samples from known and unknown families. The harmonized latent representations subsequently enhance the discriminative capability of the 1D-CNN classifier in detecting zero-day ransomware. The comprehensive experimental results have verified that the proposed method can effectively detect zero-day ransomware attacks.</div></div>","PeriodicalId":100605,"journal":{"name":"High-Confidence Computing","volume":"6 1","pages":"Article 100338"},"PeriodicalIF":3.0,"publicationDate":"2026-03-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145618667","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

KANs-DETR: Enhancing Detection Transformer with Kolmogorov–Arnold Networks for small object kan - detr：基于Kolmogorov-Arnold网络的小目标增强检测变压器

IF 3

High-Confidence Computing Pub Date : 2026-03-01 Epub Date: 2025-07-01 DOI: 10.1016/j.hcc.2025.100336

Jingyu Zhang , Wentao Peng , Anyan Xiao , Tao Liu , Junchao Fu , Jian Chen , Zhuo Yan

引用次数: 0

Securing educational LLMs: A generalised taxonomy of attacks on LLMs and DREAD risk assessment 保护教育法学硕士：法学硕士攻击的一般分类和恐惧风险评估

IF 3

High-Confidence Computing Pub Date : 2026-03-01 Epub Date: 2025-11-04 DOI: 10.1016/j.hcc.2025.100371

Farzana Zahid , Anjalika Sewwandi , Lee Brandon , Vimal Kumar , Roopak Sinha

{"title":"Securing educational LLMs: A generalised taxonomy of attacks on LLMs and DREAD risk assessment","authors":"Farzana Zahid , Anjalika Sewwandi , Lee Brandon , Vimal Kumar , Roopak Sinha","doi":"10.1016/j.hcc.2025.100371","DOIUrl":"10.1016/j.hcc.2025.100371","url":null,"abstract":"<div><div>Due to perceptions of efficiency and significant productivity gains, various organisations, including in education, are adopting Large Language Models (LLMs) into their workflows. Educator-facing, learner-facing, and institution-facing LLMs, collectively, Educational Large Language Models (eLLMs), complement and enhance the effectiveness of teaching, learning, and academic operations. However, their integration into an educational setting raises significant cybersecurity concerns. A comprehensive landscape of contemporary attacks on LLMs and their impact on the educational environment is missing. This study presents a generalised taxonomy of fifty attacks on LLMs, which are categorised as attacks targeting either models or their infrastructure. The severity of these attacks is evaluated in the educational sector using the DREAD risk assessment framework. Our risk assessment indicates that token smuggling, adversarial prompts, direct injection, and multi-step jailbreak are critical attacks on eLLMs. The proposed taxonomy, its application in the educational environment, and our risk assessment will help academic and industrial practitioners to build resilient solutions that protect learners and institutions.</div></div>","PeriodicalId":100605,"journal":{"name":"High-Confidence Computing","volume":"6 1","pages":"Article 100371"},"PeriodicalIF":3.0,"publicationDate":"2026-03-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"147394842","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

Is the metaverse really coming to fruition? A survey of applied metaverse and extended reality 超宇宙真的要开花结果了吗？应用元现实与扩展现实综述

IF 3

High-Confidence Computing Pub Date : 2026-03-01 Epub Date: 2025-12-05 DOI: 10.1016/j.hcc.2025.100376

Zhiguo Liu , Yan Huang , Junyu Mai , Wei Li , Zhipeng Cai , Yingshu Li

{"title":"Is the metaverse really coming to fruition? A survey of applied metaverse and extended reality","authors":"Zhiguo Liu , Yan Huang , Junyu Mai , Wei Li , Zhipeng Cai , Yingshu Li","doi":"10.1016/j.hcc.2025.100376","DOIUrl":"10.1016/j.hcc.2025.100376","url":null,"abstract":"<div><div>This survey examines the current state of the Metaverse, encompassing its fundamental concepts, technological framework, practical applications, and user experience to evaluate its stage of development. This paper reviews the core concepts of the Metaverse and Extended Reality (XR) and evaluates the latest advancements in hardware and software technologies. Furthermore, it examines the Metaverse’s typical applications in four key domains: education, training, medicine, and mixed life, while summarizing user feedback to identify its advantages and challenges. The feedback indicates that the Metaverse offers notable benefits, including immersive experiences, enhanced training effectiveness, cost efficiency, and improved safety. However, significant challenges remain, such as hardware performance limitations, software inefficiencies, user discomfort, health risks, and social and ethical concerns. Our analysis suggests that while the Metaverse has yet to reach full maturity, it holds great potential for future development. To further advance the field, this paper highlights key research priorities in artificial intelligence(AI), quantum computing, and social governance, providing insights for future studies.</div></div>","PeriodicalId":100605,"journal":{"name":"High-Confidence Computing","volume":"6 1","pages":"Article 100376"},"PeriodicalIF":3.0,"publicationDate":"2026-03-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"147394843","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0