High-Confidence Computing最新文献_第3页

Performance evaluation of file operations using Mutagen 使用Mutagen进行文件操作的性能评估

IF 3.2

High-Confidence Computing Pub Date : 2025-01-15 DOI: 10.1016/j.hcc.2024.100282

Mahid Atif Hosain , Sriram Chellappan , Jannatun Noor

引用次数: 0

An insider threat detection method based on improved Test-Time Training model 一种基于改进Test-Time Training模型的内部威胁检测方法

IF 3.2

High-Confidence Computing Pub Date : 2025-01-14 DOI: 10.1016/j.hcc.2024.100283

Xiaoling Tao , Jianxiang Liu , Yuelin Yu , Haijing Zhang , Ying Huang

{"title":"An insider threat detection method based on improved Test-Time Training model","authors":"Xiaoling Tao , Jianxiang Liu , Yuelin Yu , Haijing Zhang , Ying Huang","doi":"10.1016/j.hcc.2024.100283","DOIUrl":"10.1016/j.hcc.2024.100283","url":null,"abstract":"<div><div>As network and information systems become widely adopted across industries, cybersecurity concerns have grown more prominent. Among these concerns, insider threats are considered particularly covert and destructive. Insider threats refer to malicious insiders exploiting privileged access to networks, systems, and data to intentionally compromise organizational security. Detecting these threats is challenging due to the complexity and variability of user behavior data, combined with the subtle and covert nature of insider actions. Traditional detection methods often fail to capture both long-term dependencies and short-term fluctuations in time-series data, which are crucial for identifying anomalous behaviors. To address these issues, this paper introduces the Test-Time Training (TTT) model for the first time in the field of insider threat detection, and proposes a detection method based on the TTT-ECA-ResNet model. First, the dataset is preprocessed. TTT is applied to extract long-term dependencies in features, effectively capturing dynamic sequence changes. The Residual Network, incorporating the Efficient Channel Attention mechanism, is used to extract local feature patterns, capturing relationships between different positions in time-series data. Finally, a Linear layer is employed for more precise detection of insider threats. The proposed approaches were evaluated using the CMU CERT Insider Threat Dataset, achieving an AUC of 98.75% and an F1-score of 96.81%. The experimental results demonstrate the effectiveness of the proposed methods, outperforming other state-of-the-art approaches.</div></div>","PeriodicalId":100605,"journal":{"name":"High-Confidence Computing","volume":"5 1","pages":"Article 100283"},"PeriodicalIF":3.2,"publicationDate":"2025-01-14","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143422030","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

Less leakage and more precise: Efficient wildcard keyword search over encrypted data 更少的泄漏和更精确：有效的通配符关键字搜索加密数据

IF 3.2

High-Confidence Computing Pub Date : 2025-01-11 DOI: 10.1016/j.hcc.2025.100297

Yunling Wang , Chenyang Gao , Yifei Huang , Lei Fu , Yong Yu

{"title":"Less leakage and more precise: Efficient wildcard keyword search over encrypted data","authors":"Yunling Wang , Chenyang Gao , Yifei Huang , Lei Fu , Yong Yu","doi":"10.1016/j.hcc.2025.100297","DOIUrl":"10.1016/j.hcc.2025.100297","url":null,"abstract":"<div><div>Wildcard searchable encryption allows the server to efficiently perform wildcard-based keyword searches over encrypted data while maintaining data privacy. A promising solution to achieve wildcard SSE is to extract the characteristics of the queried keyword and check the existence based on a membership test structure. However, existing schemes have false positives of character order, that is, the server cannot identify the order between the first and the last wildcard character. Besides, the schemes also suffer from characteristic matching pattern leakage due to the one-by-one membership testing. In this paper, we present the first efficient wildcard SSE scheme to eliminate the false positives of character order and characteristic matching pattern leakage. To this end, we design a novel characteristic extraction technique that enables the client to exact the characteristics of the queried keyword maintaining the order between the first and the last wildcard character. Then, we utilize the primitive of Symmetric Subset Predicate Encryption, which supports checking if one set is a subset of another in one shot to reduce the characteristic matching pattern leakage. Finally, by performing a formal security analysis and implementing the scheme on a real-world database, we demonstrate that the desired security properties are achieved with high performance.</div></div>","PeriodicalId":100605,"journal":{"name":"High-Confidence Computing","volume":"5 3","pages":"Article 100297"},"PeriodicalIF":3.2,"publicationDate":"2025-01-11","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144678976","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

Secure and trusted sharing mechanism of private data for Internet of Things 安全可信的物联网私有数据共享机制

IF 3.2

High-Confidence Computing Pub Date : 2024-12-24 DOI: 10.1016/j.hcc.2024.100273

Mengyuan Li , Shaoyong Guo , Wenjing Li , Ao Xiong , Xiaoming Zhou , Jun Qi , Feng Qi , Dong Wang , Da Li

{"title":"Secure and trusted sharing mechanism of private data for Internet of Things","authors":"Mengyuan Li , Shaoyong Guo , Wenjing Li , Ao Xiong , Xiaoming Zhou , Jun Qi , Feng Qi , Dong Wang , Da Li","doi":"10.1016/j.hcc.2024.100273","DOIUrl":"10.1016/j.hcc.2024.100273","url":null,"abstract":"<div><div>In recent years, the rapid development of Internet of Things (IoT) technology has led to a significant increase in the amount of data stored in the cloud. However, traditional IoT systems rely primarily on cloud data centers for information storage and user access control services. This practice creates the risk of privacy breaches on IoT data sharing platforms, including issues such as data tampering and data breaches. To address these concerns, blockchain technology, with its inherent properties such as tamper-proof and decentralization, has emerged as a promising solution that enables trusted sharing of IoT data. Still, there are challenges to implementing encrypted data search in this context. This paper proposes a novel searchable attribute cryptographic access control mechanism that facilitates trusted cloud data sharing. Users can use keywords To efficiently search for specific data and decrypt content keys when their properties are consistent with access policies. In this way, cloud service providers will not be able to access any data privacy-related information, ensuring the security and trustworthiness of data sharing, as well as the protection of user data privacy. Our simulation results show that our approach outperforms existing studies in terms of time overhead. Compared to traditional access control schemes,our approach reduces data encryption time by 33%, decryption time by 5%, and search time by 75%.</div></div>","PeriodicalId":100605,"journal":{"name":"High-Confidence Computing","volume":"5 2","pages":"Article 100273"},"PeriodicalIF":3.2,"publicationDate":"2024-12-24","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143902136","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

Kubernetes application performance benchmarking on heterogeneous CPU architecture: An experimental review Kubernetes应用程序在异构CPU架构上的性能基准测试：实验回顾

IF 3.2

High-Confidence Computing Pub Date : 2024-12-18 DOI: 10.1016/j.hcc.2024.100276

Jannatun Noor, MD Badsha Faysal, MD Sheikh Amin, Bushra Tabassum, Tamim Raiyan Khan, Tanvir Rahman

{"title":"Kubernetes application performance benchmarking on heterogeneous CPU architecture: An experimental review","authors":"Jannatun Noor, MD Badsha Faysal, MD Sheikh Amin, Bushra Tabassum, Tamim Raiyan Khan, Tanvir Rahman","doi":"10.1016/j.hcc.2024.100276","DOIUrl":"10.1016/j.hcc.2024.100276","url":null,"abstract":"<div><div>With the rapid advancement of cloud technologies, cloud services have enormously contributed to the cloud community for application development life-cycle. In this context, Kubernetes has played a pivotal role as a cloud computing tool, enabling developers to adopt efficient and automated deployment strategies. Using Kubernetes as an orchestration tool and a cloud computing system as a manager of the infrastructures, developers can boost the development and deployment process. With cloud providers such as GCP, AWS, Azure, and Oracle offering Kubernetes services, the availability of both x86 and ARM platforms has become evident. However, while x86 currently dominates the market, ARM-based solutions have seen limited adoption, with only a few individuals actively working on ARM deployments. This study explores the efficiency and cost-effectiveness of implementing Kubernetes on different CPU platforms. By comparing the performance of x86 and ARM platforms, this research seeks to ascertain whether transitioning to ARM presents a more advantageous option for Kubernetes deployments. Through a comprehensive evaluation of scalability, cost, and overall performance, this study aims to shed light on the viability of leveraging ARM on different CPUs by providing valuable insights.</div></div>","PeriodicalId":100605,"journal":{"name":"High-Confidence Computing","volume":"5 1","pages":"Article 100276"},"PeriodicalIF":3.2,"publicationDate":"2024-12-18","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143403423","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

Scale-aware Gaussian mixture loss for crowd localization transformers 群体定位变压器的尺度感知高斯混合损耗

IF 3.2

High-Confidence Computing Pub Date : 2024-12-10 DOI: 10.1016/j.hcc.2024.100296

Alabi Mehzabin Anisha, Sriram Chellappan

{"title":"Scale-aware Gaussian mixture loss for crowd localization transformers","authors":"Alabi Mehzabin Anisha, Sriram Chellappan","doi":"10.1016/j.hcc.2024.100296","DOIUrl":"10.1016/j.hcc.2024.100296","url":null,"abstract":"<div><div>A fundamental problem in crowd localization using computer vision techniques stems from intrinsic scale shifts. Scale shifts occur when the crowd density within an image is uneven and chaotic, a feature common in dense crowds. At locations nearer to the camera, crowd density is lower than those farther away. Consequently, there is a significant change in the number of pixels representing a person across locations in an image depending on the camera’s position. Existing crowd localization methods do not effectively handle scale shifts, resulting in relatively poor performance in dense crowd images. In this paper, we explicitly address this challenge. Our method, called Gaussian Loss Transformers (GLT), directly incorporates scale variants in crowds by adapting loss functions to handle them in the end-to-end training pipeline. To inform the model about the scale variants within the crowd, we utilize a Gaussian mixture model (GMM) for pre-processing the ground truths into non-overlapping clusters. This cluster information is utilized as a weighting factor while computing the localization loss for that cluster. Extensive experiments on state-of-the-art datasets and computer vision models reveal that our method improves localization performance in dense crowd images. We also analyze the effect of multiple parameters in our technique and report findings on their impact on crowd localization performance.</div></div>","PeriodicalId":100605,"journal":{"name":"High-Confidence Computing","volume":"5 3","pages":"Article 100296"},"PeriodicalIF":3.2,"publicationDate":"2024-12-10","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144678974","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

Erratum to “Exploring Personalized Internet of Things (PIoT), social connectivity, and Artificial Social Intelligence (ASI): A survey” [High-Confidence Computing 4 (2024) 100242] “探索个性化物联网（PIoT）、社会连接和人工社会智能（ASI）：一项调查”的勘误[高置信度计算4 (2024)100242]

IF 3.2

High-Confidence Computing Pub Date : 2024-12-01 DOI: 10.1016/j.hcc.2024.100294

Bisma Gulzar , Shabir Ahmad Sofi , Sahil Sholla

引用次数: 0

Connectivity maintenance against link uncertainty and heterogeneity in adversarial networks 对抗网络中链路不确定性和异质性的连通性维护

IF 3.2

High-Confidence Computing Pub Date : 2024-11-28 DOI: 10.1016/j.hcc.2024.100293

Jianzhi Tang , Luoyi Fu , Lei Zhou , Xinbing Wang , Chenghu Zhou

{"title":"Connectivity maintenance against link uncertainty and heterogeneity in adversarial networks","authors":"Jianzhi Tang , Luoyi Fu , Lei Zhou , Xinbing Wang , Chenghu Zhou","doi":"10.1016/j.hcc.2024.100293","DOIUrl":"10.1016/j.hcc.2024.100293","url":null,"abstract":"<div><div>This paper delves into the challenge of maintaining connectivity in adversarial networks, focusing on the preservation of essential links to prevent the disintegration of network components under attack. Unlike previous approaches that assume a stable and homogeneous network topology, this study introduces a more realistic model that incorporates both link uncertainty and heterogeneity. Link uncertainty necessitates additional probing to confirm link existence, while heterogeneity reflects the varying resilience of links against attacks. We model the network as a random graph where each link is defined by its existence probability, probing cost, and resilience. The primary objective is to devise a defensive strategy that maximizes the expected size of the largest connected component at the end of an adversarial process while minimizing the probing cost, irrespective of the attack patterns employed. We begin by establishing the NP-hardness of the problem and then introduce an optimal defensive strategy based on dynamic programming. Due to the high computational cost of achieving optimality, we also develop two approximate strategies that offer efficient solutions within polynomial time. The first is a heuristic method that assesses link importance across three heterogeneous subnetworks, and the second is an adaptive minimax policy designed to minimize the defender’s potential worst-case loss, with guaranteed performance. Through extensive testing on both synthetic and real-world datasets across various attack scenarios, our strategies demonstrate significant advantages over existing methods.</div></div>","PeriodicalId":100605,"journal":{"name":"High-Confidence Computing","volume":"5 3","pages":"Article 100293"},"PeriodicalIF":3.2,"publicationDate":"2024-11-28","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144678975","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

Redactable Blockchain from Accountable Weight Threshold Chameleon Hash 可读的区块链从可问责权重阈值变色龙哈希

IF 3.2

High-Confidence Computing Pub Date : 2024-11-08 DOI: 10.1016/j.hcc.2024.100281

Qiang Ma , Yanqi Zhao , Xiangyu Liu , Xiaoyi Yang , Min Xie , Yong Yu

{"title":"Redactable Blockchain from Accountable Weight Threshold Chameleon Hash","authors":"Qiang Ma , Yanqi Zhao , Xiangyu Liu , Xiaoyi Yang , Min Xie , Yong Yu","doi":"10.1016/j.hcc.2024.100281","DOIUrl":"10.1016/j.hcc.2024.100281","url":null,"abstract":"<div><div>The redactable blockchain provides the editability of blocks, which guarantees the data immutability of blocks while removing illegal content on the blockchain. However, the existing redactable blockchain relies on trusted assumptions regarding a single editing authority. Ateniese et al. (EuroS&P 2017) and Li et al. (TIFS 2023) proposed solutions by using threshold chameleon hash functions, but these lack accountability for malicious editing. This paper delves into this problem and proposes an accountability weight threshold blockchain editing scheme. Specifically, we first formalize the model of a redactable blockchain with accountability. Then, we introduce the novel concept of the Accountable Weight Threshold Chameleon Hash Function (AWTCH). This function collaboratively generates a chameleon hash trapdoor through a weight committee protocol, where only sets of committees meeting the weight threshold can edit data. Additionally, it incorporates a tracer to identify and hold accountable any disputing editors, thus enabling supervision of editing rights. We propose a generic construction for AWTCH. Then, we introduce an efficient construction of AWTCH and develop a redactable blockchain scheme by leveraging AWTCH. Finally, we demonstrate our scheme’s practicality. The editing efficiency of our scheme is twice that of Tian et al. (TIFS 2023) with the same number of editing blocks.</div></div>","PeriodicalId":100605,"journal":{"name":"High-Confidence Computing","volume":"5 3","pages":"Article 100281"},"PeriodicalIF":3.2,"publicationDate":"2024-11-08","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144713671","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

Three-dimensional dynamic gesture recognition method based on convolutional neural network 基于卷积神经网络的三维动态手势识别方法

IF 3.2

High-Confidence Computing Pub Date : 2024-11-06 DOI: 10.1016/j.hcc.2024.100280

Ji Xi , Weiqi Zhang , Zhe Xu , Saide Zhu , Linlin Tang , Li Zhao

{"title":"Three-dimensional dynamic gesture recognition method based on convolutional neural network","authors":"Ji Xi , Weiqi Zhang , Zhe Xu , Saide Zhu , Linlin Tang , Li Zhao","doi":"10.1016/j.hcc.2024.100280","DOIUrl":"10.1016/j.hcc.2024.100280","url":null,"abstract":"<div><div>With the rapid advancement of virtual reality, dynamic gesture recognition technology has become an indispensable and critical technique for users to achieve human–computer interaction in virtual environments. The recognition of dynamic gestures is a challenging task due to the high degree of freedom and the influence of individual differences and the change of gesture space. To solve the problem of low recognition accuracy of existing networks, an improved dynamic gesture recognition algorithm based on ResNeXt architecture is proposed. The algorithm employs three-dimensional convolution techniques to effectively capture the spatiotemporal features intrinsic to dynamic gestures. Additionally, to enhance the model’s focus and improve its accuracy in identifying dynamic gestures, a lightweight convolutional attention mechanism is introduced. This mechanism not only augments the model’s precision but also facilitates faster convergence during the training phase. In order to further optimize the performance of the model, a deep attention submodule is added to the convolutional attention mechanism module to strengthen the network’s capability in temporal feature extraction. Empirical evaluations on EgoGesture and NvGesture datasets show that the accuracy of the proposed model in dynamic gesture recognition reaches 95.03% and 86.21%, respectively. When operating in RGB mode, the accuracy reached 93.49% and 80.22%, respectively. These results underscore the effectiveness of the proposed algorithm in recognizing dynamic gestures with high accuracy, showcasing its potential for applications in advanced human–computer interaction systems.</div></div>","PeriodicalId":100605,"journal":{"name":"High-Confidence Computing","volume":"5 1","pages":"Article 100280"},"PeriodicalIF":3.2,"publicationDate":"2024-11-06","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143387521","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