检索结果-内蒙古大学图书馆

Privacy-Enhanced Role-Based Access Control for IoT Systems

IEEE Internet of Things Journal 2025年

作者： Zhang, Yawei Chen, Lanxiang Zhu, Yizhao Fujian Normal University Fujian Provincial Key Laboratory of Network Security and Cryptology College of Computer and Cyber Security Fuzhou350117 China City University of Macau Faculty of Data Science China

Unauthorized accessing private information poses a significant security risk in IoT. Although role-based access control can partially address the problem, there is a lack of studies on protecting the privacy of roles. This work proposes a novel privacy-enhanced role-based access control (PE-RBAC) scheme for resource-limited cloud-based IoT systems. Our PE-RBAC scheme enhances the privacy of traditional RBAC by allowing the cloud platform (CP) to learn only the necessary roles of IoT devices rather than their entire role sets. IoT devices remain unaware of the cloud platform’s roles through the incorporation of the authorized private set intersection (APSI) protocol. Our scheme enables role authorization to prevent unauthorized requests by integrating an authorization center (AC) with an authentication scheme. It achieves lightweight computation and communication overhead using a garbled Bloom filter (GBF), independent of IoT roles. It requires only XOR operations and random number generation, making it ideal for IoT devices with limited computing capacity and bandwidth. Through formal security model definitions and rigorous security analysis, it achieves unlinkability and ciphertext indistinguishability for IoT device privacy and unforgeability for both the cloud platform and IoT devices. It demonstrates high practicality through real-world-like experiments considering varying role sizes and the number of concurrent IoT devices. Experimental results show a throughput of 1,100 requests per second, with storage overhead remaining linear with the number of devices. © 2014 IEEE.

关键词： Cloud platforms

来源：评论

学校读者我要写书评

暂无评论

Unveiling the Superiority of Unsupervised Learning on GPU Cryptojacking Detection: Practice on Magnetic Side Channel-based Mechanism

引用

IEEE Transactions on Information Forensics and security 2025年 20卷 4874-4889页

作者： Cao, Hangcheng Xu, Guowen He, Ziyang Shi, Shaoqing Xu, Shengmin Wu, Cong Ning, Jianting Hunan University College of Computer Science and Electronic Engineering China City University of Hong Kong Department of Computer Science Hong Kong University of Electronic Science and Technology of China School of Computer Science and Engineering Chengdu China Zhengzhou University School of Cyber Science and Engineering Zhengzhou China Zhengzhou University International College Zhengzhou China Fujian Provincial Key Laboratory of Network Security and Cryptology College of Computer and Cyber Security Fuzhou China Nanyang Technological University College of Computing and Data Science Singapore Wuhan University School of Cyber Science and Engineering Wuhan China City University of Macau Faculty of Data Science China

Ample profits of GPU cryptojacking attract hackers to recklessly invade victims’ devices, for completing specific cryptocurrency mining tasks. Such malicious invasion undoubtedly obstructs normal device usage and wastes computation resources. To resist the threat of GPU cryptojacking, existing works aim to timely detect and clear away it, by distinguishing the dissimilitude between it and legitimate applications. However, these detection mechanisms inappropriately rely on two conflict cornerstones, manifested in leveraging mutable samples of illegitimate cryptojacking to design supervision-based detection models requiring samples with stable patterns. This limitation compromises the practicability of existing detection mechanisms in the face of mutable cryptojacking samples. To fill the gap, we explore the superiority of unsupervised learning in handling this issue and further propose an unsupervised manner-enabled detection mechanism named MagInspector, only using legitimate applications’ magnetic signatures from GPU side channels for model construction. MagInspector innovates in training an unsupervised autoencoder network by an adversarial mode that well learns the stable signature patterns of legitimate applications, while incompatible with mutable cryptojacking ones. In the process of model training, we elaborately extract mutual energy cumulation distribution features to represent legitimate applications to overcome the impact of their inter-type differences. Meanwhile, a locality sensitive hashing-driven outlier removal algorithm is designed to enhance MagInspector’s robustness to the noise samples. Finally, extensive experiments are conducted on GPUs covering four generations of common NVIDIA architectures and two generations of AMD architectures;the results show that applying MagInspector to mutable cryptojacking signature detection achieves a significant average accuracy improvement of 25.5% and 17.8%, respectively. © 2005-2012 IEEE.

关键词： Unsupervised learning

来源：评论

学校读者我要写书评

暂无评论

A circuit area optimization of MK-3 S-box

引用

Cybersecurity 2025年第1期8卷 61-71页

作者： Yanjun Li Weiguo Zhang Yiping Lin Jian Zou Jian Liu Information Industry Information Security Evaluation Center The 15th Research Institute of China Electronic Technology Group CorporationBeijing 100083China Henan Key Laboratory of Network Cryptography Technology Zhengzhou 450001China Beijing Electronic Science and Technology Institute No.7 Fufeng RoadFengtai DistricBeijing 100083China College of Computer and Data Science Fuzhou UniversityFuzhou 350108China

In MILCOM 2015,Kelly et *** the authentication encryption algorithm MK-3,which applied the 16-bit *** paper aims to implement the 16-bit S-box with less circuit ***,we classifed the irreducible polynomials over F_(2n)into three *** we compared the logic gates required for multiplication over the fnite feld constructed by the three types of irreducible *** to the comparison result,we constructed the composite felds, F_((2^(4))^(2))and F_((2^(8))^(2)).Based on the isomorphism of fnite felds,the operations over F_(2)^(16)can be conducted over F_((2^(8))^(2)).Similarly,elements over F28 can be mapped to the corresponding elements over F_((2^(4)))^(2).Next,the SAT solver was used to optimize the operations over smaller feld F_(2)^(4).At last,the architecture of the optimized MK-3 S-box was worked *** with the implementation proposed by the original designer,the circuit area of the MK-3 S-box in this paper is reduced by at least 55.9%.

关键词： irreducible operations optimization

来源：评论

学校读者我要写书评

暂无评论

引用

8th International Congress on Edge Computing, EDGE 2024, Held as Part of the Services Conference Federation, SCF 2024

作者： Tian, Yun Wu, Bin Shi, Jiaoli Zhang, Caicai Xu, Du School of Computer and Big Data Science Jiujiang University Jiujiang332005 China Jiujiang Key Laboratory of Network and Information Security Jiujiang332005 China School of Modern Information Technology Zhejiang Polytechnic University of Mechanical and Electrical Engineering Hangzhou310053 China

ISBN: (纸本)9783031770685

The development of cloud computing and the widespread application of cloud services have made outsourcing services more convenient. The need for individuals and businesses to store and manipulate the graph data they generate is growing rapidly. The unreliability and insecurity of cloud servers make outsourcing graph data a great risk of information leakage. To effectively protect data security, encrypting outsourced data is a useful method. The adjacent vertex query is a very commonly used and fundamental operation, and similarity search is a widely used and powerful tool to improve the scope and functionality of queries. After outsourcing encrypted sparse graph data to cloud servers, it becomes very inconvenient to use and manipulate the data. In this work, we present a scheme to realize the adjacent vertex query supporting similarity search on sparse graph data in cloud environment (SSAQ), which also protects the security of the information. This work uses edit distance and the searchable encryption principle to construct query index, and next implement the similar adjacent vertex query on cloud server. This work provides a formal security analysis, and also gives the experimental comparison and analysis. © The Author(s), under exclusive license to Springer Nature Switzerland AG 2025.

关键词： Information leakage

来源：评论

学校读者我要写书评

暂无评论

Get the Punchline: Headline-Centric Multi-View Learning for Personalized Headline Generation

IEEE Transactions on Audio, Speech and Language Processing

引用

IEEE Transactions on Audio, Speech and Language Processing 2025年 33卷 962-973页

作者： Wenya Guo Shenglong Yu Ying Zhang Zhengkun Zhang Xiaojie Yuan College of Computer Science Nankai University Tianjin China Tianjin Key Laboratory of Network and Data Security Technology College of Cyber Science Nankai University Tianjin China

Personalized news headline generation aims to generate a user-specific headline based on their reading histories. Existing approaches incorporate information from various aspects of news articles, which leads to a lack of focus on news headlines and fails to capture accurate information for guiding headline generation. As a result, the generated headlines lack differences across users and fail to reflect user-specific information. To address this problem, we propose a Headline-centric Multi- view Headline Generation network, denoted as HMvNet. HMvNet is an extraction-integration method that centers around the punchline, i.e. the user-specific information contained in history headlines. HMvNet extracts user-preferred entity tokens and syntactic structures from history headlines through the token-level view and the sequence-level view, respectively. Besides, we propose a reinforcement learning method specifically designed for personalized headline generation to train HMvNet. This method allows the model to fully leverage extracted information and ensures the fluency of the generated headlines. Experimental results demonstrate the superiority of the proposed personalization modeling method as well as the reinforcement learning framework.

关键词： History data mining Syntactics Reinforcement learning Recommender systems Generators Training User preference Speech processing Hybrid power systems

来源：评论

学校读者我要写书评

暂无评论

Understanding User Passwords through Parsing Tree

引用

IEEE Transactions on Dependable and Secure Computing 2025年

作者： Wang, Ding Shan, Xuan Wu, Yuxuan Jia, Chunfu Nankai University College of Cryptology and Cyber Science Tianjin300350 China Ministry of Education of China Henan Key Laboratory of Network Cryptography Key Laboratory of Cyberspace Security China Nankai University Tianjin Key Laboratory of Network and Data Security Technology Tianjin300350 China

Passwords are today's dominant form of authentication, and password guessing is the most effective method for evaluating password strength. Most password guessing models (e.g., PCFG, Markov, and RFGuess) regard passwords as sequences composed of basic units (i.e., characters/segments), with the information flow being unidirectional (i.e., predicting the next unit based on the preceding units). However, modeling passwords in a single direction fails to capture users' password creation behavior that is actually impacted by the full password context. Through an in-depth analysis of real-world passwords, we reveal that users often create passwords around a central keyword, like common words, names, or dates, and then embellish them with numbers or symbols. Based on this observation, we, for the first time, attempt to parse passwords as trees. Unlike existing sequence models, trees can reveal the semantic connections within passwords and the logical thought processes users follow when creating passwords. For instance, in the password iloveyou, the basic units i and you are semantically dependent on the predicate love, forming a natural tree structure. We propose a trawling guessing model called PassTree and a targeted guessing model based on personally identifiable information (PII), named PassTree-PII. Our extensive experiments demonstrate the effectiveness of our models: (1) PassTree outperforms its leading counterparts by 0.38%-2.51% when guessing numbers are below 107 (2) PassTree-PII achieves a cracking rate comparable to the state-of-the-art RFGuess-PII proposed in USENIX security'23, but operates significantly more efficiently, using only 0.87% of the memory and being 16.60 times faster. Our work provides a new perspective on understanding user passwords and demonstrates a feasible technical route of applying tree structures to password guessing. © 2025 IEEE.

关键词： Markov processes

来源：评论

学校读者我要写书评

暂无评论

POINTERGUESS: targeted password guessing model using pointer mechanism 24

POINTERGUESS: targeted password guessing model using pointer...

引用

Proceedings of the 33rd USENIX Conference on security Symposium

作者： Kedong Xiu Ding Wang College of Cyber Science Nankai University Tianjin China and Key Laboratory of Data and Intelligent System Security (NKU) Ministry of Education Tianjin China and Tianjin Key Laboratory of Network and Data Security Technology Nankai University Tianjin China

ISBN: (纸本)9781939133441

Most existing targeted password guessing models view users' reuse behaviors as sequences of edit operations (e.g., insert and delete) performed on old passwords. These atomic edit operations are limited to modifying one character at a time and cannot fully cover users' complex password modification behaviors (e.g., modifying the password structure). This partially leads to a significant gap between the proportion of users' reused passwords and the success rates that existing targeted password models can achieve. To fill this gap, this paper models users' reuse behaviors by focusing on two key components: (1) What they want to copy/keep; (2) What they want to tweak. More specifically, we introduce the pointer mechanism and propose a new targeted guessing model, namely POINTERGUESS. By hierarchically redefining password reuse from both personal and population-wide perspectives, we can accurately and comprehensively characterize users' password reuse behaviors. Moreover, we propose MS-POINTERGUESS, which can employ the victim's multiple leaked *** employing 13 large-scale real-world password datasets, we demonstrate that POINTERGUESS is effective: (1) When the victim's password at site A (namely pwA) is known, within 100 guesses, the average success rate of POINTERGUESS in guessing her password at site B (namely pwB, pwA ≠ pwB) is 25.21% (for common users) and 12.34% (for security-savvy users), respectively, which is 21.23%~71.54% (38.37% on average) higher than its foremost counterparts; (2) When not excluding identical password pairs (i.e., pwA can equal pwB), within 100 guesses, the average success rate of POINTERGUESS is 48.30% (for common users) and 28.42% (for security-savvy users), respectively, which is 6.31%~15.92% higher than its foremost counterparts; (3) Within 100 guesses, the MS-POINTERGUESS further improves the cracking success rate by 31.21% compared to POINTERGUESS.

关键词：

来源：评论

学校读者我要写书评

暂无评论

DSDIR: A Two-Stage Method for Addressing Noisy Long-Tailed Problems in Malicious Traffic Detection

DSDIR: A Two-Stage Method for Addressing Noisy Long-Tailed P...

引用

International Conference on Acoustics, Speech, and Signal Processing (ICASSP)

作者： Guoliang Li Ruiqi Zhang Zhe Sun Lingkai Xing Yu Zhang College of Cyber Science Nankai University China Tianjin Key Laboratory of Network and Data Security Technology China Key Laboratory of Data and Intelligent System Security Ministry of Education China College of Computer Science Nankai University China

ISBN: (数字)9798350368741

ISBN: (纸本)9798350368758

In recent years, deep learning based malicious traffic detection (MTD) systems have demonstrated remarkable success. However, their effectiveness tend to decrease because most malicious traffic datasets are suffered from noisy-labeled and long-tailed problems. While numerous approaches have been developed to address these two problems individually, they become inefficient when confronting the combined challenge of noisy long-tailed data, as they typically tackle only a single adverse factor at a time. This paper proposes a two-stage method called Distribution-aware sample Selection and Dynamic Instance-based Relabeling (DSDIR), which simultaneously addresses the impacts of noisy-labeled and long-tailed problems. In the first stage, a noise-independent clean sample selection method is designed to obtain a clean dataset, which converts negative effects of the long-tailed problem into positive ones. In the second stage, dynamic instance-based relabeling is designed to train a model and improve the dataset’s quality simultaneously. Eventually, DSDIR not only produces a balanced and noise-tolerant model but also obtains a clean dataset. Experimental results demonstrate that in the high noise condition of 60% and 80%, the accuracy rate of DSDIR is 5% higher than the state-of-the-art methods. Our code is available at https://***/nku-ligl/DSDIR.

关键词： Deep learning Computer vision Heavily-tailed distribution Codes Accuracy Noise Speech recognition Acoustics Noise measurement Speech processing

来源：评论

学校读者我要写书评

暂无评论

Spatial-Temporal Reconstruction Error for AIGC-based Forgery Image Detection

Spatial-Temporal Reconstruction Error for AIGC-based Forgery...

引用

International Conference on Acoustics, Speech, and Signal Processing (ICASSP)

作者： Chengji Shen Zhenjiang Liu Kaixuan Chen Jie Lei Mingli Song Zunlei Feng State Key Laboratory of Blockchain and Data Security Zhejiang University Hangzhou High-Tech Zone (Binjiang) Institute of Blockchain and Data Security Idlefish Network Technology Alibaba Group College of Computer Science and Technology Zhejiang University of Technology

ISBN: (数字)9798350368741

ISBN: (纸本)9798350368758

The remarkable success of AI-Generated Content (AIGC), especially diffusion image generation models, brings about unprecedented creative applications, but also creates fertile ground for malicious counterfeiting and crime. A highly effective family of forgery image detection methods based on diffusion reconstruction error has emerged, as images generated by diffusion are more easily reconstructed by any diffusion model. However, we find that existing methods only use reconstruction error from a single time step, failing to fully leverage the entire reconstruction process. To this end, we propose to comprehensively consider every single time step to form the Temporal Reconstruction Error (TRE) that offers a richer feature representation. Furthermore, we design temporal aggregation and spatial focusing modules from two dimensions respectively to more effectively extract discriminative information from the TRE feature. Finally, we validate the proposed method on two popular datasets, and experimental results demonstrate that the proposed approach achieves state-of-the-art performance.

关键词： Deepfakes Image synthesis Focusing Signal processing Feature extraction Diffusion models Forgery data mining Speech processing Image reconstruction

来源：评论

学校读者我要写书评

暂无评论

TransMatch: Employing Bridging Strategy to Overcome Large Deformation for Feature Matching in Gastroscopy Scenario

引用

IEEE Transactions on Medical Imaging 2025年 PP卷 PP页

作者： Zhu, Guosong Qin, Zhen Yu, Linfang Ding, Yi Qin, Zhiguang University of Electronic Science and Technology of China Network and Data Security Key Laboratory of Sichuan Province Chengdu611731 China Industrial Research Institute Chengdu610000 China

Feature matching is widely applied in the image processing field. However, both traditional feature matching methods and previous deep learning-based methods struggle to accurately match the features with severe deformations and large displacements, particularly in gastroscopy scenario. To fill this gap, an effective feature matching framework named TransMatch is proposed, which addresses the largely displacements issue by matching features with global information leveraged via Transformer structure. To address the severe deformation of features, an effective bridging strategy with a novel bidirectional quadratic interpolation network is employed. This bridging strategy decomposes and simplifies the matching of features undergoing severe deformations. A deblurring module for gastroscopy scenario is specifically designed to address the potential blurriness. Experiments have illustrated that proposed method achieves state-of-the-art performance of feature matching and frame interpolation in gastroscopy scenario. Moreover, a large-scale gastroscopy dataset is also constructed for multiple tasks. © 1982-2012 IEEE.

关键词： Feature extraction

来源：评论

学校读者我要写书评

暂无评论

建议与咨询留下您的常用邮箱和电话号码，以便我们向您反馈解决方案和替代方法

时间限定

文献类型

馆藏选择

核心期刊

语言

文献类型

帮助

文字说明：

检索规则说明：

检索范例：

分类表

所选分类

限定检索结果

文献类型

馆藏范围

日期分布

学科分类号

主题

机构

作者

语言

请选择保存的检索档案：

请选择收藏分类：

通借通还

建议与咨询 留下您的常用邮箱和电话号码，以便我们向您反馈解决方案和替代方法

时间限定

文献类型

馆藏选择

核心期刊

语言

文献类型

帮助

文字说明：

检索规则说明：

检索范例：

分类表

所选分类

限定检索结果

文献类型

馆藏范围

日期分布

学科分类号

主题

机构

作者

语言

请选择保存的检索档案： 新增检索档案 确定 取消

请选择收藏分类： 新增自定义分类 确定 取消

通借通还

建议与咨询留下您的常用邮箱和电话号码，以便我们向您反馈解决方案和替代方法

请选择保存的检索档案：

请选择收藏分类：