检索结果-内蒙古大学图书馆

PassDiff: A New Approach for Password Guessing Using Diffusion Model 1

13th International Conference on computer engineering and networks, CENet 2023

作者： Guo, Sheng Duan, Ming Du, Yibin Wang, Wei Guo, Lulu College of Cyberspace Security Information Engineering University Zhengzhou450001 China Henan Key Laboratory of Network Cryptography Technology Zhengzhou450001 China

ISBN: (数字)9789819992393

ISBN: (纸本)9789819992386

Password guessing models can be broadly divided into three classes: dictionary-based password guessing model, password guessing model based on probability statistics and password guessing model based on deep learning. Recurrent neural networks and generative adversarial networks are the main deep learning techniques used for password guessing in the past. In this paper, we propose a novel PassDiff method for password guessing using denoising diffusion probabilistic models (DDPMs). Considering the similarity between the password space and the text space, we incorporate a byte-level tokenizer in the input phase and optimize the sampling process by modifying the source code. We encode a special character, and it makes PassDiff can handle input of variable length and obtain variable output without manual truncation. The experimental results show that PassDiff produces high-quality passwords even with minimal denoising steps. We recommend setting the denoising steps to 5–50, which can increase the sampling speed by tens of times. Compared with PassGAN, the training process of PassDiff is more stable and the cracking rate is also significantly improved. Specifically, when the denoising steps is set to 10 and 108 passwords are generated, PassDiff increases the cracking rate by 3.17%, 6.33% and 13.22% on 12306, CSDN and RockYou datasets, respectively. © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd 2024.

关键词： Diffusion

来源：评论

学校读者我要写书评

暂无评论

Incorporating metapath interaction on heterogeneous information network for social recommendation

引用

Frontiers of computer Science 2024年第1期18卷 33-48页

作者： Yanbin JIANG Huifang MA Xiaohui ZHANG Zhixin LI Liang CHANG College of Computer Science and Engineering Northwest Normal UniversityLanzhou 730070China Guangxi Key Lab of Multi-source Information Mining and Security Guangxi Normal UniversityGuilin 541001China Guangxi Key Laboratory of Trusted Software Guilin University of Electronic TechnologyGuilin 541004China

Heterogeneous information network(HIN)has recently been widely adopted to describe complex graph structure in recommendation systems,proving its effectiveness in modeling complex graph *** existing HIN-based recommendation studies have achieved great success by performing message propagation between connected nodes on the defined metapaths,they have the following major *** works mainly convert heterogeneous graphs into homogeneous graphs via defining metapaths,which are not expressive enough to capture more complicated dependency relationships involved on the ***,the heterogeneous information is more likely to be provided by item attributes while social relations between users are not adequately *** tackle these limitations,we propose a novel social recommendation model MPISR,which models MetaPath Interaction for Social Recommendation on heterogeneous information ***,our model first learns the initial node representation through a pretraining module,and then identifies potential social friends and item relations based on their similarity to construct a unified *** then develop the two-way encoder module with similarity encoder and instance encoder to capture the similarity collaborative signals and relational dependency on different *** experiments on five real datasets demonstrate the effectiveness of our method.

关键词： heterogeneous information network social reco-mmender system metapath interaction attention mechanism

来源：评论

学校读者我要写书评

暂无评论

A bitcoin service community classification method based on Random Forest and improved KNN algorithm

IET Blockchain

引用

IET Blockchain 2024年第3期4卷 276-286页

作者： Gao, Muyun Lin, Shenwen Tian, Xin He, Xi He, Ketai Chen, Shifeng School of Mechanical Engineering University of Science and Technology Beijing Beijing China Internet Financial Security Technology Key Laboratory National Computer Network Emergency Response Technical Team/Coordination Center of China Beijing China

There are service communities with different functions in the Bitcoin transactions system. Identifying community categories helps to further understand the Bitcoin transactions system and facilitates targeted regulation of anonymized Bitcoin transactions. To this end, a Bitcoin service community classification method based on Random Forest and improved K-Nearest Neighbor (KNN) algorithm is proposed. First, the transaction characteristics of different types of communities are analyzed and summarized, and the corresponding transaction features are extracted from the address and entity levels;then multiple classification algorithms are compared, the optimal model to filter the effective features is selected, and the feature vector of entity addresses is constructed. Finally, a classification model is constructed based on Random Forest and improved KNN algorithm to classify the entities. By constructing different classification models for experimental comparison, the accuracy and stability advantages of the proposed method for classification in service community classification research are verified. © 2024 The Authors. IET Blockchain published by John Wiley & Sons Ltd on behalf of The Institution of engineering and Technology.

关键词： Classification (of information)

来源：评论

学校读者我要写书评

暂无评论

Task Offloading and Resource Allocation Based on Reinforcement Learning and Load Balancing in Vehicular networking

引用

IEEE Transactions on Consumer Electronics 2025年第1期71卷 2217-2230页

作者： Tian, Shujuan Xiang, Shuhuan Zhou, Ziqi Dai, Haipeng Yu, Enze Deng, Qingyong Xiangtan University Key Laboratory of Hunan Province for Internet of Things and Information Security Hunan International Scientific and Technological Cooperation Base of Intelligent network School of Computer Science and School of Cyberspace Security Xiangtan411105 China Nanjing University School of Computer Science and Technology Nanjing211189 China Guangxi Normal University Key Lab of Education Blockchain and Intelligent Technology Ministry of Education Guilin541004 China Guangxi Normal University Guangxi Key Lab of Multi-source Information Mining and Security Guilin541004 China

Due to limited on-board resources and the mobility characteristics of vehicles in a multi-access edge computing (MEC)-based vehicular network, efficient task offloading and resource allocation schemes are essential for achieving low-latency and low-energy consumption applications in the Internet of Vehicles (IoV). The spatial distribution of vehicles, influenced by various factors, leads to significant workload variations across MEC servers. In this paper, we address task offloading and resource allocation as a joint optimization problem and propose a Load-Balancing Deep Deterministic Policy Gradient (LBDDPG) algorithm to achieve optimal results. The joint optimization problem is modeled as a Markov Decision Process (MDP), enabling the LBDDPG algorithm to systematically address the challenges of workload imbalance and resource inefficiency. The algorithm incorporates a load optimization strategy to balance workload distribution across MEC servers, mitigating disparities caused by uneven vehicle distributions. The reward function is designed to account for both energy consumption and delay, ensuring an optimal trade-off between these critical factors. To enhance training efficiency, a noise-based exploration strategy is employed, preventing ineffective exploration during the early stages. Additionally, constraints such as computational capacity and latency thresholds are embedded to ensure the algorithm's practical applicability. Experimental results demonstrate that the proposed LBDDPG algorithm achieves faster convergence and superior performance in terms of energy consumption and latency compared to other reinforcement learning algorithms. © 1975-2011 IEEE.

关键词： Markov processes

来源：评论

学校读者我要写书评

暂无评论

Offloading Mode Hidden for Delay Sensitive Tasks in Edge Computing networks 5

Offloading Mode Hidden for Delay Sensitive Tasks in Edge Com...

引用

5th International Seminar on Artificial Intelligence, networking and information Technology, AINIT 2024

作者： Zhang, Guowei Du, Jiayuan Qufu Normal University School of Cyber Science and Engineering Qufu China Key Laboratory of Computing Power Network and Information Security Jinan China

ISBN: (纸本)9798350385557

Edge computing has been developed to support local services with lower burden to the core network. However, privacy issues deteriorate when terminal users enjoy the high-quality services with the price of sending their data to multiple service nodes (SNs) scattered throughout the network. Existing researches concerning this problem are almost all based on the traditional security technologies including encryption and authentication, which are too complicated to be well supported by the terminal nodes (TNs) in edge networks due to the poor local processing capabilities. In view of the above, this article proposes an offloading mode hidden (OMH) algorithm, which reduces the risk level by equalizing the task offloading modes of different TNs. Gaming process is adopted to achieve the risk-minimized offloading solutions for TNs. Numerical results demonstrate that comparing with the Greedy algorithm, the proposed OMH algorithm can achieve lower task offloading risk with higher efficiency and provide more TNs with satisfied services. © 2024 IEEE.

关键词： Edge computing

来源：评论

学校读者我要写书评

暂无评论

DRSC: Dual-Reweighted Siamese Contrastive Learning network for Cross-Domain Rotating Machinery Fault Diagnosis With Multi-Source Domain Imbalanced Data

引用

IEEE Internet of Things Journal 2025年第13期12卷 23678-23693页

作者： Bi, Yuanguo Fu, Rao Jiang, Cunyu Zhang, Xiaoling Li, Fengyun Zhao, Liang Han, Guangjie Northeastern University School of Computer Science and Engineering Shenyang110169 China Ministry of Education Engineering Research Center of Security Technology of Complex Network System Shenyang110169 China Shenyang University of Technology Institute of Artificial Intelligence Shenyang110870 China Shenyang Aerospace University School of Computer Science Shenyang110135 China Hohai University Key Laboratory of Maritime Intelligent Network Information Technology Ministry of Education China

To enhance the reliability of rotating machinery, cross-domain fault diagnosis becomes vital for detecting faults under unknown operating conditions. However, multi-source domain imbalanced data present significant challenges, as divergent label distributions across domains cause complex domain-class shifts and degrade the performance of cross-domain fault diagnosis. Moreover, diagnostic models often struggle to learn features from minority classes due to label imbalance within each domain, which may degrade the performance in diagnosing these minority classes. To address these challenges, we propose a Dual-Reweighted Siamese Contrastive learning network (DRSC) for cross-domain fault diagnosis with multi-source domain imbalanced data. In DRSC, we design a Siamese feature extractor based on a wide-kernel convolutional neural network to capture short-term characteristics and leverage the convenience in extracting domain-invariant features. Subsequently, to alleviate domain-class shifts, we design a reweighted contrastive domain-class alignment mechanism that strategically pulls domain-class pairs together while pushing other health conditions away. Finally, to enable the diagnostic model to learn from minority health conditions, a reweighted health condition classifier is developed by assigning higher weights to the minority classes. Evaluation results on two public datasets illustrate DRSC outperforms comparison models in cross-domain fault diagnosis. © 2014 IEEE.

关键词： Convolutional neural networks

来源：评论

学校读者我要写书评

暂无评论

Spatio-temporal Constrained Geomagnetic Indoor Localization with Arbitrary Walking Speed

引用

IEEE Internet of Things Journal 2025年第12期12卷 21740-21756页

作者： Liu, Ning Peng, Gezhi Ling, Hua-Bao Niu, Qun He, Tao Sun Yat-sen University Guangdong Key Laboratory of Information Security Technology School of Computer Science and Engineering Guangzhou510006 China Sun Yat-sen University School of Artificial Intelligence Zhuhai519000 China

This paper focuses on the accuracy and practicality of geomagnetic-based indoor localization in large-scale real-world sites. Previous studies often use continuous geomagnetic sequences for better discriminability but ignore that longer sequences, while more accurate, increase response times. Shorter sequences enhance practicality but sacrifice accuracy. Additionally, varying walking speeds can cause sequence variations along the same path, leading to ambiguity. To address these challenges, we propose a spatial-temporal constrained indoor localization model (STC-loc) that enables both accurate and practical localization using geomagnetic sequences collected at arbitrary walking speeds. Specifically, we first design a speed-oriented adaptive normalization module to tackle the walking speed heterogeneity problem. Then to balance accuracy and practicality, we initially employ shorter sequences as input and a self-attention hierarchical structure to efficiently extract the temporal features for initial location estimation. Subsequently, we further apply an Encoder-Decoder based refinement strategy, leveraging spatial contextual for continuous localization to ensure accuracy and robustness. Additionally, a slide-window based two-phase system architecture optimizes processing efficiency and scalability. Experiments on three large-scale real-world sites have demonstrated the superiority of the proposed STC-loc, reducing the localization error by more than 43% with shorter geomagnetic sequences. © 2014 IEEE.

关键词： Geomagnetism

来源：评论

学校读者我要写书评

暂无评论

Review of Static Image Compression Algorithms 7

Review of Static Image Compression Algorithms

引用

7th IEEE Advanced information Technology, Electronic and Automation Control Conference, IAEAC 2024

作者： Liang, Yanxia Jia, Tong Li, Na Liu, Xin Jiang, Jing Lu, Guangyue Zhao, Meng Xi'an University of Posts and Telecommunications Shaanxi Key Laboratory of Information Communication Network and Security Xi'an China Xi'an Eurasia University College of Information Engineering Xi'an China

ISBN: (纸本)9798350339161

With the increasing development of digital image communication technology, the requirements for image quality, such as texture details, color levels, and dynamic range, are gradually increasing. This presents unprecedented challenges to the tradeoff between the reliability and effectiveness of image compression technology. Static image compression standards are an integration of phased research achievements in image compression algorithms and provide guidance for the study of image compression. Firstly, static image compression algorithms are introduced in three types: discrete cosine transform (DCT) coding-based methods, prediction coding-based methods, and discrete wavelet transform (DWT) coding-based methods, based on the existing literatures and the latest achievements. The basic idea is elaborated for each typical algorithm. Secondly, the advantages and disadvantages of these algorithms are analyzed, compared, and summarized. Thirdly, research on static image compression in the past three years is detailed and analyzed, which is divided into three technical fields: image steganography, high fidelity image processing, and optimization of image processing performance. Finally, the challenges in each field are proposed as well. Some potential future research work is advised. © 2024 IEEE.

关键词： Discrete wavelet transforms

来源：评论

学校读者我要写书评

暂无评论

Defending Against Poisoning Attacks in Federated Prototype Learning on Non-IID Data 18th

Defending Against Poisoning Attacks in Federated Prototype ...

引用

18th International Conference on Wireless Artificial Intelligent Computing Systems and Applications, WASA 2024

作者： Zhang, Jie Zhang, Hongliang Wang, Guijuan Dong, Anming Jinan250353 China Shandong Provincial Key Laboratory of Industrial Network and Information System Security Shandong Fundamental Research Center for Computer Science Jinan250353 China

ISBN: (纸本)9783031714665

Federated learning (FL) is an emerging distributed machine learning paradigm that enables participants to cooperatively train learning tasks without revealing the raw data. However, the distributed nature of FL makes it susceptible to poisoning attacks, especially when the local data of participants are non-independent and identically diatributed (non-IID). Although several defense methods have been proposed to mitigate poisoning attacks, their effectiveness is limited by the specific assumptions about the data distribution. In this work, we propose a new defense strategy, FedAPA (Federated Prototype Learning Against Poisoning Attacks). Specifically, we use abstract class prototypes to communicate between the clients and server, thus effectively alleviating the impact of non-IID data. Moreover, we propose a new abnormal client detection method that aims to mitigate the impact of malicious clients while distinguishing between malicious and benign clients, thereby effectively defending against poisoning attacks. Extensive experiments on different datasets show that FedAPA can effectively resist the poisoning attacks under various data distributions. © The Author(s), under exclusive license to Springer Nature Switzerland AG 2025.

关键词： Federated learning

来源：评论

学校读者我要写书评

暂无评论

An Efficient and Provably Secure SM2 key-Insulated Signature Scheme for Industrial Internet of Things

引用

computer Modeling in engineering & Sciences 2024年第1期138卷 903-915页

作者： Senshan Ouyang Xiang Liu Lei Liu Shangchao Wang Baichuan Shao Yang Zhao School of Mechanical Engineering Northwestern Polytechnical UniversityXi’anChina Department of Process and Information Technology Chengdu Aircraft Industrial(Group)Co.Ltd.ChengduChina School of Information and Software Engineering The Network and Data Security Key Laboratory of Sichuan ProvinceUniversity of Electronic Science and Technology of ChinaChengduChina

With the continuous expansion of the Industrial Internet of Things(IIoT),more andmore organisations are placing large amounts of data in the cloud to reduce ***,the channel between cloud servers and smart equipment is not trustworthy,so the issue of data authenticity needs to be *** SM2 digital signature algorithm can provide an authentication mechanism for data to solve such ***,it still suffers from the problem of key *** order to address this concern,this study first introduces a key-insulated scheme,SM2-KI-SIGN,based on the SM2 *** scheme boasts strong key insulation and secure *** scheme uses the elliptic curve algorithm,which is not only more efficient but also more suitable for IIoT-cloud ***,the security proof of SM2-KI-SIGN is given under the Elliptic Curve Discrete Logarithm(ECDL)assumption in the random oracle.

关键词： key-insulated SM2 algorithm digital signature Industrial Internet of Things(IIoT) provable security

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：