检索结果-内蒙古大学图书馆

36th International Conference on Software Engineering and Knowledge Engineering, SEKE 2024

作者： Hao, Huijuan Zhu, Sijian Chen, Yu Yi, Changle Zhao, Hongge Feng, Yue Yang, Rong Key Laboratory of Computing Power Network and Information Security Ministry of Education Shandong Computer Science Center National Supercomputer Center in Jinan Qilu University of Technology Shandong Academy of Sciences Jinan China Shandong Provincial Key Laboratory of Computer Networks Shandong Fundamental Research Center for Computer Science Jinan China

ISBN: (纸本)1891706594

Addressing the challenges posed by the insufficient computational power of low-spec devices to achieve the inference efficiency of prevailing deep learning models, alongside the variability in type and shape within industrial metal sheet datasets, while also catering to the demand for high-precision detection, this paper proposes a novel single-stage detection architecture termed MTRNet. In this paper, we introduce three key enhancements: Firstly, to reduce the number of parameters, we propose a novel Partial Depth Convolution (PDC) structure. By minimizing redundant computations, we aim to enhance the efficiency of spatial feature extraction. Secondly, we propose a Convolution Transformer (CTR) structure that replaces the self-attention module with a convolutional module. This modification addresses the computational inefficiency inherent in the self-attention mechanism of existing Visual Transformers (ViT). Finally, we introduce a novel Attention Convolution Transformer (ACTR) structure to enhance the extraction of global and local feature information. This architecture seamlessly integrates the strengths of both attention and convolution mechanisms, working synergistically to improve performance. Our proposed MTRNet network demonstrates superior detection performance compared to existing industrial standards, achieving a detection accuracy of 81.5% on the NEU-DET dataset. © 2024 Knowledge Systems Institute Graduate School. All rights reserved.

关键词： Deep learning

来源：评论

学校读者我要写书评

暂无评论

LESaET: Low-Dimensional Embedding Method for Link Prediction Combining Self-Attention and Enhanced-TuckER

LESaET: Low-Dimensional Embedding Method for Link Prediction...

引用

2024 IEEE International Conference on Systems, Man, and Cybernetics, SMC 2024

作者： Ding, Lichao Zhao, Jing Lu, Kai Hao, Zenghao Key Laboratory of Computing Power Network and Information Security Ministry of Education Shandong Computer Science Center Jinan China Shandong Engineering Research Center of Big Data Applied Technology Faculty of Computer Science and Technology Jinan China Shandong Provincial Key Laboratory of Computer Networks Shandong Fundamental Research Center for Computer Science Jinan China

ISBN: (纸本)9781665410205

Knowledge graphs (KGs) provide a structured representation of the real world through entity-relation triples. However, current KGs are often incomplete, typically containing only a small fraction of all possible facts. This involves inferring missing content from existing information, a task known as link prediction. Existing methods in the field of link prediction struggle with controlling the dimensionality of embedding vectors or suffer from overly complex models. In order to tackle these challenges, we introduce a method in this paper, named Low-Dimensional Embedding Method for Link Prediction Combining Self-Attention and Enhanced-TuckER (LESaET). LESaET leverages both self-attention mechanisms and tensor factorization to learn expressive contextual-enhanced representations of KGs. Specifically, LESaET employs the multi-head self-attention mechanism of Transformer as an encoder to capture the mutual information between entities and relationships, and utilizes Enhanced-TuckER as a decoder, ultimately achieving expressive low-dimensional embeddings for link prediction tasks. LESaET demonstrates competitive performance compared to advanced methods on standard datasets. © 2024 IEEE.

关键词： Prediction models

来源：评论

学校读者我要写书评

暂无评论

A Lightweight IoT Malware Detection and Family Classification Method

引用

Journal of computer and Communications 2024年第4期12卷 201-227页

作者： Changguang Wang Ziqi Ma Qingru Li Dongmei Zhao Fangwei Wang College of Computer and Cyber Security Hebei Normal University Shijiazhuang China Key Laboratory of Network and Information Security of Hebei Province Hebei Normal University Shijiazhuang China

A lightweight malware detection and family classification system for the Internet of Things (IoT) was designed to solve the difficulty of deploying defense models caused by the limited computing and storage resources of IoT devices. By training complex models with IoT software gray-scale images and utilizing the gradient-weighted class-activated mapping technique, the system can identify key codes that influence model decisions. This allows for the reconstruction of gray-scale images to train a lightweight model called LMDNet for malware detection. Additionally, the multi-teacher knowledge distillation method is employed to train KD-LMDNet, which focuses on classifying malware families. The results indicate that the model’s identification speed surpasses that of traditional methods by 23.68%. Moreover, the accuracy achieved on the Malimg dataset for family classification is an impressive 99.07%. Furthermore, with a model size of only 0.45M, it appears to be well-suited for the IoT environment. By training complex models using IoT software gray-scale images and utilizing the gradient-weighted class-activated mapping technique, the system can identify key codes that influence model decisions. This allows for the reconstruction of gray-scale images to train a lightweight model called LMDNet for malware detection. Thus, the presented approach can address the challenges associated with malware detection and family classification in IoT devices.

关键词： IoT security Visual Explanations Multi-Teacher Knowledge Distillation Lightweight CNN

来源：评论

学校读者我要写书评

暂无评论

Research on the Application of Digital Signature in University Electronic Government System 6

Research on the Application of Digital Signature in Universi...

引用

6th International Conference on computer network, Electronic and Automation, ICCNEA 2023

作者： Wang, Tianma Zhao, Dongmei Qi, Jianjun North China Electric Power University International Education Institute Beijing China Hebei Normal University College of Computer and Cyber Security Shijiazhuang China Key Laboratory of Network and Information Security in Hebei Province Shijiazhuang China

ISBN: (纸本)9798350305388

This article analyzes the principles and processes of digital signature technology, as well as its application methods in the university e-government system. On the one hand, it meets the business management needs of university, and on the other hand, the application of digital signature and timestamp electronic signature ensures the security of university e-government SystemThe university e-government management system based on digital signature technology verifies the identity of users such as the sender and receiver of files, and issues digital certificates. The sender uses digital certificates for system authorization. Signs and encrypts confidential files such as documents through digital signatures to protecting the security of electronic file transmission. Upload the digitally signed and encrypted documents to the system, which completes the sending and receiving of the documents. On the one hand, the recipient downloads documents through the system and uses digital signatures to verify the integrity and authenticity of the documents. On the other hand, digitally sign the document and upload it to the system. Both parties complete the distribution and upload of documents through the system. © 2023 IEEE.

关键词： Authentication

来源：评论

学校读者我要写书评

暂无评论

Feature Aggregation network for Memory-Based Industrial Anomaly Detection 9

Feature Aggregation Network for Memory-Based Industrial Anom...

引用

9th IEEE International Conference on Data Science in Cyberspace, DSC 2024

作者： Li, Min Han, Delong Shao, Rui He, Jinghui City University of Macau Faculty of Data Science China Qilu University of Technology Shandong Academy of Sciences Key Laboratory of Computing Power Network and Information Security Ministry of Education Shandong Computer Science Center National Supercomputer Center in Jinan Shandong Computer Science Center National Supercomputer Center in Jinan Jinan China Qilu University of Technology Shandong Academy of Sciences Key Laboratory of Computing Power Network and Information Security Ministry of Education Shandong Computer Science Center National Supercomputer Center in Jinan Jinan China Shandong Fundamental Research Center for Computer Science Shandong Provincial Key Laboratory of Computer Networks Jinan China

ISBN: (纸本)9798350391367

In the process of large-scale industrial production, complex defects of different shapes and sizes can occur in industrial products and their components, and these defects seriously affect the performance of industrial products and their components, and being able to quickly and accurately identify defective industrial products and their components is an important task. Memory-based methods are usually used for anomaly detection, and these methods often rely on pre-trained models for feature extraction, but have high requirements on feature quality. In addition, patch-based methods mainly focus on local differences in feature embedding when detecting anomalies without explicitly exploiting broader contextual information, thus greatly affecting the robustness and accuracy of anomaly detection. Aiming at the above problems and requirements, this paper proposes a Feature Aggregation network for Memory-Based Industrial Anomaly Detection (FANet), which is based on the pre-extraction of anomalous features, attentional attention to key features, information aggregation of multi-level and multi-scale features, and the FANet provides advanced performance in industrial image anomaly detection and localisation by pre-extraction of anomalous features, attentional attention to key features, information aggregation of multi-layered and multi-scale features, and introduction of environmental information in the anomaly score computation to perform industrial anomaly detection tasks, and using core set subsampling to reduce storage requirements. On the MVTec AD, FANet has an image-level AUROC score of 99.1% and a pixel-level AUROC score of 98.2%. Similarly high detection results were achieved on the ViSA dataset, further demonstrating the robustness of FANet. © 2024 IEEE.

关键词： Anomaly detection

来源：评论

学校读者我要写书评

暂无评论

High-Level security Dimension Evaluation of Blockchain BaaS System and key Technology

引用

China Communications 2024年第6期21卷 176-191页

作者： Chen Wanfa Zheng Qing’an Chen Shuzhen Fu Hongyi Chen Liang Fujian Police College Fuzhou 350007China Computer Science and Information Security Management Department Fujian Police CollegeFuzhou 350007China Fujian Province Key Laboratory for Digital Network Crime Supervision and Prevention Fuzhou 350007China Beijing University of Posts and Telecommunications Beijing 100876China Fujian Provincial Prison Administration Fuzhou 350002China

In recent years,blockchain technology integration and application has gradually become an important driving force for new technological innovation and industrial *** blockchain technology and applications are developing rapidly,the emerging security risks and obstacles have gradually become *** can still find security issues in blockchain systems and conduct attacks,causing increasing losses from network attacks every *** response to the current demand for blockchain application security detection and assessment in all industries,and the insufficient coverage of existing detection technologies such as smart contract detectiontechnology,this paper proposes a blockchain core technology security assessment system model,and studies the relevant detection and assessment key technologies and systems.A security assessment scheme based on a smart contract and consensus mechanism detection scheme is *** the underlying blockchain architecture supports the traceability of detection results using super ***,the functionality and performance of the system were tested,and the test results show that the model and solutions proposed in this paper have good feasibility.

关键词： blockchain security consensus mechanis smart contract

来源：评论

学校读者我要写书评

暂无评论

network Intrusion Detection in Internet of Blended Environment Using Ensemble of Heterogeneous Autoencoders(E-HAE)

引用

computer Systems Science & Engineering 2023年第9期46卷 3261-3284页

作者： Lelisa Adeba Jilcha Deuk-Hun Kim Julian Jang-Jaccard Jin Kwak ISAA Lab. Department of AI Convergence NetworkAjou UniversitySuwon16499Korea ISAA Lab. Institute for Information and CommunicationAjou UniversitySuwon16499Korea Department of Computer Science and Information Technology Massey UniversityAuckland0745New Zealand Department of Cyber Security Ajou UniversitySuwon16499Korea

Contemporary attackers,mainly motivated by financial gain,consistently devise sophisticated penetration techniques to access important information or *** growing use of Internet of Things(IoT)technology in the contemporary convergence environment to connect to corporate networks and cloud-based applications only worsens this situation,as it facilitates multiple new attack vectors to emerge *** such,existing intrusion detection systems suffer from performance degradation mainly because of insufficient considerations and poorly modeled detection *** address this problem,we designed a blended threat detection approach,considering the possible impact and dimensionality of new attack surfaces due to the aforementioned *** collectively refer to the convergence of different technology sectors as the internet of blended *** proposed approach encompasses an ensemble of heterogeneous probabilistic autoencoders that leverage the corresponding advantages of a convolutional variational autoencoder and long short-term memory variational *** extensive experimental analysis conducted on the TON_IoT dataset demonstrated 96.02%detection ***,performance of the proposed approach was compared with various single model(autoencoder)-based network intrusion detection approaches:autoencoder,variational autoencoder,convolutional variational autoencoder,and long short-term memory variational *** proposed model outperformed all compared models,demonstrating F1-score improvements of 4.99%,2.25%,1.92%,and 3.69%,respectively.

关键词： network intrusion detection anomaly detection TON_IoT dataset smart grid smart city smart factory digital healthcare autoencoder variational autoencoder LSTM convolutional variational autoencoder ensemble learning

来源：评论

学校读者我要写书评

暂无评论

BAV-DSS: Blockchain Assisted Verifiable Data Sharing Scheme with Fast Encryption and Outsourced Decryption for IoT 24th

BAV-DSS: Blockchain Assisted Verifiable Data Sharing Scheme ...

引用

24th International Conference on Algorithms and Architectures for Parallel Processing, ICA3PP 2024

作者： Huang, Yao Yu, Zhongyuan Wang, Guijuan Chai, Baobao Zhang, Hongliang Jinan250353 China Shandong Provincial Key Laboratory of Industrial Network and Information System Security Shandong Fundamental Research Center for Computer Science Jinan250353 China School of Information Science and Engineering Lanzhou University Lanzhou730000 China School of Computer Science and Engineering Shandong University of Science and Technology Qingdao266590 China

ISBN: (纸本)9789819615414

Many sophisticated cloud platforms are available to provide services such as storage and computation for Internet of Things (IoT) applications. However, with the concerns about the semi-trusted nature of cloud platforms, data security and privacy have been gaining broad attention. In this paper, we propose a blockchain-assisted verifiable data sharing scheme (BAV-DSS) for IoT. The BAV-DSS features fine-grained access control, fast encryption, and outsourced decryption with verifiability. Specifically, with the application of ciphertext-policy attribute-based encryption (CP-ABE), flexible access control is enabled. Blockchain is employed to store public keys, generate account addresses, and perform data verification. Furthermore, the selective replayable chosen ciphertext attacks (sRCCA) security of BAV-DSS is proved, and the simulation results indicate that BAV-DSS obtains high efficiency and practicality compared to other schemes. © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2025.

关键词： Cloud platforms

来源：评论

学校读者我要写书评

暂无评论

A firmware upgrade solution based on hardware unique ID and SM4 cryptographic algorithm

A firmware upgrade solution based on hardware unique ID and ...

引用

2023 International Conference on computer Graphics, Artificial Intelligence, and Data Processing, ICCAID 2023

作者： Gao, Shaofeng Wang, Kun Chen, Xinze Fu, Yong Key Laboratory of Computing Power Network and Information Security Ministry of Education Shandong Computer Science Center National Supercomputer Center in Jinan Qilu University of Technology Shandong Academy of Sciences Jinan250000 China Shandong Provincial Key Laboratory of Computer Networks Shandong Fundamental Research Center for Computer Science Jinan250000 China

ISBN: (纸本)9781510677685

This article presents and implements a firmware upgrade solution for embedded systems, which aims to address the vulnerability of using the same file that can be easily copied or stolen. When upgrading firmware, a txt file is sent by the upper-level software, which is encrypted using the SM4 algorithm. This encryption is based on a hardware-unique ID and a pre-stored upper-level software-unique ID, which act as encryption keys. During the upgrade, a secure downloader utilizes its hardware-unique ID and a pre-stored upper-level software-unique ID as decryption keys to decrypt the file. If the decryption key does not match the encryption key, the decrypted txt file will not be readable. Once decrypted, the firmware is downloaded to the target device using BSL (Bootstrap Loader) technology. Practical tests have shown that this method can achieve "one file, one key,"effectively preventing unauthorized use of the upgraded firmware. © 2024 SPIE.

关键词： Firmware

来源：评论

学校读者我要写书评

暂无评论

Violence Video Detection Based on Multi-modal Fusion and Dual Channel Contrastive Learning

Violence Video Detection Based on Multi-modal Fusion and Dua...

引用

2023 Human-Centered Cognitive Systems, HCCS 2023

作者： Li, Huan Bai, Zhijian Li, Yue Qin, Tao Xi'an Jiaotong University Faculty of Electronic and Information Engineering Xi'an China Xi'an Jiaotong University Moe Key Lab for Intelligent Networks and Network Security Xi'an China

ISBN: (纸本)9798350359183

There are more and more violence videos propagated on the Internet, it is important to detect them to provide support for online social network management. In the traditional detection methods, the utilization of different modal data is insufficient. In this paper, we propose a violence detection method based on multimodal fusion and dual channel contrastive learning. Firstly, an adaptive feature fusion method based on the attention mechanism is proposed. The problem of original information missing in the fused features is effectively solved by adaptively weighting the original modal features and the attention fusion features. And then the similarity of the same data representations is improved by contrastive learning between different feature fusion methods, in turn, the local problems caused by different fusion methods are solved. Experimental results based on public datasets show that the proposed method can learn the audio and video features and then generate high quality multimodal fusion features, the detection accuracy is improved by 1.3% to 4.8% compared with popular violence detection methods. © 2023 IEEE.

关键词： Social networking (online)

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：