检索结果-内蒙古大学图书馆

IEEE International Symposium on Dependable, Autonomic and Secure Computing (DASC)

作者： Shi Chen Qianmu Li Qi Wang Xinjian Zhao Hao Ge Nianzhe Li State Grid Jiangsu Information & Telecommunication Company Nanjing China School of Computer Science ana Engineering Nanjing University of Science and Technology Nanjing China Ltd. State Grid Key Laboratory of Information & Network Security State Grid Smart Grid Research Institute Co. Nanjing China

There are various data security risks in power grid systems, and recommendation systems are a good solution to the selection of security strategies. Federated learning is a machine learning framework that can protect the data privacy of each participant and can effectively solve the data security external collaborative training modeling challenges. This paper proposed a flexible implantable recommendation algorithm based on knowledge graph and multi-channel composite to enhance federated learning on demand. The algorithm designs three different feature extraction modules to learn to construct multi-dimensional features on multi-attribute collaborative knowledge graphs. Among them, the distance translation module utilizes the Trans R recommendation algorithm to learn the internal semantic structure information in knowledge graph triples and terminal-security policy selection history; the random walk module uses the Node2Vec algorithm to learn the homogeneous and Structured information in the multi-attribute collaborative knowledge graph; the graph convolutional network module learns the high-order neighborhood multi-attribute collaborative information of terminal/security policy nodes through multi-layer feature propagation and aggregation. The composite network module adopts a convolutional neural network-like operation to first locally optimize the three modules, and then extracts the highly informative elements to obtain the final feature representation of the terminal/security policy. Finally, the algorithm merges the vectors of terminal-security policy pairs into the multi-layer perceptron to learn and calculate the prediction score for recommendation.

关键词：

来源：评论

学校读者我要写书评

暂无评论

Long Text Classification Based on BERT 5

Long Text Classification Based on BERT

引用

5th IEEE Information Technology, networking, Electronic and Automation Control Conference, ITNEC 2021

作者： Weijie, DIng Yunyi, Li Jing, Zhang Xuchen, Shen Big-data and Network Security Research Institute Zhejiang Police College Hangzhou310053 China College of Information Engineering Zhejiang University of Technology Hangzhou310023 China Key Laboratory of Public Security Information Application Based on Big-data Architecture Ministry of Public Security Hangzhou310053 China Zhejiang Police College Department of Investigation Hangzhou310053 China Zhejiang Police College Department of Computer and Information Security Hangzhou310053 China Science and Technology Management Section Hangzhou311203 China

ISBN: (纸本)9781665415989

Existing text classification algorithms generally have limitations in terms of text length and yield poor classification results for long texts. To address this problem, we propose a BERT-based long text classification method. First, we slice the long text and use BERT to encode the sliced clauses to obtain the local semantic information. Second, we use BiLSTM to fuse the local semantic information and adopt the attention mechanism to increase the weight of important clauses in the long text, so as to obtain the global semantic information. Finally, the global semantic information is input to the softmax layer for classification. Experimental results show that the proposed method achieves higher accuracy than commonly used models. © 2021 IEEE.

关键词： Classification (of information)

来源：评论

学校读者我要写书评

暂无评论

A fine-grained petri model for SQL time-blind injection

A fine-grained petri model for SQL time-blind injection

引用

International Conference on networking and network Applications (NaNA)

作者： Guiling Zhang Yaling Zhang Yichuan Wang Lei Zhu Wenjiang Ji School of computer science and engineering Xi’an University of technology Xi’an China Shaanxi Key Laboratory for Network Computing and Security Technology Xi’an China

With the background of severe SQL injection attacks, the existing SQL injection modeling methods cannot reflect the process of SQL injection attacks in a fine-grained manner. Based on the discussion of attack technology, this paper takes SQL time-blind injection as an example to model its process with Petri Net. The validity of the model is verified by quantitative analysis and qualitative analysis. Try to inject 10, 20, 30, 40 and 50 times into target aircraft and Petri Net model respectively. The blind injection time is recorded and compared. The results show that the injection time increases with the increase of injection times. Under the same injection times, the Petri Net model takes less time. The sending time in the token can be set. When the sending time is short, the injection speed is fast, and super real-time simulation can be realized, which can realize the rapid prediction of attacks and resource vulnerability effects. When the sending time is long, the injection process slows down. It is beneficial to observe the details of the injection process and whether conflicts occur at a fine-grained level, analyze the purpose of the attack and achieve the purpose of building a patch model. The patch model can effectively take countermeasures against attacks, predict unknown vulnerabilities and ensure network information security.

关键词： Analytical models Statistical analysis Firing Atmospheric modeling Petri nets Predictive models SQL injection

来源：评论

学校读者我要写书评

暂无评论

ConvCatb: An Attention-based CNN-CATBOOST Risk Prediction Model for Driving Safety

ConvCatb: An Attention-based CNN-CATBOOST Risk Prediction Mo...

引用

International Conference on networking and network Applications (NaNA)

作者： Xinhong Hei Hao Zhang Wenjiang Ji Yichuan Wang Lei Zhu Yuan Qiu College of Computer Science and Engineering Xi’an University of Technology Xi’an China Shaanxi Key Laboratory for Network Computing and Security Technology Xi’an China

Risk prediction is one of the most important tasks in assistant and automatic driving. In recent years, by the help of VANET and various sensors in the cars, the status of cars and roads can be collected in real time and used for data-driven based driving risk prediction. However, it is challenging to predict the driving risk due to the complex relationship between multiple environment factors like location, weather, time etc. Thus, a deep learning model ConvCatb was proposed in this paper, which improves the attention mechanism to the traditional Convolutional Neural networks, and combines the CatBoost algorithm to predict the current driving safety. The main idea is to emphasize the combination relationship between driving environment features through Non-local attention mechanism, and then use CatBoost to replace the softmax of CNN for classification. Finally, the experiment results show that the ConvCatb achieved superiorities in accuracy and F1-score, compared with existing schemes.

关键词： Machine learning algorithms Roads Vehicular ad hoc networks Prediction algorithms Real-time systems Classification algorithms Safety

来源：评论

学校读者我要写书评

暂无评论

Safeguarding Next Generation Multiple Access Using Physical Layer security Techniques: A Tutorial

arXiv

引用

arXiv 2024年

作者： Lv, Lu Xu, Dongyang Hu, Rose Qingyang Ye, Yinghui Yang, Long Lei, Xianfu Wang, Xianbin Kim, Dong In Nallanathan, Arumugam The State Key Laboratory of Integrated Services Networks Xidian University Xi’an710071 China The School of Information and Communications Engineering Xi’an Jiaotong University Xi’an710049 China The Department of Electrical and Computer Engineering Utah State University LoganUT84322 United States The Shaanxi Key Laboratory of Information Communication Network and Security Xi’an University of Posts & Telecommunications Xi’an710121 China The School of Information Science and Technology Southwest Jiaotong University Chengdu610031 China The Department of Electrical and Computer Engineering Western University LondonONN6A 5B9 Canada The Department of Electrical and Computer Engineering Sungkyunkwan University Suwon16419 Korea Republic of School of Electronic Engineering and Computer Science Queen Mary University of London E1 4NS United Kingdom

Driven by the ever-increasing requirements of ultrahigh spectral efficiency, ultra-low latency, and massive connectivity, the forefront of wireless research calls for the design of advanced next generation multiple access schemes to facilitate provisioning of these stringent demands. This inspires the embrace of non-orthogonal multiple access (NOMA) in future wireless communication networks. Nevertheless, the support of massive access via NOMA leads to additional security threats, due to the open nature of the air interface, the broadcast characteristic of radio propagation as well as intertwined relationship among paired NOMA users. To address this specific challenge, the superimposed transmission of NOMA can be explored as new opportunities for security aware design, for example, multiuser interference inherent in NOMA can be constructively engineered to benefit communication secrecy and privacy. The purpose of this tutorial is to provide a comprehensive overview on the state-of-the-art physical layer security techniques that guarantee wireless security and privacy for NOMA networks, along with the opportunities, technical challenges, and future research trends. Copyright © 2024, The Authors. All rights reserved.

关键词： Radio waves

来源：评论

学校读者我要写书评

暂无评论

A Driving Risk Prediction Approach Based on Generative Adversarial networks and VANET for Autonomous Trams

A Driving Risk Prediction Approach Based on Generative Adver...

引用

International Conference on networking and network Applications (NaNA)

作者： Wenjiang Ji Jiangcheng Yang Yichuan Wang Lei Zhu Yuan Qiu Xinhong Hei College of Computer Science and Engineering Xi’an University of Technology Xi’an China Shaanxi Key Laboratory for Network Computing and Security Technology Xi’an China

Driving safety is an essential prerequisite to the rapid development of autonomous trams. However, the relationship of driving risk factors is nonlinear, which makes modeling difficult. To improve the accuracy of driving risk prediction, a data driven approach based on Generative Adversarial networks was proposed. First of all, a communication and alarming scenario of Vehicular Ad-hoc networks was demonstrated, in which the original data sets can be collected and transmitted by the help of sensors and Road Side Units. Then the RFE feature selection algorithm was used to keep the key features. To deal the sample asymmetry problem, a DCGAN model was designed for sparse samples expansion. At last, the XGBoost algorithm was used to classification and output the risk prediction result. During the experiment implemented with the public and real data sets, the risk prediction accuracy of proposed approach can up to 97.24%, for which takes the advantages in generating of the sparse samples.

关键词： Road side unit Transportation Prediction methods Prediction algorithms Generative adversarial networks Classification algorithms Sensors

来源：评论

学校读者我要写书评

暂无评论

Smart Contract Vulnerability Detection Based on Dual Attention Graph Convolutional network 17th

Smart Contract Vulnerability Detection Based on Dual Attent...

引用

17th EAI International Conference on Collaborative Computing: networking, Applications, and Worksharing, CollaborateCom 2021

作者： Fan, Yuqi Shang, Siyuan Ding, Xu School of Computer Science and Information Engineering Hefei University of Technology Anhui Hefei230601 China Anhui Provincial Key Laboratory of Network and Information Security Anhui Normal Unviersity Anhui Wuhu241002 China Institute of Industry and Equipment Technology Hefei University of Technology Anhui Hefei230009 China

ISBN: (纸本)9783030926373

Smart contracts on blockchains have received increasing attention due to the decentralized, transparent, and immutable characteristics of blockchain. However, smart contracts are prone to security problems caused by critical vulnerabilities, which can lead to huge economic losses. Therefore, it is urgent to provide strong and robust security assurance for smart contracts. Most existing studies on smart contract vulnerability detection methods take heavy reliance on experts-defined rules, which are extremely time-consuming and labor-demanding. Moreover, the manually-set rules are limited to specific tasks and subject to errors. Although some studies explore the use of deep learning methods, they fail to represent both semantics and structural information. In this paper, we propose a novel model, Dual Attention Graph Convolutional network (DA-GCN), to detect vulnerabilities in smart contracts on blockchains. Both control flow graph and opcode sequence extracted from smart contract bytecodes are fed into the feature extractor based on graph convolutional network and self-attention mechanism. Model DA-GCN then uses control flow level attention to focus on the more important nodes in the control flow graph and suppress useless information. Finally, a multi layer perceptron is used to identify whether the smart contract is vulnerable. Experimental results on the real-world smart contract data set containing two vulnerabilities of reentrancy and timestamp dependency demonstrate that our proposed model DA-GCN can effectively improve the performance of smart contract vulnerability detection. © 2021, ICST Institute for computer Sciences, Social Informatics and Telecommunications engineering.

关键词： Smart contract

来源：评论

学校读者我要写书评

暂无评论

PPA: Preference Profiling Attack Against Federated Learning

arXiv

引用

arXiv 2022年

作者： Zhou, Chunyi Gao, Yansong Fu, Anmin Chen, Kai Dai, Zhiyang Zhang, Zhi Xue, Minhui Zhang, Yuqing School of Computer Science and Engineering Nanjing University of Science and Technology China State Key Laboratory of Information Security Institute of Information Engineering Chinese Academy of Science China Data61 CSIRO Sydney Australia National Computer Network Intrusion Protection Center University of Chinese Academy of Science China

Federated learning (FL) trains a global model across a number of decentralized users, each with a local dataset. Compared to traditional centralized learning, FL does not require direct access to local datasets and thus aims to mitigate data privacy concerns. However, data privacy leakage in FL still exists due to inference attacks, including membership inference, property inference, and data inversion. Copyright © 2022, The Authors. All rights reserved.

关键词： Data privacy

来源：评论

学校读者我要写书评

暂无评论

Rapid Maize Seedling Detection Based on Receptive-Field and Cross-Dimensional Information Interaction

Rapid Maize Seedling Detection Based on Receptive-Field and ...

引用

IEEE International Conference on Systems, Man and Cybernetics

作者： Fengqi Hao Shulei Zhu Dexin Ma Xiangjun Dong Hoiio Kong Xia Liu Chunhua Mu 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 Faculty of Data Science City University of Macau Macau China Shandong Provincial Key Laboratory of Computer Networks Shandong Fundamental Research Center for Computer Science Jinan China Communication College Qingdao Agricultural University Qingdao China National Engineering Laboratory of Wheat and Maize/Key Laboratory of Biology and Genetic Improvement of Maize in Northern Yellow-huai River Plain Ministry of Agriculture Maize Research Institute Shandong Academy of Agricultural Sciences Jinan China

ISBN: (数字)9781665410205

ISBN: (纸本)9781665410212

The seedling stage is important in the growth and development of maize, and it is also a critical period that affects maize yield and quality. Accurately recognizing this stage of maize is challenging, as current maize seedling detection methods struggle with small sizes and complex environments. This paper proposes a rapid maize seedling detection method that emphasizes spatial feature extraction in the receptive field and cross-dimensional interaction—receptive field triplet attention YOLO (RFT-YOLO). First, we design a convolutional module for cross-dimensional information interaction within the receptive field. Second, we introduce an advanced selective fusion network, which boosts the multi-scale fusion capability of small object features while reducing the GFLOPs of the model. Additionally, we employ Inner-IoU to diminish sensitivity to positional biases of small objects and expedite the convergence of the loss function. Finally, we create a dataset of maize seedlings to conduct our experiments. Experimental results demonstrate that the RFT-YOLO significantly enhances performance, reducing the number of parameters by 35.3% and GFLOPs by 14.24%, compared to the baseline model. Moreover, the mean average precision (mAP) improves from 89.6% to 91.5%. These improvements confirm the model's effectiveness in detecting maize seedlings.

关键词： YOLO Sensitivity Accuracy Semantics Feature extraction Complexity theory Data mining Cybernetics Convergence

来源：评论

学校读者我要写书评

暂无评论

Task Offloading and Primary Node Selection in Blockchain, Digital Twin, and MEC Enabled Internet of Vehicles

Task Offloading and Primary Node Selection in Blockchain, Di...

引用

IEEE Conference on Vehicular Technology (VTC)

作者： Jianbo Du Huifang Fang Shulei Li Pengfei Du Ziwen Kong Jiawen Kang Dusit Niyato Shaanxi Key Laboratory of Information Communication Network and Security School of Communications and Information Engineering Xi’an University of Posts and Telecommunications Xi’an China School of Communications Engineering Xidian University Xi’an China Tianyuan Ruixin Communication Technology Co. LTD. Xi’an China Engineering Research Center of Intelligent Airground Integrated Vehicle and Traffic Control Ministry of Education Xihua University Chengdu China School of Automation Guangdong University of Technology China School of Computer Science and Engineering Nanyang Technological University Singapore

ISBN: (数字)9798331517786

ISBN: (纸本)9798331517793

In this paper, we investigate the safe task offloading and primary node selection in blockchain, digital twin (DT) and Multi-access Edge Computing (MEC) enabled Internet of Vehicles (IoV). Edge servers centers provide computing power for task processing for Mobile Vehicles (MVs), while blockchain can provide security guarantees for MVs during task offloading. Based on the above system, we propose a joint optimization scheme for vehicle task offloading decision and the Practical Byzantine Fault Tolerance (PBFT) consensus process. Due to the large number of optimization variables and constraints, the problem becomes more complex. Traditional convex optimization and dynamic programming methods are difficult to effectively solve this problem. To address this issue, we propose a deep reinforcement learning based algorithm that utilizes Proximal Policy Optimization (PPO). The experimental results show that the algorithm proposed in this paper outperforms the benchmark algorithm in terms of convergence and other aspects.

关键词： Fault tolerance Vehicular and wireless technologies Heuristic algorithms Simulation Fault tolerant systems Blockchains Digital twins Servers Optimization Internet of Vehicles

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：