检索结果-内蒙古大学图书馆

44th ACM/IEEE International Conference on Software Engineering, ICSE 2024

作者： Li, Zhen Wang, Ning Zou, Deqing Li, Yating Zhang, Ruqian Xu, Shouhuai Zhang, Chao Jin, Hai Hubei Key Laboratory of Distributed System Security Hubei Engineering Research Center on Big Data Security Cluster and Grid Computing Lab National Engineering Research Center for Big Data Technology and System Services Computing Technology and System Lab Hong Kong Jin YinHu Laboratory Wuhan China School of Cyber Science and Engineering Huazhong University of Science and Technology Wuhan China University of Colorado Colorado Springs Department of Computer Science Colorado Springs Colorado United States Institute for Network Sciences and Cyberspace Tsinghua University Beijing China School of Computer Science and Technology Huazhong University of Science and Technology Wuhan China

ISBN: (纸本)9798400702174

Software vulnerabilities are a major cyber threat and it is important to detect them. One important approach to detecting vulnerabilities is to use deep learning while treating a program function as a whole, known as function-level vulnerability detectors. However, the limitation of this approach is not understood. In this paper, we investigate its limitation in detecting one class of vulnerabilities known as inter-procedural vulnerabilities, where the to-be-patched statements and the vulnerability-triggering statements belong to different functions. For this purpose, we create the first Inter -Procedural Vulnerability dataset (InterPVD) based on C/C++ open-source software, and we propose a tool dubbed VulTrigger for identifying vulnerability-triggering statements across functions. Experimental results show that VulTrigger can effectively identify vulnerability-triggering statements and inter-procedural vulnerabilities. Our findings include: (i) inter-procedural vulnerabilities are prevalent with an average of 2.8 inter-procedural layers;and (ii) function-level vulner-ability detectors are much less effective in detecting to-be-patched functions of inter-procedural vulnerabilities than detecting their counterparts of intra-procedural vulnerabilities. © 2024 ACM.

关键词： Open source software

来源：评论

学校读者我要写书评

暂无评论

A robust three-way classifier with shadowed granular-balls based on justifiable granularity

arXiv

引用

arXiv 2024年

作者： Yang, Jie Xiaodiao, Lingyun Wang, Guoyin Pedrycz, Witold Xia, Shuyin Zhang, Qinghua Wu, Di The Key Laboratory of Cyberspace Big Data Intelligent Security Ministry of Education Chongqing University of Posts and Telecommunications Chongqing400065 China The School of Physics and Electronic Science Zunyi Normal University Zunyi563002 China The Key Laboratory of Cyberspace Big Data Intelligent Security Chongqing University of Posts and Telecommunications Chongqing400065 China The Department of Electrical and Computer Engineering University of Alberta EdmontonAB Canada College of Computer and Information Science Southwest University Chongqing400715 China

The granular-ball (GB)-based classifier introduced by Xia, exhibits adaptability in creating coarse-grained information granules for input, thereby enhancing its generality and flexibility. Nevertheless, the current GB-based classifiers rigidly assign a specific class label to each data instance and lacks of the necessary strategies to address uncertain instances. These far-fetched certain classification approachs toward uncertain instances may suffer considerable risks. To solve this problem, we construct a robust three-way classifier with shadowed GBs for uncertain data. Firstly, combine with information entropy, we propose an enhanced GB generation method with the principle of justifiable granularity. Subsequently, based on minimum uncertainty, a shadowed mapping is utilized to partition a GB into Core region, Important region and Unessential region. Based on the constructed shadowed GBs, we establish a three-way classifier to categorize data instances into certain classes and uncertain case. Finally, extensive comparative experiments are conducted with 2 three-way classifiers, 3 state-of-the-art GB-based classifiers, and 3 classical machine learning classifiers on 12 public benchmark datasets. The results show that our model demonstrates robustness in managing uncertain data and effectively mitigates classification risks. Furthermore, our model almost outperforms the other comparison methods in both effectiveness and efficiency. Copyright © 2024, The Authors. All rights reserved.

关键词： Machine learning

来源：评论

学校读者我要写书评

暂无评论

PRAD: Unsupervised KPI Anomaly Detection by Joint Prediction and Reconstruction of Multivariate Time Series 19

PRAD: Unsupervised KPI Anomaly Detection by Joint Prediction...

引用

2022 IEEE SmartWorld, 19th IEEE International Conference on Ubiquitous Intelligence and Computing, 2022 IEEE International Conference on Autonomous and Trusted Vehicles Conference, 22nd IEEE International Conference on Scalable Computing and Communications, 2022 IEEE International Conference on Digital Twin, 8th IEEE International Conference on Privacy Computing and 2022 IEEE International Conference on Metaverse, SmartWorld/UIC/ATC/ScalCom/DigitalTwin/PriComp/Metaverse 2022

作者： Xiong, Zhiying Fan, Qilin Wang, Kai Li, Xiuhua Zhang, Xu Xiong, Qingyu Chongqing University School of Big Data and Software Engineering Chongqing China Chongqing University Chongqing Key Laboratory of Digital Cinema Art Theory and Technology Chongqing China Harbin Institute of Technology School of Computer Science and Technology Weihai China Harbin Institute of Technology Research Institute of Cyberspace Security Weihai China Haihe Laboratory of Information Technology Application Innovation Tianjin China University of Exeter Department of Computer Science Exeter United Kingdom

ISBN: (纸本)9798350346558

Detecting anomalies for key performance indicator (KPI) data is of paramount importance to ensure the quality and reliability of network services. However, building the anomaly detection system for KPI is challenging due to the scarcity of abnormal labels and highly dynamic or even unseen patterns. In this paper, we propose an unsupervised KPI anomaly detection framework named PRAD by jointly optimizing prediction-based and reconstruction-based modules. Specifically, PRAD employs two parallel graph attention networks (GATs) to learn metric-oriented and time-oriented relationships. A temporal convolutional attention network (TCAN) is used to capture complex long-term dependencies. Furthermore, to tackle the overfitting issue in the variational auto-encoder (VAE), PRAD designs a reconstruction-based module that combines VAE with a generative adversarial network (GAN). The experimental results on the two public datasets demonstrate that the proposed PRAD outperforms existing anomaly detection methods. © 2022 IEEE.

关键词： Anomaly detection

来源：评论

学校读者我要写书评

暂无评论

PP-LDG: A Medical Privacy-Preserving Labeled data Generation Framework

PP-LDG: A Medical Privacy-Preserving Labeled Data Generation...

引用

Medical Artificial Intelligence (MedAI), IEEE International Conference on

作者： Haoxiang Yuan Xiaochen Yuan Xiuli Bi Weisheng Li Guoyin Wang Bin Xiao Chongqing Key Laboratory of Image Cognition School of Computer Science and Technology Chongqing University of Posts and Telecommunications Chongqing China Faculty of Applied Sciences Macao Polytechnic University Macao China Key Laboratory of Cyberspace Big Data Intelligent Security Ministry of Education Chongqing China

ISBN: (数字)9798350377613

ISBN: (纸本)9798350377620

The rapid development of deep learning has led to an increasing demand for data. However, such data are scarce in many fields and often contain private and sensitive information, such as medical images. The fact that data owners are unwilling to share these high-privacy data further exacerbates the scarcity of data. A prevalent research direction is to combine differential privacy and image generation, by which we can obtain a large amount of synthetic data with a similar distribution to the original dataset, and that synthetic dataset does not compromise the privacy of the original dataset. However, these methods cannot provide labels for the generated data, limiting the subsequent uses of the data. To solve the problem, we propose a generic privacy-preserving labeled data generation framework named PP-LDG. It is the first data publishing framework to generate labeled data while not disclosing the privacy of original data. We theoretically demonstrate that our proposed framework can provide strict privacy guarantees with differential privacy and demonstrate the effectiveness of the synthetic dataset obtained from the framework under practical privacy budgets through extensive experiments.

关键词： Deep learning Differential privacy Accuracy Limiting Image synthesis Publishing data collection Synthetic data Biomedical imaging Image classification

来源：评论

学校读者我要写书评

暂无评论

Identifying data Breaches in Dark Web through Prompt Active Learning

Identifying Data Breaches in Dark Web through Prompt Active ...

引用

IEEE International Conference on data Science in cyberspace (DSC)

作者： Hui Xiang Yifan Wu Xuebin Wang Jinqiao Shi Can Zhao Jiapeng Zhao School of Cyberspace Security Beijing University of Posts and Telecommunications Beijing China The State Key Laboratory of Blockchain and Data Security Zhejiang University Hangzhou China Key Laboratory of Trustworthy Distributed Computing and Service (BUPT) Ministry of Education Beijing China School of Cyber Security University of Chinese Academy of Sciences Beijing China Institute of Information Engineering Chinese Academy of Sciences Beijing China

ISBN: (数字)9798350391367

ISBN: (纸本)9798350391374

Frequent data breaches have attracted people’s attention. The dark web forums have become the crucial platform for data breach transactions. While some forums are well-organized with distinct sections, such as a dedicated area for leaks where most threads are related to data breaches, others lack clear segmentation, like Fear Forum. Hackers conduct a variety of discussions there so it is filled with tons of non-data breach information and building a data breach dataset for a classifier requires huge human resources. The content of these threads is diverse and complex, spanning various fields, and containing specific terminology in the dark web. Identifying data breach threads in these forums is extremely challenging for the model. With the influx of new threads every day the static model trained on old data may not perform well when processing new data, which also brings serious difficulty to identification. To address these challenges, we first design Prompt-based Active Learning (PAL) to guide the model to identify through prompts and select high-quality samples for annotation. Then we propose a system that combines PAL and DarkBERT. Our system can not only greatly reduce annotation costs but also continuously learn and update itself with the new informative data selected by PAL. Through rich experiments on a data breach dataset, we demonstrate that our system effectively identifies breach threads and PAL significantly outperforms other baselines. We also search for the optimal prompt templates to improve system performance.

关键词： Dark Web Costs Annotations Terminology Instruction sets System performance Active learning Semantics data breach data models

来源：评论

学校读者我要写书评

暂无评论

MCU-Net: A Multi-prior Collaborative Deep Unfolding Network with Gates-controlled Spatial Attention for Accelerated MR Image Reconstruction

arXiv

引用

arXiv 2024年

作者： Qiao, Xiaoyu Li, Weisheng Wang, Guofen Huang, Yuping Chongqing Key Laboratory of Image Cognition Chongqing University of Posts and Telecommunications Chongqing China Key Laboratory of Cyberspace Big Data Intelligent Security Chongqing University of Posts and Telecommunications Ministry of Education Chongqing China College of Computer and Information Science Chongqing Normal University Chongqing China

Deep unfolding networks (DUNs) have demonstrated significant potential in accelerating magnetic resonance imaging (MRI). However, they often encounter high computational costs and slow convergence rates. Besides, they struggle to fully exploit the complementarity when incorporating multiple priors. In this study, we propose a multi-prior collaborative DUN, termed MCU-Net, to address these limitations. Our method features a parallel structure consisting of different optimization-inspired subnetworks based on low-rank and sparsity, respectively. We design a gates-controlled spatial attention module (GSAM), evaluating the relative confidence (RC) and overall confidence (OC) maps for intermediate reconstructions produced by different subnetworks. RC allocates greater weights to the image regions where each subnetwork excels, enabling precise element-wise collaboration. We design correction modules to enhance the effectiveness in regions where both subnetworks exhibit limited performance, as indicated by low OC values, thereby obviating the need for additional branches. The gate units within GSAMs are designed to preserve necessary information across multiple iterations, improving the accuracy of the learned confidence maps and enhancing robustness against accumulated errors. Experimental results on multiple datasets show significant improvements on PSNR and SSIM results with relatively low FLOPs compared to cutting-edge methods. Additionally, the proposed strategy can be conveniently applied to various DUN structures to enhance their performance. Copyright © 2024, The Authors. All rights reserved.

关键词： Magnetic resonance imaging

来源：评论

学校读者我要写书评

暂无评论

A Synchronous Parallel Method with Parameters Communication Prediction for Distributed Machine Learning 19th

A Synchronous Parallel Method with Parameters Communication...

引用

19th EAI International Conference on Collaborative Computing: Networking, Applications and Worksharing, CollaborateCom 2023

作者： Zeng, Yanguo Xue, Meiting Xu, Peiran Shi, Yukun Zeng, Kaisheng Zhang, Jilin Yue, Lupeng School of Computer Science and Technology Hangzhou Dianzi University Hangzhou310018 China Key Laboratory for Modeling and Simulation of Complex Systems Ministry of Education Hangzhou310018 China Data Security Governance Zhejiang Engineering Research Center Hangzhou310018 China National University of Defense Technology Changsha China School of Cyberspace HangZhou Dianzi University Hangzhou China Department of Computer Science and Technology Tsinghua University Beijing China

ISBN: (纸本)9783031545306

With the development of machine learning technology in various fields, such as medical care, smart manufacturing, etc., the data has exploded. It is a challenge to train a deep learning model for different application domains with large-scale data and limited resources of a single device. The distributed machine-learning technology, which uses a parameter server and multiple clients to train a model collaboratively, is an excellent method to solve this problem. However, it needs much communication between different devices with limited communication resources. The stale synchronous parallel method is a mainstream communication method to solve this problem, but it always leads to high synchronization delay and low computing efficiency as the inappropriate delay threshold value set by the user based on experience. This paper proposes a synchronous parallel method with parameters communication prediction for distributed machine learning. It predicts the optimal timing for synchronization, which can solve the problem of long synchronization waiting time caused by the inappropriate threshold settings in the stale synchronous parallel method. Moreover, it allows fast nodes to continue local training while performing global synchronization, which can improve the resource utilization of work nodes. Experimental results show that compared with the delayed synchronous parallel method, the training time and quality, and resource usage of our method are both significantly improved. © ICST Institute for Computer Sciences, Social Informatics and Telecommunications Engineering 2024.

关键词： Forecasting

来源：评论

学校读者我要写书评

暂无评论

PSSPR: A Source Location Privacy Protection Scheme Based on Sector Phantom Routing in WSNs

PSSPR: A Source Location Privacy Protection Scheme Based on ...

引用

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

作者： Jing Sun Yuling Chen Tao Li Jia Liu Yixian Yang State Key Laboratory of Public Big Data College of Computer Science and Technology Guizhou University Guiyang China Air Force Engineering University Xi'an China School of Cyberspace Security Beijing University of Posts and Telecommnuications Beijing China

ISBN: (纸本)9781665421751

Source location privacy, occupying an unshakable status in wireless sensor networks, has a profound influence on the widespread application of wireless sensor networks (WSNs). Most privacy policies that introduce phantom nodes to the source location have the problem that the transmission path between the phantom node and the source is not diversified enough, and the phantom node area is close to the source. For this reason, a source location privacy protection scheme based on sector domain phantom routing is proposed. The scheme uses the sector domain and the coordinates of the central node $V$ to increase the geographical position diversity of the phantom node, which completely avoids the visible and protects the security of the source location. Theoretical analysis and simulation results show that our scheme can enhance the security of source position.

关键词： Wireless communication Wireless sensor networks Privacy Simulation Phantoms Position measurement Routing

来源：评论

学校读者我要写书评

暂无评论

Locally Private Estimator for Degree Distribution with Laplacian Mechanism

Locally Private Estimator for Degree Distribution with Lapla...

引用

Smart World Congress (SWC), IEEE

作者： Jiayu Li Yuke Hu Xiaoguang Li Shiqi Zhou Yuxiang Wang Fenghua Li Ben Niu Chinese Academy of Sciences Institute of Information Engineering P.R. China School of Cyber Security University of Chinese Academy of Sciences P.R. China Key Laboratory of Cyberspace Security Defense P.R. China The State Key Laboratory of Blockchain and Data Security Zhejiang University P.R. China School of Cyber Engineering Xidian University P.R. China

ISBN: (数字)9798331520861

ISBN: (纸本)9798331520878

Local differential privacy (LDP) provides a solution such that an untrusted server can achieve privacy-preserving data analysis. Node degree is one of the most fundamental metrics in graph data analysis. Existing LDP-based methods can not balance degree distribution estimation and degree distribution characteristic estimation. In this paper, we propose a local private estimator for degree distribution with Laplacian mechanism (LEDL), which uses elastic net regression to estimate the degree distribution, selects the regularization weight to prevent overfitting or underfitting. We can aslo adopt the existing method to calculate degree distribution characteristics according to data with Laplacian noise. Experiments demonstrate the effectiveness of LEDL in degree distribution and Laplacian mechanism in degree distribution characteristic estimation, six metrics are applied across four datasets.

关键词： Laplace equations data analysis Accuracy Estimation Probability Vectors Stability analysis Servers Noise measurement Overfitting

来源：评论

学校读者我要写书评

暂无评论

Cloud-assisted Road Condition Monitoring with Privacy Protection in VANETs

Cloud-assisted Road Condition Monitoring with Privacy Protec...

引用

International Conference on Mobile Ad-hoc and Sensor Networks, MSN

作者： Lemei Da Yujue Wang Yong Ding Bo Qin Xiaochun Zhou Hai Liang Huiyong Wang Guangxi Key Laboratory of Cryptography and Information Security School of Computer Science and Information Security Guilin University of Electronic Technology Guilin China Pengcheng Laboratory Cyberspace Security Research Center Shenzhen China Key Laboratory of Data Engineering and Knowledge Engineering Ministry of Education School of Information Renmin University of China Beijing China Shenzhen Olym Information Security Technology Co. Ltd. Shenzhen China School of Mathematics and Computing Science Guilin University of Electronic Technology Guilin China

Vehicular ad hoc network (VANET) is one of the fastest developing technologies in intelligent transportation systems (ITS), which has made great contributions to improving traffic congestion and reducing traffic accidents. As it is deployed in an open environment, security and privacy are threatened to a certain extent. Moreover, there are huge data exchanges in high traffic areas, which require VANET system to improve computing efficiency while ensuring communication security. To solve the above issues, this paper proposes a cloud-assisted road condition monitoring (RCM) system. The trusted authority (TA) monitors the road conditions with the help of the cloud server. The vehicle collects the road condition information of the road section managed by the roadside unit (RU), and only the vehicles authorized by the administrative roadside unit can successfully upload the road condition reports to the cloud server. The cloud server divides the road condition reports into different equivalence classes, in this way to report the emergency to the TA when the reported quantity exceeds the threshold. security analysis showed that the proposed RCM system can effectively protect the security and privacy of road condition reports in VANETs.

关键词： Condition monitoring Privacy data privacy Roads Vehicular ad hoc networks Forgery Sensors

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：