检索结果-内蒙古大学图书馆

IEEE International Conference on Trust, Security and Privacy in Computing and Communications (TrustCom)

作者： Changnan Jiang Chunhe Xia Chen Chen Huacheng Li Tianbo Wang Xiaojian Li Beijing Key Lab. of Network Technology Beihang University Beijing China Guangxi Collaborative Innovation Center of Multi-Source Information Integration and Intelligent Processing Guangxi Normal University Guilin China School of Cyber Science and Technology Beihang University Beijing China Shanghai Key Laboratory of Computer Software Evaluating and Testing Shanghai China College of Computer Science and Information Technology Guangxi Normal University Guilin China

ISBN: (数字)9798350381993

ISBN: (纸本)9798350382006

In the traditional centralized Android malware classification framework, privacy concerns arise as it requires collecting users’ app samples containing sensitive information directly. To address this problem, new classification frameworks based on Federated Learning (FL) have emerged for privacy preservation. However, research shows that these frameworks still face risks of indirect information leakage due to adversary inference. Unfortunately, existing research lacks an effective assessment of the extent and location of this leakage risk. To bridge the gap, we propose the FedDLM, which provides a fine-grained assessment of the risk of sensitive information leakage in an FL-based Android malware classifier. FedDLM estimates attackers’ theoretical maximum inference ability from the information theory perspective to gauge the degree of leakage risk in the classifier effectively. It precisely identifies critical positions in the shared gradient where the leakage risk exists by utilizing characteristics of class activation in classifiers. Through extensive experiments on the Androzoo dataset, FedDLM demonstrates its superior effectiveness and precision compared to baseline methods in evaluating the risk of sensitive information leakage. The evaluation results provide valuable insights into information leakage problems in classifiers and targeted privacy protection methods.

关键词： Meters Privacy Federated learning Information leakage Malware Security Risk management

来源：评论

学校读者我要写书评

暂无评论

HF-Mid: A Hybrid Framework of Network Intrusion Detection for Multi-type and Imbalanced Data

HF-Mid: A Hybrid Framework of Network Intrusion Detection fo...

引用

IEEE International Conference on Trust, Security and Privacy in Computing and Communications (TrustCom)

作者： Weidong Zhou Tianbo Wang Guotao Huang Xiaopeng Liang Chunhe Xia Xiaojian Li Beijing Key Lab. of Network Technology Beihang University Beijing China School of Cyber Science and Technology Beihang University Beijing China Shanghai Key Laboratory of Computer Software Evaluating and Testing Shanghai China Guangxi Collaborative Innovation Center of Multi-source Information Integration and Intelligent Processing Guangxi Normal University Guilin China College of Computer Science and Information Technology Guangxi Normal University Guilin China

ISBN: (数字)9798350381993

ISBN: (纸本)9798350382006

The data-driven deep learning methods have brought significant progress and potential to intrusion detection. However, there are two thorny problems caused by the characteristics of intrusion data: "multi-type features" and "data imbalance". The former means that forcefully and improperly transforming intrusion features from distinct metric spaces can result in semantic loss and noise. The latter indicates that the intrusion data is imbalanced in quantity and quality due to its complex spatial distribution. We propose a Hybrid Framework for Multi-type and Imbalance Data (HF-Mid) to address the above two problems. Firstly, we divide the intrusion features into equivalent and non-equivalent groups, and then embed them sequentially using Supervised Paragraph Vector-Distributed Memory (SPV-DM), which excels at modeling co-occurrence relationships, and Deep Neural Network (DNN), which is suitable for modeling non-linear relationships, thereby solving the "multitype features" problem. Secondly, we adopt a low-noise collective matrix factorization (CMF) model to fuse the two obtained features for dimensionality reduction. Finally, we employ a multiple classifier to detect intrusion. During the classifier training stage, we design a genetic algorithm-based proportional sampling method to select high-quality samples in each training batch. thus addressing the "data imbalance" problem. The experimental results demonstrate the proposed framework exhibits an overall improvement of 5.9% and 1.5% in terms of accuracy and false positive rate on average, respectively.

关键词： Training Privacy Fuses Noise Semantics Artificial neural networks Feature extraction

来源：评论

学校读者我要写书评

暂无评论

MLFuse: Multi-Scenario Feature Joint Learning for Multi-Modality Image Fusion

引用

IEEE Transactions on multimedia 2025年

作者： Lei, Jia Li, Jiawei Liu, Jinyuan Wang, Bin Zhou, Shihua Zhang, Qiang Wei, Xiaopeng Kasabov, Nikola K. Dalian University Key Laboratory of Advanced Design and Intelligent Computing Ministry of Education School of Software Engineering Dalian116622 China University of Science and Technology Beijing School of Computer and Communication Engineering Beijing100083 China Dalian University of Technology School of Mechanical Engineering Dalian116024 China Dalian University of Technology School of Computer Science and Technology Dalian116024 China Auckland University of Technology Knowledge Engineering and Discovery Research Institute Auckland1010 New Zealand Ulster University Intelligent Systems Research Center LondonderryBT52 1SA United Kingdom

Multi-modality image fusion (MMIF) entails synthesizing images with detailed textures and prominent objects. Existing methods tend to use general feature extraction to handle different fusion tasks. However, these methods have difficulty breaking fusion barriers across various modalities owing to the lack of targeted learning routes. In this work, we propose a multi-scenario feature joint learning architecture, MLFuse, that employs the commonalities of multi-modality images to deconstruct the fusion progress. Specifically, we construct a cross-modal knowledge reinforcing network that adopts a multipath calibration strategy to promote information communication between different images. In addition, two professional networks are developed to maintain the salient and textural information of fusion results. The spatial-spectral domain optimizing network can learn the vital relationship of the source image context with the help of spatial attention and spectral attention. The edge-guided learning network utilizes the convolution operations of various receptive fields to capture image texture information. The desired fusion results are obtained by aggregating the outputs from the three networks. Extensive experiments demonstrate the superiority of MLFuse for infrared-visible image fusion and medical image fusion. The excellent results of downstream tasks (i.e., object detection and semantic segmentation) further verify the high-quality fusion performance of our method. The code is publicly available at https://***/jialei-sc/MLFuse. © 1999-2012 IEEE.

关键词： Semantic Segmentation

来源：评论

学校读者我要写书评

暂无评论

CoHop

引用

ACM Transactions on Sensor Networks 2021年第2期17卷

作者： Wang, Yuting Zheng, Xiaolong Liu, Liang Ma, Huadong School of Computer Science Beijing Key Laboratory of Intelligent Telecommunication Software and Multimedia Beijing University of Posts and Telecommunications No. 10 Xitucheng Road Beijing100876 China

Cross-technology Interference (CTI) badly harms the transmission reliability for low-power networks such as ZigBee at 2.4-GHz band. Though promising, channel hopping still faces challenges because the increasingly dense deployment of CTI leaves very few available channels. Selecting a good channel with the least overhead is crucial but challenging. Most of the existing works are heuristic methods that choose a channel far from the current one to avoid adjacent channels that may be correlatively interfered by CTI with a wider bandwidth such as WiFi. However, we observe that the correlated channels influenced by the same CTI source do not necessarily have the same channel qualities and even the opposite state, due to the uneven spectrum power density of CTI. Such channel opportunities are unexplored and wasted. In this article, we propose CoHop, a quantitative correlation-based channel hopping method for low-power wireless networks. We establish a quantitative model that describes the correlation of channel qualities to capture channel opportunities and calculate channel quality without probing, to reduce probing overhead. The probing sequence is optimized based on the Pearson Correlation Coefficient and the prediction-based probing algorithm. We implement CoHop on TinyOS and evaluate its performance in various environments. The experimental results show that CoHop can increase the Packet Reception Ratio by 80%, compared with existing methods. © 2021 ACM.

关键词： Heuristic methods

来源：评论

学校读者我要写书评

暂无评论

Extract the knowledge of graph neural networks and go beyond it: An effective knowledge distillation framework

arXiv

引用

arXiv 2021年

作者： Yang, Cheng Liu, Jiawei Shi, Chuan School of Computer Science Beijing University of Posts and Telecommunications China Beijing Key Laboratory of Intelligent Telecommunications Software and Multimedia China

Semi-supervised learning on graphs is an important problem in the machine learning area. In recent years, state-of-the-art classification methods based on graph neural networks (GNNs) have shown their superiority over traditional ones such as label propagation. However, the sophisticated architectures of these neural models will lead to a complex prediction mechanism, which could not make full use of valuable prior knowledge lying in the data, e.g., structurally correlated nodes tend to have the same class. In this paper, we propose a framework based on knowledge distillation to address the above issues. Our framework extracts the knowledge of an arbitrary learned GNN model (teacher model), and injects it into a well-designed student model. The student model is built with two simple prediction mechanisms, i.e., label propagation and feature transformation, which naturally preserves structure-based and feature-based prior knowledge, respectively. In specific, we design the student model as a trainable combination of parameterized label propagation and feature transformation modules. As a result, the learned student can benefit from both prior knowledge and the knowledge in GNN teachers for more effective predictions. Moreover, the learned student model has a more interpretable prediction process than GNNs. We conduct experiments on five public benchmark datasets and employ seven GNN models including GCN, GAT, APPNP, SAGE, SGC, GCNII and GLP as the teacher models. Experimental results show that the learned student model can consistently outperform its corresponding teacher model by 1.4% ∼ 4.7% on average. Code and data are available at https://***/BUPT-GAMMA/CPF © 2021, CC BY.

关键词： Graph neural networks

来源：评论

学校读者我要写书评

暂无评论

A method for detecting floating objects on water based on edge computing

A method for detecting floating objects on water based on ed...

引用

2023 IEEE International Symposium on Broadband multimedia Systems and Broadcasting, BMSB 2023

作者： Li, He Yang, Shuaipeng Liu, Jinjiang Fang, Honglin Fu, Zhumu Zhang, Rui Jia, Huimei Lv, Lianmeng Henan Costar Group Co. Ltd Henan Nanyang473003 China Henan University of Science and Technology College of Information Engineering Henan Luoyang471000 China Nanyang Normal University Henan Engineering Research Center of Intelligent Processing for Big Data of Digital Image School of Computer Science and Technology Nanyang473061 China Beijing University of Posts and Telecommunication State Key Laboratory of Networking and Switching Technology Beijing100876 China Xi'an Hengpin Electronic Technology Co. Ltd Xi'an710100 China

ISBN: (纸本)9798350321524

With the development and application of computer vision, many target detection networks are applied to the detection of floating objects in rivers. For the detection problems such as small targets easily missed and misdetected in water surface floating object detection tasks and difficult to deploy models. An edge computing-oriented approach to river floater detection is proposed. First, a four-fold down-sampling feature layer is added to the YOLOv5 network which enhances more target detail features and improves the detection capability of small objects. Second, CA (Coordinate Attention) is added to the Backbone to suppress background noise interference, and different pooling is used to accommodate different hierarchical features. Then, a bilinear interpolation method is adopted for up-sampling to avoid the loss of small object features. Design a data enhancement algorithm for small targets based on Mosaic to increase the number of small objects and enrich the training background. Finally, for the edge computing architecture platform, the channel pruning algorithm is used to prune and compress the model structure to adapt to the computing capability of edge devices. The experimental results show that the method can effectively improve the detection capability of the network for floating objects on the water surface. The detection accuracy can reach 93.6%, and the detection speed can be maintained at 36 frames per second, which can achieve high-precision real-time detection of floating objects on the water surface. © 2023 IEEE.

关键词： Object detection

来源：评论

学校读者我要写书评

暂无评论

An Energy-Efficient Load Balance Strategy Based on Virtual Machine Consolidation in Cloud Environment

SSRN

引用

SSRN 2022年

作者： Yao, Wenbin Wang, Zhuqing Hou, Yingying Zhu, Xikang Li, Xiaoyong Xia, Yamei School of Computer science Beijing University of Posts and Telecommunications Beijing100876 China Beijing Key Laboratory of Intelligent Telecommunications Software and Multimedia Beijing University of Posts and Telecommunications Beijing100876 China School of Cyberspace Security Beijing University of Posts and Telecommunications Beijing100876 China

In the cloud computing environment, unbalanced utilization of multi-dimensional resources in physical servers generates resource fragmentation, leading to inefficient resource utilization and energy wastage in data centers. The inefficient utilization of resources makes high energy consumption and low quality of service (QoS) in resource management an urgent problem to be solved. We propose a load balancing strategy based on virtual machine consolidation (LBVMC), which aims to reduce the energy consumption and service level agreement (SLA) violation of data centers by balancing the multi-dimensional resource utilization in physical machines (PMs). First, we present a load state classification algorithm for PMs with load abnormality considering current and future loads to reduce unnecessary virtual machine (VM) migrations caused by occasional load fluctuations. Then, we propose a resource-weight based selection model for migratable VMs, which selects appropriate VMs to be migrated based on multi-dimensional resource utilization, and reduces resource fragmentation caused by load imbalance. Finally, we design a VM placement algorithm based on resource fitness and load correlation to deploy VMs on the optimal destination PMs to assure load balancing of the destination PMs after VM placement. We perform simulated experiments under homogeneous, heterogeneous, and bottleneck resource environments. Experimental results show that LBVMC is superior to other strategies in reducing energy consumption and SLA violations and achieves better overall performance. © 2022, The Authors. All rights reserved.

关键词： Cloud computing

来源：评论

学校读者我要写书评

暂无评论

A Lattice-Based Semantic Aware Multi-keyword Searchable Encryption for Multi-User Environments

SSRN

引用

SSRN 2022年

作者： Hou, Yingying Yao, Wenbin Li, Xiaoyong School of Computer science Beijing University of Posts and Telecommunications Beijing100876 China Beijing Key Laboratory of Intelligent Telecommunications Software and Multimedia Beijing University of Posts and Telecommunications Beijing100876 China School of Cyberspace Security Beijing University of Posts and Telecommunications Beijing100876 China

Searchable encryption is a key technology that can perform retrieval computation on ciphertext. The existing multi-keyword searchable encryption schemes generally use "coordinate matching" technology, which make the retrieval results accurate and efficient. However, in a multi-user environment, all users in such searchable encryption systems will share the same key and default ciphertext splitting rules, which leads to the risk of privacy leakage of retrieval information between users. To solve the problem, this paper proposes a lattice-based semantic aware multi-keyword searchable encryption for multi-user environments. We first design a searchable encryption model that supports multi-keyword search, multi-user retrieval, semantic-aware retrieval, and ranked retrieval functions to ensure that our retrieval system has stronger query expressiveness and complete retrieval capability. Then, in order to make all users have independent retrieval rights and there is no possibility of ciphertext privacy leakage among users, we propose a secure multi-user retrieval scheme based on proxy re-encryption. Thirdly, a simple homomorphic encryption OT protocol is proposed, which enables users to have secure channels when interacting with the topic management server. Finally, the construction of this scheme is based on GSW fully homomorphic encryption, which can resist quantum attacks and has stronger security. Through the security analysis, it is proved that the scheme is secure and achieves all the security goals proposed in the paper. © 2022, The Authors. All rights reserved.

关键词： Semantics

来源：评论

学校读者我要写书评

暂无评论

A software Defect Prediction Method Based on Learnable Three-Line Hybrid Feature Fusion

SSRN

引用

SSRN 2023年

作者： Tang, Yu Dai, Qi Du, Ye Chen, Lifang Niu, Xuanwen School of Computer and Information Technology Beijing Jiaotong University Beijing100044 China Department of Automation College of Information Science and Engineering China University of Petroleum-Beijing Beijing China Beijing Key Laboratory of Security and Privacy in Intelligent Transportation Beijing Jiaotong University Beijing100044 China College of Science North China University of Science and Technology Hebei Tangshan063210 China Hebei Key Laboratory of Data Science and Application Hebei Tangshan063210 China

software defect prediction plays a crucial role in ensuring the security and quality of software systems. However, it faces challenges posed by high-dimensional features present in software defect datasets and the limited effectiveness of traditional nonlinear dimensionality reduction methods in extracting essential feature information. To address these issues, we propose a novel approach called learnable three-line hybrid feature fusion (LTHFFA), which incorporates the principle of three-line hybrid breeding into feature fusion for the first time. In this method, three distinct dimensionality reduction techniques are initially employed to obtain three separate sets of features. Subsequently, a learnable weight factor feature fusion method is proposed to facilitate automatic learning and dynamic adjustment of feature weights. By integrating the three feature sets based on the principle of three-line hybrid breeding, we derive learnable three-line hybrid fusion features. These features are then utilized in the context of software defect prediction. Experimental results demonstrate the superior performance of LTHFFA compared to nine other dimensionality reduction methods across seventeen publicly available software defect datasets. LTHFFA exhibits the ability to effectively integrate multiple feature sets, reduce feature redundancy, and enhance predictive accuracy. Moreover, statistical analysis using Friedman ranking and Holm's post-hoc test confirms the significant advantage of LTHFFA over alternative dimensionality reduction methods. © 2023, The Authors. All rights reserved.

关键词： Defects

来源：评论

学校读者我要写书评

暂无评论

Direct Participant Recruitment Strategy in Sparse Mobile Crowdsensing

引用

Jisuanji Xuebao/Chinese Journal of computers 2022年第7期45卷 1539-1556页

作者： Tu, Chun-Yu Yu, Zhi-Yong Han, Lei Zhu, Wei-Ping Huang, Fang-Wan Guo, Wen-Zhong Wang, Le-Ye College of Mathematics and Computer Science Fuzhou University Fuzhou350108 China Department of Fujian Key Laboratory of Network Computing and Intelligent Information Processing Fuzhou University Fuzhou350108 China School of Computer Science Northwestern Polytechnical University Xi'an710072 China Key Lab of High Confidence Software Technologies Peking University Beijing100871 China School of Computer Science Peking University Beijing100871 China

Sparse Mobile Crowdsensing (Sparse MCS) selects a small part of sub-areas for data collection and infers the data of other sub-areas from the collected data. Compared with Mobile Crowdsensing (MCS) that does not use data inference methods, Sparse MCS saves sensing costs while ensuring the quality of global data. However, the existing research works on Sparse MCS only focus on selecting a small part of sub-areas with higher value. It does not consider whether the recruited participants can collect the data of the required sub-areas, and also ignores the value of other data collected by the participants. In order to solve the limitations of traditional methods in sub-areas selection, this paper starts from the perspective of participants and concentrates on the contribution of the data collected by each participant to the entire collection task. All the data contributions collected by each participant will become the basis for decision-making for the participant's choice. And correspondingly, a new idea to deal with the problem of participant selection under Sparse MCS is proposed. In view of the fact that each person's daily movement trajectory is basically stable, and the data collected by different people on their respective trajectories have different values, this paper uses this regularity and difference to study how to directly recruit participants who can collect high-value data. Furthermore, the participant selection problem considered in this paper is not limited to the data collection in the next cycle, but directly recruits some participants to continue the data collection task in the next multiple cycles. The participant selection problem that spans multiple cycles can be modeled as a dynamic decision-making problem. Since heuristic strategies may fall into a local optimal solution, this paper uses reinforcement learning to solve the participant selection problem: We use the participant selection system as an agent of reinforcement learning, and design the

关键词： Compressed sensing

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：