检索结果-内蒙古大学图书馆

Understanding the Bad Development Practices of Android Custom Permissions in the Wild

IEEE Transactions on Dependable and Secure Computing 2025年

作者： Zhang, Xiaohan Yu, Zhiyuan Li, Xinghua Zhang, Cen Sun, Cong Zhang, Ning Deng, Robert H. Xidian University State Key Laboratory of Integrated Service Networks School of Cyber Engineering Xi'an710071 China Ministry of Education Engineering Research Center of Big data Security China Washington University in St. Louis Department of Computer Science and Engineering St. LouisMO63130 United States Nanyang Technological University 639798 Singapore Singapore Management University 188065 Singapore

Android system provides application developers with the ability to define custom permissions, which serve to regulate the sharing of resources and functionalities with other applications. However, developers' improper development practices can render the permission mechanism ineffective, facilitating easy exploitation by attackers. This paper presents a comprehensive examination of the problematic practices surrounding custom permissions employed by developers, referred to as Bad Practices of Custom Permissions (BPCP issues). To accomplish this, we conducted an empirical study and identified nine common BPCP issue patterns that can lead to various adverse consequences, such as installation failures, crashes, and even component hijacking. To automatically identify these patterns of bad practices, we devised PERMEAGRE, a static analysis tool. Employing PERMEAGRE, we performed a large-scale analysis of 83,085 applications obtained from seven major app markets, aiming to detect instances of BPCP issues. The results revealed that more than 26% of the analyzed apps contained at least one issue, and a significant number of apps had garnered millions of downloads. Our analysis delved into the underlying causes of these issues. Consequently, this analysis sheds light on the potential threat landscape associated with bad practices in custom permissions, emphasizing the urgent requirement for effective mitigation strategies. © 2004-2012 IEEE.

关键词： Android custom permission empirical study static analysis

来源：评论

学校读者我要写书评

暂无评论

FedSTDN: A Federated Learning-Enabled Spatial-Temporal Prediction Model for Wireless Traffic Prediction

引用

IEEE Transactions on Mobile Computing 2025年

作者： Li, Zhenyu Fu, Yuchuan Tian, Mengqiu Li, Changle Yu, F. Richard Cheng, Nan Xidian University State Key Laboratory of Integrated Services Networks Shaanxi 710071 China Xidian University Research Institute of Smart Transportation Shaanxi Xi'an710071 China Carleton University Department of Systems and Computer Engineering OttawaONK1S 5B6 Canada

Wireless Traffic Prediction (WTP) plays a significant role in achieving intelligent resource management for communication systems. However, WTP still faces challenges such as inaccurate prediction resulting from the complex spatial-temporal characteristics due to user mobility, high communication overhead caused by the complexity of the prediction model, and user privacy issues stemming from Centralized Learning (CL). To address the aforementioned issues, this paper proposes a WTP framework under the Federated Learning (FL) strategy called Federated Spatial-Temporal Dual-attention based Network (FedSTDN). Aiming at improving communication efficiency and simultaneously representing various wireless traffic patterns, a data augmentation-based clustering algorithm is adopted, which groups cells into different regions using a small augmented dataset, facilitating subsequent processing. To improve prediction performance, a local prediction model based on Convolutional Neural Network (CNN) and Long Short-Term Memory (LSTM) is proposed to capture the short- and long-term dependencies of traffic. Additionally, a novel Kolmogorov-Arnold Network (KAN) layer is introduced to replace the traditional Multi-Layer Perceptron (MLP) layer, further enhancing prediction performance. Simulations on two different real-world datasets verify the effectiveness and efficiency of FedSTDN. Compared to the well-performing baseline, the proposed FedSTDN achieves up to 32.83% and 24.30% improvements in Mean Square Error (MSE) and Mean Absolute Error (MAE) on the Milan dataset, respectively. For the Trentino dataset, FedSTDN achieves up to 17.25% and 5.86% improvements in MSE and MAE, respectively. © 2002-2012 IEEE.

关键词： Prediction models

来源：评论

学校读者我要写书评

暂无评论

Channel Estimation for Stacked Intelligent Metasurfaces in Rician Fading Channels

arXiv

引用

arXiv 2025年

作者： Papazafeiropoulos, Anastasios Kourtessis, Pandelis Kaklamani, Dimitra I. Venieris, Iakovos S. Communications and Intelligent Systems Research Group University of Hertfordshire HatfieldAL10 9AB United Kingdom Microwave and Fiber Optics Laboratory Greece Intelligent Communications and Broadband Networks Laboratory School of Electrical and Computer Engineering National Technical University of Athens Zografou Athens15780 Greece

The recent combination of the rising architectures, known as stacked intelligent metasurface (SIM) and holographic multiple-input multiple-output (HMIMO), drives toward breakthroughs for next-generation wireless communication systems. Given the fact that the number of elements per surface of the SIM is much larger than the base station (BS) antennas, the acquisition of the channel state information (CSI) in SIM-aided multi-user systems is challenging, especially when a line-of-sight (LoS) component is present. Thus, in this letter, we address the channel procedure under conditions of Rician fading by proposing a protocol in terms of a minimum mean square error (MMSE) estimator for wave-based design in a single phase. Moreover, we derive the normalized mean square error (NMSE) of the suggested estimator, and provide the optimal phase shifts minimising the NMSE. Numerical results illustrate the performance of the new channel estimation protocol. © 2025, CC BY.

关键词： Mean square error

来源：评论

学校读者我要写书评

暂无评论

A Reliable Federated Learning Server Rotation Algorithm in IoV

引用

IEEE Internet of Things Journal 2025年

作者： Cai, Xuelian Yang, Junyi Chang, Tianyu Fu, Yuchuan Yu, F. Richard Cheng, Nan Li, Changle Hui, Yilong The State Key Laboratory of Integrated Services Networks Xidian University Shaanxi 710071 China Research Institute of Smart Transportation Xidian University Shaanxi Xi’an710071 China The Department of Systems and Computer Engineering Carleton University OttawaONK1S 5B6 Canada

Federated Learning (FL) enables the collaborative training of models by users distributed across various locations, transforming traditional data sharing into model sharing. This paradigm holds the promise of facilitating the development of safe, reliable, and accurate driving models within Internet of Vehicles (IoV), with its performance contingent upon the stability of the training process. However, traditional FL relies on a central server for aggregation, which is susceptible to malicious attacks. Moreover, limited communication resources prevent the inclusion of all users in the training process. To resolve issues related to reliability and resource utilization, this paper proposes a reliable Rotating Server Federated Learning (RSFL) algorithm to enhance the security and efficiency of FL. Specifically, we first consider the vehicular topology and participation in FL during their transition, and introduce a server rotation algorithm that incorporates a weighted sum of multiple factors including model training activity, vehicle credibility, speed stability, and distance to augment system security. Additionally, addressing the limitation of server channel resources that can impede FL efficiency, this paper proposes a method to select high-quality users for channel resource allocation by comprehensively considering participation latency, contribution, energy, and channel state during the FL process. This optimizes resource usage at the FL server side and constructs an efficiency-maximization problem for FL to improve the convergence rate. Simulation results confirm that the proposed RSFL algorithm can significantly enhance the security and system efficiency of FL. © 2014 IEEE.

关键词： Federated learning

来源：评论

学校读者我要写书评

暂无评论

Resource Allocation for Multi-Modal Semantic Communication in UAV Collaborative networks

引用

IEEE Transactions on Communications 2025年

作者： Hu, Han Zhu, Xingwu Zhou, Fuhui Wu, Wei Hu, Rose Qingyang Zhu, Hongbo Nanjing University of Posts and Telecommunications Jiangsu Key Laboratory of Wireless Communications Nanjing210003 China Nanjing University of Posts and Telecommunications Engineering Research Center of Health Service System Based on Ubiquitous Wireless Networks Ministry of Education Nanjing210003 China Southeast University School of Cyber Science and Engineering Nanjing211189 China Nanjing University of Aeronautics and Astronautics College of Artificial Intelligence Nanjing210000 China Xidian University State Key Laboratory of Integrated Services Networks Xian710071 China Nanjing University of Posts and Telecommunications College of Communication and Information Engineering China Utah State University Department of Electrical and Computer Engineering LoganUT210003 United States

Semantic communication is envisioned as a potential communication paradigm enabled by artificial intelligence and is promising to break the Shannon limit for future 6G networks. This paradigm benefits unmanned aerial vehicles (UAVs) to conserve communication resources and minimize latency by only transmitting task-relevant semantic information. However, resource allocation in the multiple collaborative UAV scenarios remains unexplored, particularly regarding multi-modal semantic communication. To tackle this challenge, this paper investigates a semantic-aware intelligent resource allocation method for multi-UAV-assisted semantic communication networks in the UAV image-sensing task-oriented scenario. A multi-modal semantic communication framework with multi-UAV relay collaboration is developed. At the semantic level, a novel quality of experience (QoE) and the transmission cost model are introduced, based on which a semantic-aware resource allocation problem is formulated, aiming to maximize QoE while minimizing the transmission cost by jointly optimizing the UAV trajectory, the spectrum bandwidth, the transmit power and the number of the transmitted semantic symbols. To deal with optimization challenges involving hybrid variables and coordination among UAVs, a multi-UAV hybrid decision-controlled deep reinforcement learning (DRL) scheme is proposed. Simulation results demonstrate the effectiveness of the proposed scheme compared with the benchmark schemes in achieving a good balance between the QoE and the transmission cost. © 1972-2012 IEEE.

关键词： Deep reinforcement learning

来源：评论

学校读者我要写书评

暂无评论

An Incentive Mechanism for Privacy Preserved Data Trading with Verifiable Data Disturbance

引用

IEEE Transactions on Dependable and Secure Computing 2025年

作者： Zhang, Man Li, Xinghua Luo, Bin Ren, Yanbing Miao, Yinbin Liu, Ximeng Deng, Robert H. State Key Laboratory of Integrated Services Networks the School of Cyber Engineering Xidian University Xi’an710071 China Engineering Research Center of Big data Security Ministry of Education Xi’an710071 China The College of Mathematics and Computer Science Fuzhou University Fujian350108 China The Department of Information Systems Singapore Management University 80 Stamford Road Singapore

To motivate data owners’ (DOs’) trading willingness, the existing incentive mechanisms allow DOs to independently disturb data following data consumer’s (DC’s) availability requirement. However, they cannot motivate DOs’ honest disturbance, which is attributed to DOs’ independent disturbance without any supervision. Thus, we implement an incentive mechanism for privacy preserved data trading with verifiable data disturbance where an honest-but-curious disturbance generator (DG) is additionally introduced to supervise DOs’ local disturbance and assist disturbance verification between DOs and DC. Specifically, DG generates the disturbance strategies and secretly distributes to DOs following private information retrieval, guaranteeing DOs’s local disturbance’s privacy and verifiability with our proposed three-level verification algorithm. Subsequently, we model the trading as a game and disturbance verification results determine the compensation and punishment for trading bilateral utilities following Nash Equilibrium where DOs honestly disturb data. Theoretical analysis shows that DOs are motivated to honestly disturb data and their raw data privacy is preserved. Extensive experiments using the real-world dataset demonstrate that the deviating DOs in our scheme can be verified with a probability of more than 90% and the statistical result accuracy can be improved by more than 80% compared with the existing works. © 2004-2012 IEEE. All rights reserved.

关键词： Differential privacy

来源：评论

学校读者我要写书评

暂无评论

WiFi-Diffusion: Achieving Fine-Grained WiFi Radio Map Estimation with Ultra-Low Sampling Rate by Diffusion Models

arXiv

引用

arXiv 2025年

作者： Liu, Zhiyuan Zhang, Shuhang Liu, Qingyu Zhang, Hongliang Song, Lingyang School of Electronic and Computer Engineering Peking University Shenzhen Graduate School Shenzhen518055 China Pengcheng Laboratory Shenzhen518055 China Department of Electronics Peking University Beijing100871 China State Key Laboratory of Advanced Optical Communication Systems and Networks School of Electronics Peking University Beijing100871 China

Fine-grained radio map presents communication parameters of interest, e.g., received signal strength, at every point across a large geographical region. It can be leveraged to improve the efficiency of spectrum utilization for a large area, particularly critical for the unlicensed WiFi spectrum. The problem of fine-grained radio map estimation is to utilize radio samples collected by sparsely distributed sensors to infer the map. This problem is challenging due to the ultra-low sampling rate, where the number of available samples is far less than the fine-grained resolution required for radio map estimation. We propose WiFi-Diffusion – a novel generative framework for achieving fine-grained WiFi radio map estimation using diffusion models. WiFi-Diffusion employs the creative power of generative AI to address the ultra-low sampling rate challenge and consists of three blocks: 1) a boost block, using prior information such as the layout of obstacles to optimize the diffusion model;2) a generation block, leveraging the diffusion model to generate a candidate set of radio maps;and 3) an election block, utilizing the radio propagation model as a guide to find the best radio map from the candidate set. Extensive simulations demonstrate that 1) the fine-grained radio map generated by WiFi-Diffusion is ten times better than those produced by state-of-the-art (SOTA) when they use the same ultra-low sampling rate;and 2) WiFi-Diffusion achieves comparable fine-grained radio map quality with only one-fifth of the sampling rate required by SOTA. Copyright © 2025, The Authors. All rights reserved.

关键词： Wi-Fi

来源：评论

学校读者我要写书评

暂无评论

A Practical Distortion Simulator for Screen-Shooting Resilient Image Watermarking in Consumer Electronic Applications

引用

IEEE Transactions on Consumer Electronics 2025年

作者： Zhang, Yulin Ni, Jiangqun Malwi, Wajdan Al Asiri, Fatima Ahmad, Jawad Jiang, Donghua Sun Yat-sen University School of Computer Science and Engineering Guangzhou510006 China Sun Yat-sen University School of Cyber Science and Technology Shenzhen518107 China Peng Cheng Laboratory Department of New Networks Shenzhen518055 China King Khalid University College of Computer Science Department of Informatics and Computer Systems Abha61421 Saudi Arabia Prince Mohammad Bin Fahd University Cybersecurity Center Alkhobar Saudi Arabia

Nowadays screen-shooting resilient watermarking still remains as a predictive and challenging area of research for proactive data protection in consumer electronic applications. Present deep learning-based methodologies embrace end-to-end frameworks and devise specialized noise layer to simulate the distortions introduced in cross-media transmission between consumer electronics. Typically, the noise layer is designed as a deterministic image-to-image network to simulate the transformation from the clean images to the screen-captured ones, overlooking the randomness of real-world distortions, which makes it challenging to satisfy the demands of various consumer electronics and application scenarios. To address this issue, we conceptualize the screen-shooting channel as an image degradation model and accordingly develop the Resolver-Simulator framework (ReSim). The involved screen-shooting simulator is designed as a conditional image-to-image network to learn the exact degradation function. By taking advantage of the pre-trained parameter resolver, the noise components can be disentangled to compose the instance sets. Then the set sampling strategy is adopted to obtain the noise instances for realistic screen-shooting simulation for unseen images. Experimental results demonstrate that the proposed scheme outperforms the previous arts in terms of real-world robustness as well as exhibits high computational efficiency to enable real-time security solutions in consumer devices. © 1975-2011 IEEE.

关键词： Watermarking

来源：评论

学校读者我要写书评

暂无评论

Multi-routine-data driven spatio-temporal short-term predictions for surface ozone in China

引用

Air Quality, Atmosphere & Health 2025年 1-10页

作者： Zheng, Canju Shen, Hengqing Sun, Jianan Liu, Guangliang Cao, Haowei Zhang, Jie Gong, Xiang Xu, Da Key Laboratory of Computing Power Network and Information Security Ministry of Education Shandong Computer Science Center 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 Environment Research Institute Shandong University Qingdao China Ningbo Yonghuanyuan Environmental Engineering and Technology Co. LTD Ningbo China School of Mathematics and Physics Qingdao University of Science and Technology Qingdao China Qingdao Innovation Center of Artificial Intelligence Ocean Technology Qingdao China

Ozone (O3) is a major atmospheric pollutant, and accurate prediction of its concentrations remains challenging due to its complex nonlinear relationships with precursor compounds. Existing machine learning methods have mainly focused on single-site or spatial predictions, lacking research on spatio-temporal short-term predictions based on simple factors. To address this gap, the MRD-O3former, a deep learning model driven by multi-routine data, was developed to predict short-term hourly spatio–temporal surface ozone concentrations over China. The model exhibits strong spatio–temporal consistency, achieving a high correlation coefficient ( $${r}^{2} = 0.85\sim 0.90$$ ) and low normalized mean biases (NMBs) between -15% and 15% at the national scale compared to reanalysis ozone data. Both ablation experiments and permutation importance analysis reveal that historical ozone levels play a primary role in next-day ozone prediction, while meteorological factors such as wind speed and temperature also make significant contributions. Regional validation confirms the model’s effectiveness in the Beijing-Tianjin-Hebei region. Moreover, the study investigates the differential impact of crucial factors in urban and rural areas, revealing that historical ozone levels and meteorological factors significantly influence rural areas. However, the influence of historical ozone levels on urban ozone prediction is relatively small, especially during the summer. This suggests that urban ozone undergoes rapid formation and removal processes. These findings highlight the promising potential of deep learning techniques in accurately predicting spatiotemporal short-term ozone concentrations and interpreting the mechanism and source for ozone pollution.

关键词：

来源：评论

学校读者我要写书评

暂无评论

Enabling Real-time Computing and Transmission Services in Large-Scale LEO Satellite networks

引用

IEEE Transactions on Vehicular Technology 2025年

作者： Guo, Binquan Zhang, Zhou Atapattu, Saman Pan, Miao Yan, Ye Xiong, Zehui Li, Hongyan Xidian University State Key Laboratory of Integrated Services Networks Xi'an710071 China Tianjin300457 China Singapore University of Technology and Design Pillar of Information Systems Technology and Design 487372 Singapore Hunan University College of Computer Science and Electronic Engineering Changsha410012 China University School of Engineering MelbourneVIC3000 Australia University of Houston Department of Electrical and Computer Engineering HoustonTX77204 United States Tianjin Artificial Intelligence Innovation Center Tianjin300387 China

Enhanced by onboard computing resources and inter-satellite links (ISLs), large-scale satellite networks (SNs) can supplement terrestrial networks, facilitating real-time edge computing and transmission in remote areas at low latency. This capability benefits applications such as real-time remote object tracking, vehicular edge computing, and environmental monitoring. However, different from terrestrial vehicular networks, satellite mobility and the intermittent nature of ISLs often render many computing satellites inaccessible, necessitating efficient scheduling scheme for joint computing and communication resource allocation. To meet this requirement, we investigate joint computing and transmission strategy for real-time computing applications over SNs to reduce end-to-end delays. We formulate the real-time computing and transmission problem over time-varying SNs as an integer linear programming problem. The mathematical optimization method has an exponential time complexity of O(2|L⫟| . (4 |V⫟| + 4∣ L⫟|)2|L∣) in the worst case, which grows rapidly with the growth of network sizes. Here, |V⫟| and |L⫟| denote the number of nodes and links, respectively, within the time window {\tau }. Resorting to the designed k-shortest path-based method yields a worst-case complexity of O(|V⫟|! . |V⫟|3). While efficient under common scenarios, its running time is still relatively unstable and may become tens of times longer when computing resources become scarce. To enhance efficiency, we further design a graph-based algorithm that leverages the solution space's unique structure, thereby achieving optimal solutions with polynomial time complexity O(3|L⫟| + (2\log |V⫟|+1) |V⫟|). Extensive evaluations conducted on the world's largest Starlink system validate the efficacy of the designed methods and highlight their potential synergy. © 1967-2012 IEEE.

关键词： Satellite links

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：