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23rd IEEE International Conference on Trust, security and Privacy in Computing and communications, TrustCom 2024

作者： Zhang, Xin Guo, Chao Guo, Buxin Beijing Electronics Science and Technology Institute Department of Electronics and Communication Engineering Beijing China Chinese Academy of Sciences Beijing Electronics Science and Technology Institute The Institute of Information Engineering Department of Electronics and Communication Engineering Beijing China Beijing Electronics Science and Technology Institute Department of Cyberspace Security Beijing China

In vehicular networks, securing the network relies on analyzing extensive vehicle perception data to create an accurate road information model. Effective data sharing is crucial for this objective. However, traditional centralized data sharing methods jeopardize the privacy and security of data providers. Federated Learning has emerged as a solution to this challenge. Unfortunately, conventional Federated Learning strategies often overlook the differences in resource investments and data quality among participating vehicles, leading to unfair model distribution and diminishing the willingness of vehicles to share data. This paper proposes a two-stage Federated Learning strategy. In the first phase, aggregation nodes employ a fuzzy comprehensive evaluation method to assess the training results of participating vehicles and allocate aggregation weights accordingly. In the second phase, aggregation nodes calculate vehicle reputations using a periodic averaging method and distribute a differentially compressed global model based on these reputation values. Experimental results demonstrate that the proposed strategy achieves performance comparable to existing Federated Learning algorithms while effectively calculating vehicle reputations, ensuring fair distribution, and mitigating malicious clients. © 2024 IEEE.

关键词： Learning algorithms

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Multi-scale network for single image deblurring based on ensemble learning module

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Multimedia Tools and Applications 2025年第11期84卷 9045-9064页

作者： Wu, WenBo Pan, Yun Su, Na Wang, JingTao Wu, ShaoChuan Xu, ZeKun Yu, YouJian Liu, YaPeng State Key Laboratory of Media Convergence and Communication Communication University of China Beijing100024 China School of Computer and Cyberspace Security Communication University of China Beijing100024 China School of Electronics and Information Engineering Harbin Institute of Technology Harbin150080 China College of Computer and Control Engineering Northeast Forestry University Harbin150040 China School of Computer and Information Engineering Tianjin Chengjian University Tianjin300384 China School of Computer Science and Technology Anhui University Hefei230601 China

In this paper, we have identified two primary issues with current multi-scale image deblurring methods. On the one hand, the blurring scale is ignored. On the other hand, the context information of images is not fully utilized, and the spatial detail information of images is lost. To better solve the above problems, on top of that, this paper proposes a multi-scale network for single image deblurring. The designed network contains two parts: 1) Ensemble Learning Model: The Weak Learning Model in this module connects multiple weak learners in series to increase network depth. Moreover, through multi-scale feature ensemble module, the output features and intermediate features of different weak learner are integrated, and finally strong learner is used to enhance the features with semantic information at different scales;2) Spatial Feature Enhancement Module: This module can preserve spatial detail information of images and compensate for the disadvantages of losing image spatial details due to continuous downsampling in the Ensemble Learning Model structure. Our proposed deblurring model has been extensively evaluated on several benchmark datasets, and the model achieves superior performance compared to the most advanced deblurring methods. In particular, the peak signal-to-noise ratio (PSNR) and structural similarity index measure (SSIM) values are 33.57 dB and 0.965 on the GoPro dataset, respectively, with PSNR and SSIM improving by 0.49 dB and 0.002 over NAFNet, and 0.65 dB and 0.004 over Restormer. © The Author(s), under exclusive licence to Springer science+Business Media, LLC, part of Springer Nature 2024.

关键词： Learning systems

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Arbitrarily rotating polarization direction and manipulating phases in linear and nonlinear ways using programmable metasurface

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Light(science & Applications) 2024年第9期13卷 1789-1800页

作者： Wei Liu Si Ran Wang Jun Yan Dai Lei Zhang Qiao Chen Qiang Cheng Tie Jun Cui State Key Laboratory of Millimeter Waves Southeast UniversityNanjing 210096China Institute of Electromagnetic Space Southeast UniversityNanjing 210096China Frontiers Science Center for Mobile Information Communication and Security Southeast UniversityNanjing 210096China Electrical Engineering Department Chalmers University of TechnologyGothenburg 41258Sweden

Independent controls of various properties of electromagnetic(EM)waves are crucially required in a wide range of *** metasurface is a promising candidate to provide an advanced platform for manipulating EM ***,we propose an approach that can arbitrarily control the polarization direction and phases of reflected waves in linear and nonlinear ways using a stacked programmable ***,we extend the space-timecoding theory to incorporate the dimension of polarization,which provides an extra degree of freedom for manipulating EM *** proof-of-principle application examples,we consider polarization rotation,phase manipulation,and beam steering at linear and nonlinear *** validation,we design,fabricate,and measure a metasurface *** experimental results show good agreement with theoretical predictions and *** proposed approach has a wide range of applications in various areas,such as imaging,data storage,and wireless communication.

关键词： surface nonlinear rotating

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Influence of Access Points’ Height and High Signal Relation in WLAN Fingerprinting-Based Indoor Positioning Systems’ Accuracy 1

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26th International Conference on Distributed Computer and communication Networks: Control, Computation, communications, DCCN 2023

作者： Nicholaus, Mrindoko R. Ruambo, Francis A. Masanga, Elijah E. Muthanna, Mohammed Saleh Ali Lashchev, Andrei Computer Science and Engineering Department College of Information and Communication Technology Mbeya University of Science and Technology 131 Mbeya Tanzania United Republic of Information Systems and Technology Department College of Information and Communication Technology Mbeya University of Science and Technology 131 Mbeya Tanzania United Republic of School of Cyber Science and Engineering Huazhong University of Science and Technology Wuhan430074 China Research and Development Department Aifrruis Laboratories Mbeya Tanzania United Republic of Institute of Computer Technologies and Information Security Southern Federal University Taganrog347922 Russia

ISBN: (数字)9783031504822

ISBN: (纸本)9783031504815

Wireless Local Area Network (WLAN) Fingerprinting-based Indoor Positioning Systems (IPS) offer several key advantages over other indoor positioning technologies, including cost-effectiveness with high accuracy and passive tracking of entities’ location without Global Positioning System (GPS) dependence. However, achieving a satisfactory accuracy performance of the WLAN fingerprinting-based IPS is still challenging, as it is affected by several factors, such as obstacles (e.g., walls and furniture) that can block or weaken wireless signals. Additionally, the IPS’s accuracy can be affected by environmental changes, such as the height, addition, or removal of a WLAN Access Point (AP) or changes in radio frequency interference. This paper presents a novel algorithm incorporating a probabilistic analytical model employing only high signal relations that mitigate the effect of low signal relation on the WLAN fingerprinting-based IPS, thus improving the accuracy performance. Furthermore, it analyses the impact of AP heights on the accuracy performance of the WLAN IPS. Upon extensive experiments, the proposed algorithm improves the accuracy of the IPS by an average of 32.74% in terms of Root Mean Square Error (RMSE). These results imply that incorporating APs’ height and high signal relations can significantly improve the accuracy performance of the WLAN fingerprinting-based IPS. © The Author(s), under exclusive license to Springer Nature Switzerland AG 2024.

关键词： Cost effectiveness

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K-Means Clustering with Local Distance Privacy

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Big Data Mining and Analytics 2023年第4期6卷 433-442页

作者： Mengmeng Yang Longxia Huang Chenghua Tang the Data61 Commonwealth Scientific and Industrial Research OrganizationMelbourne 3168Australia the School of Computer Science and Communication Engineering Jiangsu UniversityZhenjiang 212013China. the School of Computer Science and Information Security Guilin University of Electronic TechnologyGuilin 541010China

With the development of information technology,a mass of data are generated every *** and analysing these data help service providers improve their services and gain an advantage in the fierce market competition.K-means clustering has been widely used for cluster analysis in real ***,these analyses are based on users’data,which disclose users’*** differential privacy has attracted lots of attention recently due to its strong privacy guarantee and has been applied for clustering ***,existing K-means clustering methods with local differential privacy protection cannot get an ideal clustering result due to the large amount of noise introduced to the whole dataset to ensure the privacy *** solve this problem,we propose a novel method that provides local distance privacy for users who participate in the clustering *** of making the users’records in-distinguish from each other in high-dimensional space,we map the user’s record into a one-dimensional distance space and make the records in such a distance space not be distinguished from each *** be specific,we generate a noisy distance first and then synthesize the high-dimensional data *** propose a Bounded Laplace Method(BLM)and a Cluster Indistinguishable Method(CIM)to sample such a noisy distance,which satisfies the local differential privacy guarantee and local dE-privacy guarantee,***,we introduce a way to generate synthetic data records in high-dimensional *** experimental evaluation results show that our methods outperform the traditional methods significantly.

关键词： K-means clustering local differential privacy data analysis

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A Novel Orthogonal LoRa Multiple Access Algorithm for Satellite Internet of Things

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China communications 2022年第3期19卷 279-289页

作者： Chengwen Zhang Liankai Wang Libin Jiao Shipeng Wang Jun Shi Jia Yue School of Electronics and Information Engineering Harbin Institute of TechnologyHarbin 150080China Science and Technology on Communication Networks Laboratory Shijiazhuang 050081China Key Laboratory of Police Wireless Digital Communication Ministry of Public SecurityHarbin 150001China

In recent years,LoRa has been extensively researched in the satellite Internet of Things(IoT).However,the multiple access technology of LoRa is still one of the bottlenecks of satellite *** improve the multiple access performance of LoRa satellite IoT,based on the orthogonality of LoRa symbols in the fractional domain,this paper proposes a low complexity Orthogonal LoRa Multiple Access(OLMA)algorithm for multiple LoRa users occupying the same frequency *** algorithm introduces the address code to divide the fractional bandwidth into multiple parts,and the OLMA users with different address codes occupy different parts to transmit the information code,thus avoiding mutual interference caused by collisions in the same frequency *** multiple access capability of OLMA can be flexibly configured only by simply adjusting the length of the address code according to the actual application requirements of data *** analysis and simulation results show that the OLMA algorithm can greatly improve the multiple access capability and the total transmission bit rate of LoRa IoT without changing the existing LoRa modulation parameters and process.

关键词： LoRa satellite Internet of Things multiple access fractional domain linear frequency modulation

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Artificial Intelligence-Aid OTFS Receiver: Design and Performance Evaluation

Artificial Intelligence-Aid OTFS Receiver: Design and Perfor...

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2023 International Conference on Ubiquitous communication, Ucom 2023

作者： Wang, Chenxi Qian, Li Ping Zhu, Huijie College of Information Engineering Zhejiang University of Technology Hangzhou China The 36th Research Institute of China Electronics Technology Science and Technology on Communication Information Security Control Laboratory Jiaxing China

ISBN: (纸本)9798350340433

OTFS technology is attracting much attention due to its strong adaptability to high-speed mobility and high spectrum efficiency. Studies show that applying deep learning (DL) to traditional communication systems significantly improves their performance. To this end, this paper proposes an AI-Aid OTFS receiver to enhance the performance of OTFS systems in high-speed mobile communication scenarios. The proposed receiver employs two networks-a channel estimation network called TFNet and a decoding network called RecovNet. In the channel estimation module, TFNet performs more accurate channel estimation based on the results of traditional channel estimation methods. In the decoding module, RecovNet decodes the signal based on the equalized signals. To validate the superiority of our proposed receiver, we conduct comparative testing with a traditional receiver under the integer Doppler channel. The experimental results demonstrate that integrating DL technology in OTFS systems effectively enhances their performance. © 2023 IEEE.

关键词： Decoding

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Analyzing Human Observer Ability in Morphing Attack Detection - Where Do We Stand?

IEEE Transactions on Technology and Society

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IEEE Transactions on technology and Society 2023年第2期4卷 125-145页

作者： Godage, Sankini Rancha Lovasdal, Froy Venkatesh, Sushma Raja, Kiran Ramachandra, Raghavendra Busch, Christoph Norwegian University of Science and Technology Department of Computer Science Trondheim 22 Gjovik2815 Norway National Police Directorate National Technology Team Euis Program Oslo0369 Norway Norwegian University of Science and Technology Department of Information Security and Communication Technology Trondheim 22 Gjovik2815 Norway

Morphing attacks are based on the technique of digitally fusing two (or more) face images into one, with the final visage resembling the contributing faces. Morphed images not only pose a challenge to Face-Recognition Systems (FRS) but also challenge experienced human observers due to high quality, postprocessing eliminating any visible artifacts, and further the printing and scanning process. Few studies have concentrated on examining how human observers can recognize morphing attacks, even as several publications have examined the susceptibility of automated FRS to morphing attacks and offered morphing attack detection (MAD) approaches. MAD approaches base their decisions either on a single image with no reference to compare against (Single-Image MAD (S-MAD)) or using a reference image (Differential MAD (D-MAD)). One prevalent misconception is that an examiner's or observer's capacity for facial morph detection depends on their subject expertise, experience, and familiarity with the issue. No works have reported the specific results of observers who regularly verify identity (ID) documents for their jobs. As human observers are involved in checking ID documents having facial images, a lapse in their competence can result in significant societal challenges. To assess the observers' proficiency, this research first builds a new benchmark database of realistic morphing attacks from 48 different subjects, resulting in 400 morphed images. Unlike the previous works, we also capture images from Automated Border Control (ABC) gates to mimic realistic border-crossing scenarios in the D-MAD setting with 400 probe images, to study the ability of human observers to detect morphed images. A new dataset of 180 morphing images is also produced to research human capacity in the S-MAD environment. In addition to creating a new evaluation platform to conduct S-MAD and D-MAD analysis, the study employs 469 observers for D-MAD and 410 observers for S-MAD who are primarily governmenta

关键词： Face recognition

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Securing tomorrow: a comprehensive survey on the synergy of Artificial Intelligence and information security

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AI and Ethics 2024年第3期5卷 1911-1929页

作者： Hashmi, Ehtesham Yamin, Muhammad Mudassar Yayilgan, Sule Yildirim Department of Information Security and Communication Technology (IIK) Norwegian University of Science and Technology (NTNU) Gjøvik Norway

This survey paper explores the transformative role of Artificial Intelligence (AI) in information security. Traditional methods, especially rule-based approaches, faced significant challenges in protecting sensitive data from ever-changing cyber threats, particularly with the rapid increase in data volume. This study thoroughly evaluates AI’s application in information security, discussing its strengths and weaknesses. It provides a detailed review of AI’s impact on information security, examining various AI algorithms used in this field, such as supervised, unsupervised, and reinforcement learning, and highlighting their respective strengths and limitations. The study identifies key areas for future AI research in information security, focusing on improving algorithms, strengthening information security, addressing ethical issues, and exploring safety and security-related concerns. It emphasizes significant security risks, including vulnerability to adversarial attacks, and aims to enhance the robustness and reliability of AI systems in protecting sensitive information by proposing solutions for potential threats. The findings aim to benefit cybersecurity professionals and researchers by offering insights into the intricate relationship between AI, information security, and emerging technologies.

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An Efficient and Lightweight Commitment Scheme for IoT Data Streams 19

An Efficient and Lightweight Commitment Scheme for IoT Data ...

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19th Annual International Conference on Distributed Computing in Smart Systems and the Internet of Things, DCOSS-IoT 2023

作者： Katsika, Angeliki Papageorgiou, Konstantinos Fakis, Alexandros Plagianakos, Vassilis Spathoulas, Georgios University of Thessaly Department of Computer Science and Biomedical Informatics Lamia Greece University of the Aegean Department of Information and Communication Systems Engineering Samos Greece Department of Information Security and Communication Technology Gjøvik Norway

ISBN: (纸本)9798350346497

Data integrity is a crucial property for offering Internet of Things (IoT) services. Usually, this is achieved by assuming a high level of trust in the service provider, a practice that induces negative implications. In the present paper, an efficient and lightweight commitment scheme for IoT data streams is presented. The proposed scheme combines KZG polynomial commitments and a blockchain smart contract to enable full data integrity while requiring trust only at the IoT device level. Given that the IoT device operates properly, no tampering can happen at any other point of the IoT workflow, without being noticed, while the protocol offers the option for automated settlement of cases where the operator tampers with users' data. A number of experiments have been conducted to assess the computation/gas fees requirements of the scheme and validate its real-world applicability. © 2023 IEEE.

关键词： Internet of things

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