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Ubiquitous Communication (Ucom), International Conference on

作者： Hongbin Fan Weiwei Yang Wenqiang Feng Chuanzhen Rong College of Communications Engineering PLA Army Engineering University Nanjing China College of Computer and Artificial Intelligence Xiangnan University Chenzhou China Guangxi Key Laboratory of Trusted Software Guilin University of Electronic Technology Guilin China

ISBN: (数字)9798350374216

ISBN: (纸本)9798350374223

Cognitive radio can meet the increasing demand for wireless spectrum in 5G and B5G, and relay can relay-assisted covert communications in underlay cognitive radio networks (UCRN) to reaching distant destinations. Therefore, we investigated relay-assisted covert communication in UCRN. In phase 1, the secondary source (SS) sends covert messages to the relay, and the relay sends interference signals to Willie. In phase 2, the relay decodes and forwards the covert information received in phase 1, and SS sends interference signals to Willie to ensure covert transmission. The average detection error probability, the transmission outage probability and the maximum effective covert rate of the system are derived. The simulation results show that the average minimum detection error probability in phase 1 and the transmission outage probability decrease with the increase of the maximum interference tolerance of primary destination (PD), and the maximum effective covert rate increases with the increase of the maximum interference tolerance of PD.

关键词： Error probability 5G mobile communication Simulation Collaboration Interference Power system reliability Performance analysis Cognitive radio Relays

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Robustness Assessment of Hyperspectral Image Cnns Using Metamorphic Testing

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SSRN 2022年

作者： Bouchoucha, Rached Ben Braiek, Houssem Khomh, Foutse Bouzidi, Sonia Zaatour, Rania Department of Computer Engineering and Software Engineering Polytechnique MontrealQCH3T 1J4 Canada Department of Computer Engineering and Software Engineering National Institute of Applied Science and Technology Tunis1080 Tunisia

Remote sensing has proven its utility in many critical domains, such as medicine, military, and ecology. Recently, we have been witnessing a surge in the adoption of deep learning (DL) techniques by the remote sensing community. DL-based classifiers, such as convolutional neural networks (CNNs), have been reported to achieve impressive predictive performances reaching 99\% of accuracy when applied to hyperspectral images (HSIs), a high-dimensional type of remote sensing data. However, these deep learners are known to be highly sensitive to even slight perturbations of their high-dimensional raw inputs. In real-world contexts, concerns can be raised about how robust they really are against corner-case scenarios. When HSI classifiers are applied in safety-critical applications, ensuring an adequate level of robustness is crucial to prevent unexpected system behaviors. Yet, there are few studies dealing with their robustness, nor are RGB-testing methods able to cover the HSI-specific challenges. This led us to propose a systematic testing method to assess the robustness of the CNNs trained to classify HSIs. First, we elaborate domain-specific metamorphic transformations that simulate naturally-occurring distortions of remote sensing HSIs. Then, we leverage metaheuristic search algorithms to optimize the fitness of synthetically-distorted inputs to stress the weaknesses of the on-testing CNN, while remaining in compliance with domain expert requirements, in order to preserve the semantic of the generated inputs. Relying on our metamorphic testing method, we assess the robustness of established and novel CNNs for HSI classification, and demonstrate their failure, on average, in 25\% of the produced test cases. Furthermore, we fine-tuned the tested CNNs on training data augmented with these failure-revealing metamorphic transformations. Results show that the fined-tuning successfully fixed at least 90\% of the CNN weaknesses, with less than 1\% of degradation in the origi

关键词： Remote sensing

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Dfgnn: An Interpretable and Generalized Graph Neural Network for Deepfakes Detection

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SSRN 2022年

作者： Khalid, Fatima Javed, Ali Ul-Ain, Qurat Ilyas, Hafsa Irtaza, Aun Department of Computer Science University of Engineering and Technology Punjab Taxila47050 Pakistan Department of Software Engineering University of Engineering and Technology Punjab Taxila47050 Pakistan

Deepfakes are generated using sophisticated deep-learning models to create fake images or videos. As the techniques for creating deep fakes improve, issues like defamation, impersonation, fraud, and misinformation on social media are becoming more prevalent. Existing deep learning-based deepfakes detection models are not interpretable and don’t generalize well when tested across diverse deepfakes generating techniques and datasets. Therefore, the creation of reliable and effective deepfakes detection algorithms is required which are not only generalizable but also interpretable. This paper introduces a novel graph neural network-based architecture to identify hyperrealist deepfake content. As far as we know, no other contemporary technique using graph neural networks for deepfake identification exists. Our proposed model is based on the pyramid ResNet structure and takes advantage of multi-scale images property by extracting features with progressively smaller spatial sizes as layer depth increases. We sliced the image into patches, which are referred to as nodes, and then constructed a graph by connecting the nearest neighbors. To transform and exchange information between all nodes, our model has two basic modules: GraphNet, which uses graph convolution layers to aggregate and update graph information, and FFN, which has linear layers for the transformation of node features. The effectiveness of our method is assessed using the diverse Deepfake Detection Challenge dataset, FaceForensics++ (FF++), World Leaders dataset (WLRD), and the Celeb-DF. To demonstrate the generalizability of our method for accurate deepfakes detection, we also performed open/close set, crossset, and cross-corpora evaluations. The AUC values of 0.98 on FF++, 0.95 on Celeb-DF, 0.92 on DFDC, and 1.00 on most of the sets of WLRD datasets demonstrate the efficacy of our method for identifying manipulated facial images produced using various deepfakes techniques. © 2022, The Authors. All rights r

关键词： Deep learning

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WoKnack-A Professional Social Media Platform for Women Using Machine Learning Approach 3

WoKnack-A Professional Social Media Platform for Women Using...

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3rd International Conference on Advancements in Computing, ICAC 2021

作者： Shanmugarajah, Sutharshan Praisoody, Ashvini Uddin, Md Rakib Paskkaran, Vinojan De Silva, Hansi Tharmaseelan, Janani Thilakarathna, Thusithanjana Sri Lanka Institute of Information Technology Department of Computer Science and Software Engineering Malabe Sri Lanka Sri Lanka Institute of Information Technology Department of Information Systems Engineering Malabe Sri Lanka

ISBN: (纸本)9781665408622

Today's generation is heavily influenced by social media. However, most users decline to post their abilities on these platforms for a variety of reasons, including security, a lack of basic skills, and a lack of knowledge about the various skill sets. It's understandable that women face many security risks on these platforms. WoKnack is a professional social networking platform dedicated to women. This opens opportunities for women to demonstrate their abilities and teach other women. This paper targets onfunctionalities like registration limited to female users, skill categorization, post verification and privacy preservation. Facial image, identification document and Voice related gender verification done using machine learning approaches to identify thegender before registration. Accuracy of 91% gained during the process. Skills have been categorized using Natural language processing and post verification done based on these categories. Usage of the best accurate algorithm gives an accuracy of 94% during this process. In order to preserve the privacy of users Data anonymization, skill and location clustering have been added to the system. © 2021 IEEE.

关键词： Machine learning

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Design of Vehicle-cloud Collaborative Framework for 5G Cross-border Multi-PLAN Scenarios

Design of Vehicle-cloud Collaborative Framework for 5G Cross...

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International Conference on Network and Information Systems for computers (ICNISC)

作者： Guotao Du Jinlong Chu Jing Ma Shiduo Ning School of Mechanical Engineering Dalian University of Technology DUT Dalian China Department of Computer Science & Software Engineering School of Engineering Computer & Mathematical Sciences Auckland University of Technology Auckland New Zealand

ISBN: (纸本)9781665453523

The 5G-MOBIX project applies 5G network communication technology and defines five user stories and specific user scenarios in order to solve the cross-border problem of 5G driverless vehicles. Combined with the remote control user scenario in the remote driving user case, this paper explains the interconnect delay problem between PLMNS in this scenario. From the perspective of infrastructure, that is, the cooperation between vehicle infrastructure and cloud infrastructure, the CCAM architecture of remote control user story simulation test scenario is designed. The proposed solution of direct interconnection is verified and tested, and the cross-border test is simulated at the test site in China. The final test results show that the solution is feasible.

关键词： computers 5G mobile communication Collaboration computer architecture Communications technology Delays Remote control

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Kco: Balancing Class Distribution in Just-in-Time software Defect Prediction Using Kernel Crossover Oversampling

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SSRN 2023年

作者： Ismail, Ahmad Muhaimin Hamid, Siti Hafizah Ab Sani, Asmiza Abdul Daud, Nur Nasuha Mohd Department of Software Engineering Faculty of Computer Science and Information Technology University of Malaya Kuala Lumpur50603 Malaysia Faculty of Ocean Engineering Technology and Informatics University Malaysia Terengganu Kuala Terengganu Terengganu21030 Malaysia

ContextThe performance of the defect prediction model by using balanced and imbalanced datasets makes a big impact on the discovery of future defects. Current resampling techniques only address the imbalanced datasets without taking into consideration redundancy and noise inherent to the imbalanced *** address the imbalance issue, we propose Kernel Crossover Oversampling (KCO), an oversampling technique based on kernel analysis and cross-over interpolation. Specifically, the proposed technique aims to generate balanced datasets by increasing data diversity in order to reduce redundancy and *** first represents multidimensional features into two-dimensional features by employing Kernel Principal Component Analysis (KPCA). KCO then divides the plotted data distribution by deploying spectral clustering to select the best region for interpolation. Lastly, KCO generates the new defect data by cross-interpolating different data templates within the selected data *** to the prediction evaluation conducted, KCO consistently produced F-scores ranging from 21% to 63% across six datasets, on average. According to the experimental results presented in this study, KCO provides more effective prediction performance than other baseline *** experimental results show that KCO within project and cross project predictions especially consistently achieve higher performance of F-score results. © 2023, The Authors. All rights reserved.

关键词： Interpolation

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A Novel Hybrid Scheme for Time Series Prediction Using LMS Filter and ISSA-based LSTM

A Novel Hybrid Scheme for Time Series Prediction Using LMS F...

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Symposia and Workshops on Ubiquitous, Autonomic and Trusted Computing, UIC-ATC

作者： Pei Heng Li Dong Xia Chen Han Yao Huang Xiao Tong Wu Hua Wei Jiang Gen Lin Ji School of Computer Science and Technology Nanjing Normal University Nanjing China Department of Computer and Software Engineering Nanyang Institute of Technology Nanyang China Department of Computer Science Engineering Sungkyunkwan Univeristy Suwon Korea

In the era of Internet of Everything (IoE), there is an explosive growth in data volumes and the data usually with time series characteristics. Therefore, how to deal with time series data to improve prediction accuracy remains a critical issue. In this paper, an efficient hybrid scheme combining linear and nonlinear models is proposed to greatly improve the prediction performance and simplify the hyperparameter selection process. Gradient-based least mean square (GD-LMS) is introduced for adaptive linear section, while a hyperparameter-determined long short-term memory (LSTM) is used for nonlinear section. In particular, an improved sparrow search algorithm combining opposition-based learning and Cauchy variance (ISSA) is used to construct the optimal hyperparameters for the two-layer LSTM. computer simulations with the UMass weather dataset reveal that the proposed scheme considerably outperforms three benchmarking methods in terms of data prediction accuracy and robustness.

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Security and privacy in 6G networks:New areas and new challenges

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Digital Communications and Networks 2020年第3期6卷 281-291页

作者： Minghao Wang Tianqing Zhu Tao Zhang Jun Zhang Shui Yu Wanlei Zhou School of Computer Science University of Technology SydneyUltimo2007Australia School of Software and Electrical Engineering Swinburne University of TechnologyAustralia

With the deployment of more and more 5g networks,the limitations of 5g networks have been found,which undoubtedly promotes the exploratory research of 6G networks as the next generation *** investigations include the fundamental security and privacy problems associated with 6G ***,in order to consolidate and solidify this foundational research as a basis for future investigations,we have prepared a survey on the status quo of 6G security and *** survey begins with a historical review of previous networking technologies and how they have informed the current trends in 6G *** then discuss four key aspects of 6G networks–real-time intelligent edge computing,distributed artificial intelligence,intelligent radio,and 3D intercoms–and some promising emerging technologies in each area,along with the relevant security and privacy *** survey concludes with a report on the potential use of *** of the references used in this paper along and further details of several points raised can be found at:***.

关键词： 6G Cyber security Privacy preservation Communication

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Design of 5G Vehicle-road-cloud Collaborative Architecture for Vehicle Platooning

Design of 5G Vehicle-road-cloud Collaborative Architecture f...

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International Conference on Network and Information Systems for computers (ICNISC)

作者： Hongyan Shi Xinran Qu Jing Ma Jiajian Li School of Mechanical Engineering Dalian University of Technology DUT Dalian China Department of Computer Science & Software Engineering School of Engineering Computer & Mathematical Sciences Auckland University of Technology Auckland New Zealand

ISBN: (纸本)9781665453523

This paper presents an architecture design of vehicle-road-cloud collaboration based on a 5g communication network, which supports vehicle platooning and applies it in the case of vehicle formation crossing the border. The architecture includes roadside, cloud systems, 5g network, and infrastructure deployment connecting vehicles, as well as specifications for interfaces of automated vehicles (network communication interface specifications and application interface specifications). In addition, solutions and simple tests are proposed for the cross-border problems in the case of vehicle platooning crossing the border, mainly including interconnection delay between PLMNs, and low delay communication between leader and team members' vehicles and roadside infrastructure.

关键词： computers 5G mobile communication Collaboration computer architecture Delays Communication networks Security

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A survey of Blockchain consensus algorithms:mechanism, design and applications

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Science China(Information Sciences) 2021年第2期64卷 100-114页

作者： Xiang FU Huaimin WANG Peichang SHI College of Computer National University of Defense Technology Key Laboratory of Software Engineering for Complex Systems National University of Defense Technology

In 2008, Blockchain was introduced to the world as the underlying technology of the Bitcoin system. After more than a decade of development, various Blockchain systems have been proposed by both academia and industry. This paper focuses on the consensus algorithm, which is one of the core technologies of Blockchain. In this paper, we propose a unified consensus algorithm process model that is suitable for Blockchains based on both the chain and directed acyclic graph(DAG) structure. Subsequently, we analyze various mainstream Blockchain consensus algorithms and classify them according to their design in different phases of the process model. Additionally, we present an evaluation framework of Blockchain consensus algorithms and then discuss the security design principles that enable resistance from different ***, we provide some suggestions for selecting consensus algorithms in different Blockchain application scenarios.

关键词： Blockchain consensus algorithm Byzantine fault-tolerant process model design principles

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