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Impact of UAV-Based Transmitter Mobility on Physical Layer Security

IEEE Transactions on Information Forensics and Security 2025年 20卷 5604-5619页

作者： Ruby, Rukhsana Elhalawany, Basem M. Pham, Quoc-Viet Wu, Kaishun Wang, Lu Shenzhen University College of Computer Science and Software Engineering Shenzhen518060 China Kuwait College of Science and Technology Electronics and Communication Engineering department Block 4 Doha District93004 Kuwait Trinity College School of Computer Science and Statistics Dublin 2 DublinD02 PN40 Ireland

Owing to flexible management and low overhead, wireless physical layer security (PLS) has been applied to support many critical applications (e.g., data dissemination) of unmanned aerial vehicles (UAVs)-based mobile communication networks in emergency scenarios. Although the impact of static network scenarios or receiver mobility on PLS has been well studied, there is no much work that studies the impact of UAV-based transmitter mobility on PLS. To fill this gap, in this paper, we investigate PLS of a scenario, where a random mobile UAV-based transmitter transmits information to a static ground entity under Rayleigh fading channel. More specifically, we consider a communication system, in which a mobile UAV hovers over a region to collect information and then disseminates this information to a static ground network entity in a confidential manner under the presence of an eavesdropper. Because of popularity and practicality, the UAV is assumed to hover following the random way point (RWP) mobility model. We investigate the secrecy characteristics of the UAV under steady running state in terms of ergodic secrecy capacity (ESC), positive secrecy capacity probability (PSCP) and secrecy outage probability (SOP) for the communication between the UAV and the receiver. We then investigate the secrecy performance of the proposed system while considering the pause time of the RWP mobility model adopted by the UAV. We further extend our proposed theoretical model to other realistic scenarios, including the presence of multiple cooperative and non-cooperative eavesdroppers, and study the PSCP and SOP metrics of the corresponding system. Furthermore, we propose three types of secrecy improvement strategies for the considered communication model. We strike a good trade-off between the secrecy improvement and transmit outage probability. Extensive simulations have been conducted to validate our theoretical analysis as well as the effectiveness of the proposed secrecy improvement s

关键词： Drones

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OSCO: An Efficient Segment Routing Scheme for Backup Path 19

OSCO: An Efficient Segment Routing Scheme for Backup Path

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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： Yang, Shu Li, Jiaming Cui, Laizhong Shenzhen University College of Computer Science and Software Engineering Shenzhen518060 China Peng Cheng Laboratory Shenzhen518060 China

ISBN: (纸本)9798350346558

As artificial intelligence enters the stage of ubiquitous intelligence, the explosive growth of data makes some delay-sensitive services face greater challenges. The environment of ubiquitous intelligence deployment is uncertain, and link failures often occur. Fast Reroute(FRR) can restore communication through backup paths in time after a link failure, but it encounters the loop problem of forwarding in traditional network forwarding. Segment Routing can avoid loops and forward efficiently under the decision of the controller. But the backup path deployed by the controller will bring additional control overhead. The redundant part of the backup path makes the network bear unnecessary load. So we propose an optimization scheme that greatly reduces the control overhead of the backup path with Segment Routing(OSCO). OSCO only labels the hot points along the backup paths and calculates the backup path with the goal of low control overhead. We use three algorithms to compute the backup path and deploy it to the real networks and generated topologies to evaluate the performance. The experimental results show that OSCO can reduce the control overhead of the backup path by up to 99%. © 2022 IEEE.

关键词： Controllers

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Deep self-attention for sequential recommendation 33

Deep self-attention for sequential recommendation

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33rd International Conference on software engineering and Knowledge engineering, SEKE 2021

作者： Zhang, Beichuan Xiao, Zhijiao Zhong, Shenghua College of Computer Science and Software Engineering Shenzhen University China

ISBN: (纸本)1891706527

Sequential recommendation aims to recommend the next item that a user will likely interact with by capturing the useful sequential patterns from users' historical behaviors. Recently, it has become an important and popular component in various e-commerce platforms. As a successful network, Transformer has been widely used to adaptively capture the dynamics of users' historical behaviors for sequential recommendation. In recommender systems, the size of embedding is usually set to be small. Under small embedding, the dot-product in Transformer may have the limitation on calculating the complex relevance between keys and queries. To address the common but neglected issue, in this paper, we present a new model, Deep Self-Attention for Sequential Recommendation (DSASrec), which proposes a chunking deep attention to compute attention weights. The chunking deep attention has two modules: a deep module and a chunking module. The deep module is used to improve the nonlinearity of the attention function. The chunking module is used to calculate attention weights several times like the multi-head attention in Transformer. Extensive experiments on three benchmark datasets show that our model can achieve state-of-the-art results. Our implementation is available in PyTorch. © 2021 Knowledge Systems Institute Graduate School. All rights reserved.

关键词： Recommender systems

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A Practical Learning-Based FTL for Memory Constrained Mobile Flash Storage

A Practical Learning-Based FTL for Memory Constrained Mobile...

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2025 Design, Automation and Test in Europe Conference, DATE 2025

作者： Du, Zelin Huang, Kecheng Wang, Tianyu Yao, Xin Chen, Renhai Shao, Zili The Chinese University of Hong Kong Department of Computer Science and Engineering Hong Kong College of Computer Science and Software Engineering Shenzhen University Shenzhen China Huawei Technologies China

ISBN: (纸本)9783982674100

The rapidly growing mobile market is pushing flash storage manufacturers to expand capacity into the terabyte range. However, this presents a significant challenge for mobile storage management: more logical-to-physical page mappings are desired to be efficiently managed and cached while the available caching space is extremely limited. This motivates us to shift toward a new learning-based paradigm: rather than maintaining mappings for individual pages, the learning-based approach can represent mapping relationships for a set of continuous pages. However, to construct linear models, existing methods that either consume the already-limited memory space or reuse flash garbage collection demonstrate poor model construction capabilities or significantly degrade flash performance, making them impractical for real-world use. In this paper, we propose LFTL, a practical, learning-based on-demand flash translation layer design for flash management in mobile devices. In contrast to prior work that centered around gathering sufficient mappings for linear model construction, our key insight is that linear patterns can be extracted and refined by leveraging the orderly, LPA-aligned write stream typical of mobile devices. By doing this, highly accurate linear models can be constructed regardless of the constraints of mobile device's cache limitation. We have implemented a fully functional prototype of LFTL based on FEMU. Our evaluation results show that LFTL is more adaptable to memory-constrained storage devices than state-of-the-art learning-based approaches. © 2025 EDAA.

关键词： flash translation layer learned index Mobile flash storage

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Securing Multi-Tenant Cloud Environments with Fully Homomorphically Encrypted Secure Multiparty Computation

Securing Multi-Tenant Cloud Environments with Fully Homomorp...

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2024 IEEE International Conference on Recent Advances in science and engineering Technology, ICRASET 2024

作者： Ponmalar, A. Pandiarajan, Rajkumar Sudha, I. Ramesh, P.S. Jagannathan, J. R.M.K. Engineering College Tamilnadu Chennai India School of Computing Kalasalingam Academy Of Research and Education Department of Information and Technology Krishnankoil Virudhunagar India Saveetha Institute of Medical and Technical Sciences Simats University Saveetha School of Engineering Department of Computer Science and Engineering Tamil Nadu Chennai India Vel Tech Rangarajan Dr.Sagunthala R&d Institute of Science and Technology Department of Computer Science and Engineering Tamil Nadu Chennai India Vellore Institute of Technology School of Computer Science Engineering and Information Systems Department of Software and Systems Engineering Katpadi Vellore India

ISBN: (纸本)9798350388602

The aim of this work is to examine the application of FHE-SMPC as a viable security model for protecting the tenant isolation in multi-tenant cloud model. In multi-tenant environments, where various customers use the same cloud system, controlling the data leakage and protecting privacy is a major issue. Actual safeguards fail to meet the needs of data protection during processing, therefore, the introduction of highly elaborate encryption processes must happen. This research work focuses on the use of FHE technique and SMPC such that data can undergo processing while the plaintext remains unknown to any party. A kind of approach is based on the acquisition of synthetic and real data sets, system-level architecture, and cryptographic algorithms using open-source libraries with cloud service providers. The results of such implementation indicate that despite the fact that the FHE-SMPC system has a considerable computational overhead in comparison with classical schemes it is very secure and protected against different types of cryptographic attacks. The problem of scalability is still an open issue, but the complex is quite efficient with various data types that is why it can be used in various cloud applications. This paper offers a pertinent solution for security of data that is housed in multitenant environment and will go along way in creating fertile ground for research and innovation in secure clouding. © 2024 IEEE.

关键词： Cloud security

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Recurrent neural graph collaborative filtering 33

Recurrent neural graph collaborative filtering

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33rd International Conference on software engineering and Knowledge engineering, SEKE 2021

作者： Zhang, Beichuan Xiao, Zhijiao Zhong, Shenghua College of Computer Science and Software Engineering Shenzhen University China

ISBN: (纸本)1891706527

Collaborative Filtering (CF) is a prevalent technique in recommender systems. Substantial research focuses on learning the embedding of users and items via exploiting past user-item interactions. Recent years have witnessed the boom of Graph Convolutional Networks (GCNs) on CF. Performing graph convolution iteratively, GCN-based models concatenate/average/sum all outputs from different graph convolution layers to generate the embeddings of users and items. Although the previous methods have been proven effective, the pooling operations in the previous methods fail to consider the outputs from different graph convolution layers have different weights and the weights are related to sequential dependencies from precursor nodes. To resolve the aforementioned problems, in this work, we present a new model, Recurrent Neural Graph Collaborative Filtering (RNGCF), which proposes a sequential dependency construction module to adaptively generate the embeddings. Specifically, the module applies a gated recurrent unit (GRU) to learn the sequential dependencies from precursor nodes and an adaptive gated unit (AGU) to adaptively construct the embeddings based on the sequential dependencies. Extensive experiments on three benchmark datasets show that our model outperforms state-of-the-art models consistently. Our implementation is available in PyTorch. © 2021 Knowledge Systems Institute Graduate School. All rights reserved.

关键词： Recommender systems

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ReForm-Eval: Evaluating Large Vision Language Models via Unified Re-Formulation of Task-Oriented Benchmarks 24

ReForm-Eval: Evaluating Large Vision Language Models via Uni...

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32nd ACM International Conference on Multimedia, MM 2024

作者： Li, Zejun Wang, Ye Du, Mengfei Liu, Qingwen Wu, Binhao Zhang, Jiwen Zhou, Chengxing Fan, Zhihao Fu, Jie Chen, Jingjing Wei, Zhongyu Huang, Xuanjing School of Data Science Fudan University Shanghai China Academy for Engineering and Technology Fudan University Shanghai China Institute of Science and Technology for Brain-Inspired Intelligence Fudan University Shanghai China Software College Northeastern University Liaoning Shenyang China Alibaba Group Zhejiang Hangzhou China Department of Computer Science and Engineering Hong Kong University of Science and Technology Hong Kong School of Computer Science Fudan University Shanghai China

ISBN: (纸本)9798400706868

Recent years have witnessed remarkable progress in the development of large vision-language models (LVLMs). Benefiting from the strong language backbones and efficient cross-modal alignment strategies, LVLMs exhibit surprising capabilities to perceive visual signals and perform visually grounded reasoning. However, the capabilities of LVLMs have not been comprehensively and quantitatively evaluated. Most existing multi-modal benchmarks require task-oriented input-output formats, posing great challenges to automatically assess the free-form text output of LVLMs. To effectively leverage the annotations available and reduce the manual efforts required for constructing new benchmarks, we propose to re-formulate existing benchmarks into unified LVLM-compatible formats. Through systematic data collection and reformulation, we present ReForm-Eval benchmark, offering substantial data for evaluating various capabilities of LVLMs. Through extensive experiments and analysis in ReForm-Eval, we demonstrate the comprehensiveness and reliability of ReForm-Eval in assessing various LVLMs. Our benchmark and evaluation framework is now available at https://***/FudanDISC/ReForm-Eval © 2024 ACM.

关键词： Visual languages

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Investigating Online Searching Behavior Based on Google Trends in MENA Region Before and After COVID-19 53

Investigating Online Searching Behavior Based on Google Tren...

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53rd IEEE ASEE Frontiers in Education International Conference, FIE 2023

作者： Belkacem, Abdelkader Nasreddine Jamil, Nuraini Alrabaee, Saed College of It Uae University Dept. of Computer and Network Engineering Al Ain United Arab Emirates College of It Uae University Dept. of Computer Science and Software Engineering Al Ain United Arab Emirates College of It Uae University Dept. of Information Systems and Security Al Ain United Arab Emirates

ISBN: (纸本)9798350336429

The outbreak of the coronavirus disease (COVID-19) has had a profound impact on education worldwide. The rise of remote learning is one of the most significant changes in this regard, as many schools and universities were forced to close down by regional health authorities. This has also caused people to become more conservative in trade-offs between healthcare and education. Google Trends is the most common tool for analyzing online search behaviors. It is a free resource that provides information on the trends and changes in users' online interests over time based on certain terms and subjects. The online search queries on Google can be used to assess users' behaviors concerning online learning to forecast their choices regarding online education. This paper examines the frequency of users' web searches for online communication tools, courses, and learning terms. We statistically compared users in the Middle East and North Africa regions by using the volumes of searches recorded on Google Trends from January 2016 to August 2022. Moreover, we used machine learning techniques to identify differences among the keywords used. The findings statistically show that COVID-19 has led to an increase in the extent of students' attention to and interest in online learning. © 2023 IEEE.

关键词： COVID-19 Google Trends MENA Online learning Remote learning Web Behavior

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Optimizing User Data Privacy and Confidentiality in Cloud Storage Systems Through Advanced Obfuscrypt Encryption Methods for Enhanced Security and Efficient Data Protection

Optimizing User Data Privacy and Confidentiality in Cloud St...

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2024 IEEE International Conference on Recent Advances in science and engineering Technology, ICRASET 2024

作者： Ramesh, P.S. Sudha, I. Jagannathan, J. Pandiarajan, Rajkumar Ponmalar, A. Vel Tech Rangarajan Dr.Sagunthala R&d Institute of Science and Technology Department of Computer Science and Engineering Tamilnadu Chennai India Saveetha Institute of Medical and Technical Sciences Simats University Saveetha School of Engineering Department of Computer Science and Engineering Tamil Nadu Chennai India Vellore Institute of Technology School of Computer Science Engineering and Information Systems Department of Software and Systems Engineering Katpadi Vellore India Kalasalingam Academy Of Research and Education School of Computing Department of Information and Technology Krishnankoil Virudhunagar India R.M.K. Engineering College Tamilnadu Chennai India

ISBN: (纸本)9798350388602

With a steady increase in the adoption of cloud storage systems, the client's data are at risk of being leaked. There are several methods of encrypting data which are quite useful but they lack some of the features of today's sophisticated threats. The methodology used in this research propose an encryption framework known as Obfuscrypt to improve the issues of data privacy as well as confidentiality of data stored in cloud storage systems. Obfuscrypt uses conventional encryption strategies incorporated with obfuscation and post-quantum cryptographic algorithms to provide a complex security plan immune to classic and quantum approaches. Some of the proposed other innovations include the decentralised key management with multi-factor authentication and RBAC-ABE as the fine-grained access control techniques. These features make it possible for only the authorized personnel to be in a position to be in a position of decrypting and retrieving the stored data thereby minimizing the possibility of the data being accessed by unauthorized people. The work also considers the issue of performance efficiency of the cloud storage systems. Similar to other symmetric encryption programs, Obfuscrypt uses light encryption algorithms and brings in to play the hardware acceleration so that it may infuse lesser computational overhead in the process of encryption and decryption. As can be clearly seen from the results, the actual work proves that the proposed framework not only offers a higher level of security but does not affect the system performance to a great extent. Also, the study addresses the post-quantum cryptographic algorithm for making the system resistant to an attack that can be posed by quantum computing systems. With the help of these improved algorithms, Obfuscrypt guarantees the permanent data security in the world of constantly emerging cyber threats. © 2024 IEEE.

关键词： Data obfuscation

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Advanced QUasi-Affine TRansformation Evolutionary (QUATRE) Algorithm and Its Application for Neural Network 4th

Advanced QUasi-Affine TRansformation Evolutionary (QUATRE) A...

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4th International Conference on Smart Vehicular Technology, Transportation, Communication and Applications, VTCA 2021

作者： Hu, Pei Pan, Jeng-Shyang Chu, Shu-Chuan College of Computer Science and Engineering Shandong University of Science and Technology Qingdao266590 China School of Computer and Software Nanyang Institute of Technology Nanyang473004 China College of Science and Engineering Flinders University Sturt Road Bedford ParkSA5042 Australia

ISBN: (纸本)9789811640384

The QUasi-Affine TRansformation Evolution (QUATRE) was first proposed by Meng et al. in 2016. It has the characteristics of few parameters and fast convergence. This paper brings two methods to improve its solution quality. The opposite position and comprehensive learning greatly advance the ability of jumping out of local traps when the QUATRE falls into stagnation. Their performance is verified by 23 benchmark functions. In the end, they succeed to train the parameters of neural network and predict the long-term traffic flow in Qingdao. © 2022, The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd.

关键词： Benchmarking

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