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26th international Symposium on Stabilization, Safety, and security of Distributed Systems, SSS 2024

26th International Symposium on Stabilization, Safety, and S...

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26th international Symposium on Stabilization, Safety, and security of Distributed Systems, SSS 2024

ISBN: (纸本)9783031744976

the proceedings contain 33 papers. the special focus in this conference is on Stabilization, Safety, and security of Distributed Systems. the topics include: Invited Paper: Gathering Oblivious Robots in the Plane;invited Paper: the Smart Contract Model;invited Paper: Using Signed Formulas for Online Certification;optimal Asynchronous Perpetual Grid Exploration;gathering of Robots in Butterfly Networks;brief Announcement: Pebble Guided Rendezvous Despite Fault;complete Graph Identification in Population Protocols;efficient Self-stabilizing Simulations of Energy-Restricted Mobile Robots by Asynchronous Luminous Mobile Robots;brief Announcement: Perpetual Exploration of Triangular Grid by Myopic Oblivious Robots Without Chirality;an Optimal Algorithm for Geodesic Mutual Visibility on Hexagonal Grids;Coating in SILBOT with One Axis Agreement;rendezvous and Merging for Two Metamorphic Robotic Systems Without Global Compass;gathering Semi-Synchronously Scheduled Two-State Robots;selective Population Protocols;partially Disjoint Shortest Paths and Near-Shortest Paths Trees;brief Announcement: Towards Proportionate Fair Assignment;BlindexTEE: A Blind Index Approach Towards TEE-Supported End-to-End Encrypted DBMS;tight Bounds for Constant-Round Domination on Graphs of High Girth and Low Expansion;adding All Flavors: A Hybrid Random Number Generator for dApps and Web3;SUPI-Rear: Privacy-Preserving Subscription Permanent Identification Strategy in 5G-AKA;anomaly Detection Within Mission-Critical Call Processing;brief Announcement: Make Master Private-Keys Secure by Keeping It Public;Selection Guidelines for Geographical SMR Protocols: A communication Pattern-Based Latency Modeling Approach;byzantine Reliable Broadcast with One Trusted Monotonic Counter;brief Announcement: On the Feasibility of Local Failover Routing on Directed Graphs;TRAIL: Cross-Shard Validation for Byzantine Shard Protection;softening the Impact of Collisions in Contention Resolution;generating the Converge

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Does Volunteer Engagement Pay Off? An Analysis of User Participation in Online Citizen science Projects 1

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28th international conference on Collaboration Technologies and Social computing (CollabTech)

作者： Krukowski, Simon Amarasinghe, Ishari Felipe Gutierrez-Paez, Nicolas Hoppe, H. Ulrich RIAS Inst Duisburg Germany Univ Pompeu Fabra ICT Dept Barcelona Spain

ISBN: (数字)9783031202186

ISBN: (纸本)9783031202179;9783031202186

An increasing number of Citizen science (CS) projects rely on digital technologies for data collection and processing as well as for the coordination and communication between participants. However, these projects raise the question about whether the interplay between professional scientists and volunteers actually constitutes a collaborative working relationship in terms of shared goals, initiative and responsibility. A related question is about added values and benefits that volunteers gain by their participation. these issues have been studied with a sample of projects taken from the Zooniverse platform, particularly by analysing the communications found in the projects' forum pages. Social network analysis techniques are used to identify structural characteristics underlying the forum interactions as basic measures that are repeated over time sequences to capture the dynamic changes. the results show that a smaller group of volunteer users is responsible for a large portion of communication and coordination actions, with some of them being promoted to moderators, which can be seen as a reward and incentive.

关键词： Citizen science Volunteer engagement Network measures of collaboration

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Securing Smart Home IoT devices using Hyperledger Fabric

Securing Smart Home IoT devices using Hyperledger Fabric

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international conference on computing and Networking Technology (ICCNT)

作者： R Pranesh B Lalith Guptha K Aswinkumar B Pramoth K Abirami Department of Computer Science and Engineering Amrita School of Computing Amrita Vishwa Vidyapeetham Coimbatore India

ISBN: (数字)9798350370249

ISBN: (纸本)9798350370270

the rapid growth of Smart Home applications has broadened the scope for a wide range of security concerns. Blockchain technology, specifically Hyperledger Fabric, offers a promising solution. the proposed model explores its application, leveraging the Fablo toolkit to enhance the security of Smart Home IoT devices. the decentralized nature of Hyperledger Fabric, with features such as its consensus mechanism and chaincode, helps mitigate common security issues like unauthorized access and device tampering. the resultant tamperproof network, coupled with favorable metrics like latency and transaction throughput, underscores the robustness of the proposed model. Our model promises low latency and high transaction throughput. the metrics assist the model in securely and efficiently handling a large number of transactions without compromising on speed and security.

关键词： Technological innovation Distributed ledger Smart cities Smart homes Computer architecture throughput Fabrics Consensus protocol security Internet of things

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Exploring the Role of Wireless communications in Industry 4.0: Enhancing Automation, Efficiency, and Scalability

Exploring the Role of Wireless Communications in Industry 4....

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Distributed computing and Electrical Circuits and Electronics (ICDCECE), IEEE international conference on

作者： Dhanraj Vikas Katkar BMS Department Johnson Controls Mumbai India

ISBN: (数字)9798331533663

ISBN: (纸本)9798331533670

the advancement of wireless communications has catalyzed a paradigm shift in industrial environments, ushering in the era of Industry 4.0. this paper investigates the critical role wireless technologies play in supporting increased automation, real-time data exchange, and scalable industrial operations. Wireless communication enables seamless connectivity between machines, devices, and control systems, facilitating Industrial Internet of things (IIoT) applications and smart manufacturing processes. through an in-depth analysis of wireless protocols, latency considerations, and the integration of emerging 5G networks, this study examines how wireless communications optimize industrial workflows. the research highlights key findings on improved efficiency, network reliability, and security challenges. the results demonstrate significant enhancements in operational performance through wireless integration, paving the way for fully automated industrial ecosystems. Experimental evaluations demonstrate that 5G NR offers the lowest latency and highest throughput, whereas LoRa exhibits superior range and energy efficiency but at the cost of increased latency. these findings underline the need for protocol selection based on specific industrial requirements.

关键词： Wireless communication Protocols 5G mobile communication LoRa Skin Real-time systems Fourth Industrial Revolution communication system security Industrial Internet of things Smart manufacturing

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TensorTEE: Unifying Heterogeneous TEE Granularity for Efficient Secure Collaborative Tensor computing 24

TensorTEE: Unifying Heterogeneous TEE Granularity for Effici...

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29th ACM international conference on Architectural Support for Programming Languages and Operating Systems, ASPLOS 2024

作者： Han, Husheng Zheng, Xinyao Wen, Yuanbo Hao, Yifan Feng, Erhu Liang, Ling Mu, Jianan Li, Xiaqing Ma, Tianyun Jin, Pengwei Song, Xinkai Du, Zidong Guo, Qi Hu, Xing SKLP Institute of Computing Technology Chinese Academy of Sciences Beijing China University of Chinese Academy of Sciences Beijing China Cambricon Technologies Beijing China Ipads Shanghai Jiao Tong University Shanghai China Peking University Beijing China University of Science and Technology of China Hefei China ZGC LAB Beijing China Shanghai Innovation Center for Processor Technologies SHIC Shanghai China Shanghai China

ISBN: (纸本)9798400703911

Heterogeneous collaborative computing with NPU and CPU has received widespread attention due to its substantial performance benefits. To ensure data confidentiality and integrity during computing, Trusted Execution Environments (TEE) is considered a promising solution because of its comparatively lower overhead. However, existing heterogeneous TEE designs are inefficient for collaborative computing due to fine and different memory granularities between CPU and NPU. 1) the cacheline granularity of CPU TEE intensifies memory pressure due to its extra memory access, and 2) the cacheline granularity MAC of NPU escalates the pressure on the limited memory storage. 3) Data transfer across heterogeneous enclaves relies on the transit of non-secure regions, resulting in cumbersome re-encryption and *** address these issues, we propose TensorTEE, a unified tensor-granularity heterogeneous TEE for efficient secure collaborative tensor computing. First, we virtually support tensor granularity in CPU TEE to eliminate the off-chip metadata access by detecting and maintaining tensor structures on-chip. Second, we propose tensor-granularity MAC management with predictive execution to avoid computational stalls while eliminating off-chip MAC storage and access. Moreover, based on the unified granularity, we enable direct data transfer without re-encryption and scheduling dilemmas. Our evaluation is built on enhanced Gem5 and a cycle-accurate NPU simulator. the results show that TensorTEE improves the performance of Large Language Model (LLM) training workloads by 4.0x compared to existing work and incurs only 2.1% overhead compared to non-secure training, offering a practical security assurance for LLM training. © 2025 Association for computing Machinery. All rights reserved.

关键词： Cache memory

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Semi-Supervised Federated Learning with non-IID Data: Algorithm and System Design 23

Semi-Supervised Federated Learning with non-IID Data: Algori...

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23rd IEEE international conference on High Performance computing and communications, 7th IEEE international conference on Data science and Systems, 19th IEEE international conference on Smart City and 7th IEEE international conference on Dependability in Sensor, Cloud and Big Data Systems and Applications, HPCC-DSS-SmartCity-DependSys 2021

作者： Zhang, Zhe Ma, Shiyao Nie, Jiangtian Wu, Yi Yan, Qiang Xu, Xiaoke Niyato, Dusit School of Data Science and Technology Heilongjiang University Harbin150080 China Cryptology and Network Security Laboratory of Heilongjiang University Harbin150080 China College of Information and Communication Engineering Dalian Minzu University China School of Computer Science and Engineering Nanyang Technological University Singapore WeBank Co. Ltd. China

ISBN: (纸本)9781665494571

Federated Learning (FL) allows edge devices (or clients) to keep data locally while simultaneously training a shared high-quality global model. However, current research is generally based on an assumption that the training data of local clients have ground-truth. Furthermore, FL faces the challenge of statistical heterogeneity, i.e., the distribution of the client's local training data is non-independent identically distributed (non-IID). In this paper, we present a robust semi-supervised FL system design, where the system aims to solve the problem of data availability and non-IID in FL. In particular, this paper focuses on studying the labels-at-server scenario where there is only a limited amount of labeled data on the server and only unlabeled data on the clients. In our system design, we propose a novel method to tackle the problems, which we refer to as Federated Mixing (FedMix). FedMix improves the naive combination of FL and semi-supervised learning methods and designs parameter decomposition strategies for disjointed learning of labeled, unlabeled data, and global models. To alleviate the non-IID problem, we propose a novel aggregation rule based on the frequency of the client's participation in training, namely the FedFreq aggregation algorithm, which can adjust the weight of the corresponding local model according to this frequency. Extensive evaluations conducted on CIFAR-10 dataset show that the performance of our proposed method is significantly better than those of the current baseline. It is worth noting that our system is robust to different non-IID levels of client data. © 2021 IEEE.

关键词： Training Smart cities Design methodology Training data Semisupervised learning Collaborative work Data models

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Quantum Man-in-the-Middle Attacks on QKD Protocols: Proposal of a Novel Attack Strategy

Quantum Man-in-the-Middle Attacks on QKD Protocols: Proposal...

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international conference on Contemporary computing and Informatics (IC3I)

作者： Hicham Amellal Salma El Hajjami Keshav Kaushik Gunjan Chhabra Suman Avdhesh Yadav Department of Computer Science Faculty of Science LabSIV Agadir Morocco School of Computer Sciences University of Petroleum and Energy Studies Dehradun Uttarakhand India Department of CSE Graphic Era Hill University Dehradun Uttarakhand India Department of Computer Science Amity School of Engineering and Technology Amity University Greater Noiida Uttar Pradesh India

Quantum Key Distribution (QKD) protocols theo- retically propose a high-security level based on the advantages of quantum computing. this paper analyze the security level of one of the most well-known QKD protocols, BB84, against the man-in-the-middle attack strategy. Specifically, we leverage quantum entanglement to enhance communication security by the injection of a malicious photon in the middle of transmission. In the security analysis, we measure the variation in mutual information between the sender-receiver and sender-attacker to assess the protocol's resilience against potential attacks.

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the Detection of Mirai Botnet Attack on the Internet of things (IoT) Device Using Support Vector Machine (SVM) Model 10

The Detection of Mirai Botnet Attack on the Internet of Thin...

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10th international conference on Information and communication Technology, ICoICT 2022

作者： Azhari, Revaldi Gilang Suryani, Vera Pahlevi, Rizka Reza Wardana, Aulia Arif School of Computing Telkom University Bandung Indonesia Doctoral School Information and Communication Technology Wroclaw University of Science and Technology Wroclaw Poland

ISBN: (数字)9781665481656

ISBN: (纸本)9781665481656

there are various types of attacks on IoT devices, one of which is Mirai botnet that attacks a number of IoT devices such as web cameras, security cameras, and routers. Even it has caused internet downtime for the users in Europe and North America. Hence, it is deemed necessary to have a system to detect Mirai botnet attack using the Support Vector Machine (SVM) model. this model becomes a solution to detect Mirai botnet attacks for having good generalization performance when the parameter is properly configured in modeling the training set (training dataset) and getting good classification results without a lot of training data. the results of the analysis from the use of the SVM model showed the accuracy of 92.91% with a linear kernel and max_iter 10000. Also, the performance of the Support Vector Machine (SVM) model based on the precision parameter obtained a value of 94.13% with max_iter 10000 and the recall parameter resulted in a value of 91.10% with max_iter 10000. © 2022 IEEE.

关键词： Support vector machines

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Comparative security Analysis of Apple Pay and Google Wallet: Strengths, Weaknesses, and threats

Comparative Security Analysis of Apple Pay and Google Wallet...

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international conference on Radioelektronika

作者： Hosam Alamleh Sima Jafarikhah Baker Al Smadi Computer Science University of North Carolina Wilmington Wilmington North Carolina Computer Science Grambling State University Grambling Louisiana

ISBN: (数字)9798331544478

ISBN: (纸本)9798331544485

Mobile payment platforms, such as Apple Pay and Google Wallet, have revolutionized financial transactions through Near Field communication (NFC), tokenization, and biometric authentication. Although both systems implement robust security measures, their architectures introduce distinct security trade-offs. Apple Pay's Secure Element ensures hardware-isolated token storage, providing a high level of protection against malware and unauthorized access. In contrast, Google Wallet relies on Host Card Emulation (HCE), allowing for broader device compatibility but increasing the attack surface due to its software-based token storage and cloud dependencies. this paper conducts a comparative security analysis of these two platforms, identifying key vulnerabilities, including NFC relay attacks, unauthorized token retrieval, backend authentication weaknesses, and cloud-based risks. In addition, we explore SIM-swapping attacks, phishing schemes, and malware threats targeting mobile payments. By examining design challenges, architectural choices, and security mechanisms, we assess trade-offs between hardware-and software-based security models.

关键词： Cloud computing Online banking Authentication Near field communication Tokenization Malware Fraud security Relays Protection

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Deep Reinforcement Learning-Based Multi-Task Offloading Strategy in Mobile Edge computing

Deep Reinforcement Learning-Based Multi-Task Offloading Stra...

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Frontiers Technology of Information and Computer (ICFTIC), IEEE international conference on

作者： Yaxi Yang Chenwei Feng School ofOpto-electronic and Communication Engineering Xiamen University of Technology Xiamen China

In multi-vehicle road segments in telematics scenarios, individual vehicles often face limitations in processing many offloading tasks within a short timeframe. Mobile Edge computing is introduced to address this challenge, wherein idle vehicles parked at the roadside are leveraged as edge devices. this allows for collaborative offloading resource allocation involving roadside units and vehicle terminal equipment. An optimization problem is formulated with the objective of reducing offloading latency and minimizing user energy consumption. this problem exhibits non-convex characteristics, including a non-convex objective function. Furthermore, it can be reformulated as a Markov Decision Process (MDP). To tackle the complexities associated with both discrete and continuous action spaces, the Parametric Deep Q-Network (P-DQN) method is employed to address this optimization challenge. Experimental results demonstrate that the proposed method effectively enhances user vehicle utility within the same scenario.

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