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检索条件"任意字段=Proceedings of the 22nd International Conference on Distributed Computing and Networking"

共 1646 条记录，以下是21-30 订阅

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distributed Dependent Task Offloading in CPU-GPU Heterogenous MEC: A Federated Reinforcement Learning Approach 22

Distributed Dependent Task Offloading in CPU-GPU Heterogenou...

引用

IEEE 22nd international conference on Trust, Security and Privacy in computing and Communications (TrustCom) / BigDataSE conference / CSE conference / EUC conference / ISCI conference

作者： Huang, Hualong Duan, Zhekai Zhan, Wenhan Liu, Yichen Wang, Zhi Zhao, Zitian Univ Elect Sci & Technol China Chengdu 611731 Peoples R China Univ Edinburgh Edinburgh Midlothian Scotland

ISBN: (纸本)9798350381993;9798350382006

Mobile edge computing (MEC) has emerged as a promising paradigm to enable computation-intensive and latency-sensitive mobile applications by offloading tasks to proximal edge servers. This paper proposes a novel federated reinforcement learning framework called Transformer-based Federated Soft Actor-Critic (TFSAC) to address a joint computation offloading and resource scheduling problem in a CPU-GPU heterogeneous MEC network while preserving privacy. Specifically, a graph attention network (GAT) extracts high-dimensional features from the task dependency graph. Rather than simply averaging weights, TFSAC applies transformer encoders to learn contextual relationships between agents and enable selective aggregation of relevant knowledge during federated model training to preserve agents' privacy. Experiments on real-world trace data demonstrate TFSAC's superiority over benchmarks in maximizing quality-of-service (QoS) across configurations.

关键词： Mobile edge computing computation offloading federated reinforcement learning privacy

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Byzantine-Robust Federated Learning through Dynamic Clustering 22

Byzantine-Robust Federated Learning through Dynamic Clusteri...

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IEEE 22nd international conference on Trust, Security and Privacy in computing and Communications (TrustCom) / BigDataSE conference / CSE conference / EUC conference / ISCI conference

作者： Wang, Hanyu Wang, Liming Li, Hongjia Chinese Acad Sci Inst Informat Engn Beijing Peoples R China Univ Chinese Acad Sci Sch Cyber Secur Beijing Peoples R China

ISBN: (纸本)9798350381993;9798350382006

Federated learning enables distributed and collaborative learning among multiple participants while protecting their privacy. However, due to its distributed nature, federated learning is vulnerable to Byzantine attacks. These attacks can poison the data or directly modify the model parameters, making the global model performance degrade or even leaving a backdoor. Existing strategies for mitigating Byzantine attacks require a priori information about the number of attackers or require additional validation datasets. However, prior knowledge of the number of Byzantine clients or the collection of representative validation datasets is not always feasible in practice. Moreover, recent research has shown that well-designed attacks can make malicious updates indistinguishable from benign ones by making them highly similar, thus bypassing existing defense methods that rely on these metrics. To tackle these problems, we propose Dynamic Clustering based Federated Learning (DCFL), a novel Byzantine robust FL approach without any additional validation datasets. The main idea behind DCFL is to rigorously constrain the magnitude and direction of local updates through norms and signs. To achieve this, we propose a novel metric that can effectively distinguish malicious updates from benign updates in terms of direction, which can help the server eliminate malicious updates before final aggregation. Our experiments on three datasets demonstrate the effectiveness of DCFL in mitigating various popular Byzantine attacks. Remarkably, the accuracy of the global model learned in the adversarial setting is even close to that of FedAVG under no attack.

关键词： Federated Learning Byzantine Attack distributed Learning Robustness

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VXLAN - A practical approach to cloud computing scalability 22

VXLAN - A practical approach to cloud computing scalability

引用

22nd RoEduNet conference: networking in Education and Research, RoEduNet 2023

作者： Traistaru, Claudiu Universitatea din Craiova Departamentul de Calculatoare si Tehnologia Informatiei Craiova Romania

ISBN: (纸本)9798350393651

关键词： Cloud computing

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Research on Assessment System for Blockchain 22

Research on Assessment System for Blockchain

引用

IEEE 22nd international conference on Trust, Security and Privacy in computing and Communications (TrustCom) / BigDataSE conference / CSE conference / EUC conference / ISCI conference

作者： Pang, Weiwei Wei, Kai Zhang, Yihui Jiang, Chunyu Cheng, Yang Zhang, Qi Liu, Bin Zhang, Lifeng Liu, Tingting Wu, Yinqian China Acad Informat & Commun Technol Cloud Comp & Big Data Res Inst Beijing Peoples R China

ISBN: (纸本)9798350381993;9798350382006

Blockchain assessment is a key method to evaluate the quality of blockchain products. Blockchain assessment can effectively address the problem of application systems breaking down due to poor quality of blockchain product. A comprehensive and in-depth assessment is important for enhancing the quality of blockchain products. Blockchain assessment can detect bugs and weaknesses in blockchain products, then it can promote the product upgrades and evolution. Recently, research on blockchain assessment is fragmented with lacking systematization. Therefore, this paper proposes a blockchain assessment framework from both technical and application perspectives. This assessment framework includes the blockchain systems assessment, supporting service systems assessment, and business application systems assessment. For each class of blockchain products, corresponding assessment dimensions and criteria are put forward.

关键词： Blockchain distributed Ledger Technology Testing Assessment System Assessment Method Assessment Index

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Constellation Autonomy Modeling for Agile on-Orbit Communication and computing 22

Constellation Autonomy Modeling for Agile on-Orbit Communica...

引用

IEEE 22nd international conference on Trust, Security and Privacy in computing and Communications (TrustCom) / BigDataSE conference / CSE conference / EUC conference / ISCI conference

作者： Wang, Shoufeng Chen, Hua-Min Ouyang, Ye Li, Fan Chen, Xuan Guo, Jianchao Li, Yun Bian, Sen Wang, Xidong Ren, Zhidong AsiaInfo Technol China INC Beijing Peoples R China Beijing Univ Technol Beijing Peoples R China China Unicom Beijing Branch Beijing Peoples R China China Unicom Beijing Beijing Peoples R China

ISBN: (纸本)9798350381993;9798350382006

With the growing demand for mobile communication services and continuous advances in communication technologies, ubiquitous coverage and Internet of Everything have become basic capabilities required for SG, 6G and future networks. On-orbit autonomy enables satellites to perform tasks, process data, adjust parameters, update software, diagnose faults and recover functions autonomously in orbit. This enhances intelligence, efficiency and reduces operation costs and risks, especially for large low earth orbit (LEO) constellations. On-orbit autonomy is an important trend with great significance and value. Although some research and tests have been conducted as well as application requests and standardization gathered, modeling constellation autonomy from an on-orbit communication and computing perspective has received little attention. This paper reviews related standards, research, and tests as a foundation for modeling. Key technologies, networking and management are studied as major concerns in modeling. The Constellation Autonomy Modeling (CAM) framework for agile on-orbit communication and computing is proposed as a reference model and method for onorbit autonomy.

关键词： Internet of Everything Satellite-based network on-orbit communication On-orbit autonomy Constellation Autonomy

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A Secure Contactless Payment System with Bidirectional Blockchain and Blake Hash Function 22

A Secure Contactless Payment System with Bidirectional Block...

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IEEE 22nd international conference on Trust, Security and Privacy in computing and Communications (TrustCom) / BigDataSE conference / CSE conference / EUC conference / ISCI conference

作者： Rongali, Bhaskar Mohapatra, Satyajit Nayak, Sanjeet Kumar Indian Inst Informat Technol Design & Mfg Dept Comp Sci & Engn Chennai Tamil Nadu India

ISBN: (纸本)9798350381993;9798350382006

The growth of the Internet of Things (IoT) has led to an increased demand for contactless payment via IoT devices. However, machine-to-machine (M2M) payment in the IoT has been limited by poor centralized transaction management, given the distributed nature of the IoT, which results in massive communication overhead. Blockchain technology provides a promising solution for M2M payments in the IoT, but the current blockchain-based solutions for contactless payment lack security and scalability. To address this, a new customised Committee Member Auction mechanism has been proposed that is both safe and scalable for resource-constrained IoT devices. This mechanism also reduces the block mining time by integrating the BLAKE hashing algorithm, as opposed to SHA-256. The proposed framework was implemented utilizing an ESP32 microcontroller and RC522 RFID reader to record over 100 transactions each for SHA-256 and Blake. The results obtained validate that Blake hashing algorithm along with Bidirectional blockchain outperforms SHA-256 by reducing 30% of mining time. Besides improved mining efficiency, this scheme is also found to be resistant against Long Range Attack, Double Spend Attack and Eclipse attack. The proposed scheme thus offers a more secure and scalable option for contactless payments in IoT systems.

关键词： Bidirectional blockchain Internet of Things (IoT) Radio Frequency Identification (RFID) distributed Ledger Technology (DLT) Contactless Payment

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Differential Privacy Frequent Closed Itemset Mining over Data Stream 22

Differential Privacy Frequent Closed Itemset Mining over Dat...

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IEEE 22nd international conference on Trust, Security and Privacy in computing and Communications (TrustCom) / BigDataSE conference / CSE conference / EUC conference / ISCI conference

作者： Ma, Xuebin Guan, Shengyi Lang, Yanan Inner Mongolia Univ Inner Mongolia Key Lab Wireless Networking & Mobi Hohhot Peoples R China

ISBN: (纸本)9798350381993;9798350382006

As the most common technique for mining and analyzing massive data, frequent itemset mining is widely used in various scenarios. However, when the data contains sensitive information, it will bring serious privacy leakage risks to mining and publishing it directly. Therefore, how to efficiently mine frequent itemset without privacy disclosure is a hot issue at present. In data stream, because the data between adjacent timestamps possess certain correlation, which makes it easier to leak privacy for frequent itemset mining in the data stream, and considering that frequent itemset will have combinatorial explosion problem in data stream, frequent closed itemset mining in the data stream will be a better choice. In this paper, we propose a differential privacy algorithm for mining frequent closed itemset in data stream, which is referred to as DPES. The algorithm uses a vertical mining method in each sliding window, which can quickly obtain the frequent closed itemset contained in the current window. Besides, to improve the data utility, we also design an adaptive privacy budget allocation strategy by computing the difference to decide whether the current timestamp should publish a low-noise statistic or an approximate statistic. Finally, we demonstrate that the DPES algorithm satisfies e-difference privacy by privacy analysis, and the experimental results on several real datasets also show the effectiveness of the DPES algorithm.

关键词： Frequent closed itemset mining Differential privacy Data stream

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Zero Trust Score-based Network-level Access Control in Enterprise Networks 22

Zero Trust Score-based Network-level Access Control in Enter...

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IEEE 22nd international conference on Trust, Security and Privacy in computing and Communications (TrustCom) / BigDataSE conference / CSE conference / EUC conference / ISCI conference

作者： Bradatsch, Leonard Miroshkin, Oleksandr Trkulja, Natasa Kargl, Frank Ulm Univ Inst Distributed Syst Ulm Germany Ulm Univ KIZ Ulm Germany

ISBN: (纸本)9798350381993;9798350382006

Zero Trust security has recently gained attention in enterprise network security. One of its key ideas is making network-level access decisions based on trust scores. However, score-based access control in the enterprise domain still lacks essential elements in our understanding, and in this paper, we contribute with respect to three crucial aspects. First, we provide a comprehensive list of 29 trust attributes that can be used to calculate a trust score. By introducing a novel mathematical approach, we demonstrate how to quantify these attributes. Second, we describe a dynamic risk-based method to calculate the trust threshold the trust score must meet for permitted access. Third, we introduce a novel trust algorithm based on Subjective Logic that incorporates the first two contributions and offers fine-grained decision possibilities. We discuss how this algorithm shows a higher expressiveness compared to a lightweight additive trust algorithm. Performance-wise, a prototype of the Subjective Logic-based approach showed similar calculation times for making an access decision as the additive approach. In addition, the dynamic threshold calculation showed only 7% increased decision-making times compared to a static threshold.

关键词： Network security access control trust

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TGCN-DA: A Temporal Graph Convolutional Network with Data Augmentation for High Accuracy Insider Threat Detection 22

TGCN-DA: A Temporal Graph Convolutional Network with Data Au...

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IEEE 22nd international conference on Trust, Security and Privacy in computing and Communications (TrustCom) / BigDataSE conference / CSE conference / EUC conference / ISCI conference

作者： Li, Ximing Li, Linghui Li, Xiaoyong Cai, Binsi Li, Bingyu Beijing Univ Posts & Telecommun Key Lab Trustworthy Distributed Comp & Serv Minist Educ Beijing Peoples R China Beihang Univ Sch Cyber Sci & Technol Beijing Peoples R China

ISBN: (纸本)9798350381993;9798350382006

Insider threats present a formidable challenge to cybersecurity, as insiders possess the privileges and information necessary to execute diverse attacks. A comprehensive analysis of user behavior, including behavioral features, sequences, and inter-user relationships, is required for effective insider threat detection. However, few existing methods consider these features in an integrated manner, which could result in high false positives. To further improve the accuracy of insider threat detection, we propose a novel framework for insider threat detection based on a temporal graph convolutional network with data augmentation (referred to as TGCN-DA), which integrates the exploration of structural information among users and simultaneously captures the behavior temporal dependencies. In particular, we introduce an edge predictor to encode user structural information and strengthen intra-class edges among users based on the representation of users' behavior. Additionally, the GCN with temporal feature mechanism is leveraged to learn dynamic changes in users' behavior to capture behavior temporal dependence. Extensive experiments demonstrate that our proposed TGCN-DA outperforms other state-of-the-art methods and achieves higher accuracy in the task of insider threat detection.

关键词： Insider Threat Detection Anomaly Detection Graph Neural Network

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Secure Synchronized Spatio-Temporal Trajectory Similarity Search 22

Secure Synchronized Spatio-Temporal Trajectory Similarity Se...

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IEEE 22nd international conference on Trust, Security and Privacy in computing and Communications (TrustCom) / BigDataSE conference / CSE conference / EUC conference / ISCI conference

作者： Teng, Yiping Qi, Jiawei Liu, Lei Wang, Shiqing Xu, Li Fan, Chunlong Shenyang Aerosp Univ Sch Comp Large Scale Distributed Syst Lab Shenyang Liaoning Peoples R China

ISBN: (纸本)9798350381993;9798350382006

Attracting research interests and applications in academic and industrial community due to the proliferation of mobile devices, computation and processing services on spatio-temporal trajectory data has usually been outsourced to cloud platforms to save the costs of data storage, computation and management. To prevent the privacy leakage of trajectories from the direct data outsourcing, in this paper, we study the secure similarity search problem on spatio-temporal trajectories and present a secure synchronized spatio-temporal trajectory similarity search approach. In the approach, adopting the Matching Point-point distance Similarity (MPS) measurement, we first propose a Secure Matching Point-point Distance Similarity Computation (SMPSC) Protocol to support the secure similarity calculation on encrypted trajectories. To improve the computational performance, we further propose a Secure Grid Filtering (SGF) method by matching spatio-temporal grid codes to filter the dissimilar trajectories based on the distance threshold in MPS. At last, with SMPSC protocol and SGF method, we propose a Secure Synchronized Spatio-Temporal Trajectory similarity Search Processing (S-3 TS) method to retrieve similar trajectories based on MPS measurement. We theoretically analyze the computational complexity and security guarantees of the presented approach, and conduct extensive experiments on real and synthetic datasets to demonstrate its search performance.

关键词： Trajectory Secure computation protocol Synchronized Spatio-temporal similarity Privacy protection

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