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检索条件"任意字段=9th International Conference on Internet and Distributed Computing Systems, IDCS 2016"

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9th international conference on internet and distributed computing systems, idcs 2016

9th International Conference on Internet and Distributed Com...

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9th international conference on internet and distributed computing systems, idcs 2016

ISBN: (纸本)9783319459394

the proceedings contain 48 papers. the special focus in this conference is on Body Sensor Networks and Wearable Devices. the topics include: Continuous gesture recognition based on hidden Markov model;a new modeling method of photoplethysmography signal based on lognormal basis;a neuro-fuzzy system for classifying fatigue degree of wheelchair user;detecting novel class for sensor-based activity recognition using reject rule;monitoring swimming motion using body sensor networks;service model design and application of product design and component procurement for small and medium sized concrete mixer manufacturers based on cloud manufacturing;a novel access control model for cloud computing;agreement in epidemic information dissemination;cloud-based wheelchair assist system for mobility impaired individuals;energy management policies in distributed residential energy systems;a dynamic heuristic application mapping algorithm based on lookup tables;distributed real-time database for the intelligent community;predicting telecommunication customer churn using data mining techniques;self labeling online sequential extreme learning machine;a modified genetic algorithm for agricultural by-products logistics delivery route planning problem;multi-objective optimization of warehouse system based on the genetic algorithm;a constraint programming based method for stockyard management problem;business process reengineering of road passenger transport based on unified modeling language method;design of distributed logistics vehicle monitoring system with high load;logistics vehicle travel preference of interest points based on speed and accessory state;a partition berth allocation scheduler based on resource utilization and load balancing and a high-precise matching algorithm.

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Pairing Computations at the Edge and Cloud Servers to Improve Performance of Heterogeneous systems 9

Pairing Computations at the Edge and Cloud Servers to Improv...

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9th IEEE international conference on Fog and Mobile Edge computing (FMEC)

作者： Uddin, Md Raihan Abu Asaduzzaman Wichita State Univ Dept Elect & Comp Engn Wichita KS 67260 USA

ISBN: (纸本)9798350366495;9798350366488

High speed internet and advanced networking technology contribute to having large number of various edge devices in heterogeneous edge-cloud systems. In conventional cloud computing systems, all device data is processed in the centralized cloud servers. the growing number of devices, i.e., increasing amount of device data, poses a challenge to the cloud servers to process data in a time- and energy-efficient manner. Studies show promise to reduce execution time and energy consumption by introducing collaborative edge-cloud computing paradigm. In this work, we study collaborative edge-cloud computing by introducing a framework of pairing the computations at edge and cloud resources to minimize execution time and energy consumption. First, the cloud servers (CSs) are made about 90% utilized by adjusting the device data i. e., computed data. then, each edge server (ES) is optimized using 50% or less of the previously generated device data i.e., cloud computed data. Finally, computations (i.e., device data) are distributed among the ESs and CSs, and performance is assessed to obtain the optimal pairing of computations. A heterogeneous system with one CS, two ESs, 10 edges, and 30 devices of five different types is modeled and simulated using VisualSim. Experimental results show that the proposed method helps reduce execution time and energy consumption by 90% and 56%, respectively. the proposed framework holds a promise for enhancing the scalability of heterogeneous systems, an avenue we intend to explore in our upcoming venture.

关键词： Edge computing cloud computing execution time energy consumption heterogeneous systems utilization edge-cloud computing

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distributed Key Value Store for IoT Edge Devices 9

Distributed Key Value Store for IoT Edge Devices

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9th international conference on Computer Science and Engineering, UBMK 2024

作者： Aslantaş, Burak Pektaş, Elif Nurdan Baydere, Şebnem Yeditepe University Dept. of Computer Engineering Istanbul Turkey

ISBN: (纸本)9798350365887

the proliferation of the internet of things (IoT) requires efficient and scalable data management solutions at the edge of the network. Edge devices play a critical role in processing data and making real-time decisions, thereby minimizing latency and reducing the load on central servers. However, these devices face challenges such as scalability, latency, and fault tolerance. this paper proposes a distributed key-value store specifically designed for IoT edge devices to address these challenges. By integrating distributed key-value stores with IoT edge devices, we aim to enhance data access and management efficiency. Our approach includes the development of optimal methodologies and architectural standards for this integration, addressing key issues such as data consistency, fault tolerance, and low latency. through prototype creation and testing, we demonstrate significant improvements in data processing speed and system reliability. the ultimate objective of this research is to advance edge computing for IoT, fostering more robust, scalable, and efficient systems. © 2024 IEEE.

关键词： Data consistency

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VPFL: A Verifiable Property Federated Learning Framework Against Invisible Attacks in distributed IoT 25

VPFL: A Verifiable Property Federated Learning Framework Aga...

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25th IEEE international conferences on High Performance computing and Communications, 9th international conference on Data Science and systems, 21st IEEE international conference on Smart City and 9th IEEE international conference on Dependability in Sensor, Cloud and Big Data systems and Applications, HPCC/DSS/SmartCity/DependSys 2023

作者： Wu, Yuanxiang Cheng, Hang Guan, Lin Liu, Peihao Chen, Fei Wang, Meiqing College of Computer Science and Big Data Fuzhou University Fuzhou China

ISBN: (纸本)9798350330014

the development of Federated Learning (FL) offers an efficient Machine Learning (ML) approach with privacy protection to solve the data island issue in distributed internet of things (IoT). However, existing FL frameworks still suffer from invisible attacks in IoT environments, such as free-rider attacks, backdoor attacks, and model theft. In this paper, we propose a Verifiable Property Federated Learning (VPFL) framework to overcome the above invisible attacks. We present a black-box watermarking task distribution mechanism to prevent free-rider attacks by verifying the property of local models. Our adversarial fine-tuning embedding technique can not only eliminate backdoors in global models, but also simultaneously embed white-box watermarks into model parameters to prevent model theft. Comprehensive experimental evaluations demonstrate that our framework outperforms state-of-the-art schemes in terms of security and feasibility against invisible attacks. © 2023 IEEE.

关键词： internet of things

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Resource Management Technology in Smart Grids enabled by Regional Energy internet 9

Resource Management Technology in Smart Grids enabled by Reg...

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9th international conference on Computer and Communications, ICCC 2023

作者： Jiang, Jing Xin, Peizhe Xiao, Zhihong Ji, Yuejie State Grid Economic and Technological Research Institute Co. Ltd. Beijing China

ISBN: (纸本)9798350317251

there are numerous distributed new energy and diversified loads in the regional energy internet. New business demands such as virtual power plant group dispatching and control, demand side response, and energy big data centers are constantly emerging, putting higher requirements on the reliability, security, and processing capacity of communication networks. As the core of the regional energy internet, the power grid's advanced communication and computing resources management is an effective way to meet the needs of the new business. In order to meet the differentiated requirements of services and security isolation, the network slicing technology is applied to the power grid. In this paper, we proposed a computing and bandwidth resource management scheme in smart grids enabled by regional energy internet to reduce energy consumption and network delay. We modeled the joint allocation of bandwidth and computing resources of network slicing as a convex optimization problem, aiming to minimize the weighted sum of system delay consumption and computational energy consumption. the Lagrange multiplier iterative optimization method is used to solve the optimal allocation scheme of computing resources and bandwidth resources. the simulation results show that the proposed solution can meet the differentiated delay requirements of the service and effectively reduce the delay consumption and energy consumption of the system. © 2023 IEEE.

关键词： Resource allocation

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ImageFed: Practical Privacy Preserving Intrusion Detection System for In-Vehicle CAN Bus Protocol 9

ImageFed: Practical Privacy Preserving Intrusion Detection S...

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9th IEEE international conference on Big Data Security on Cloud (BigDataSecurity) / 9th IEEE international conference on High Performance and Smart computing (IEEE HPSC) / 9th IEEE international conference on Intelligent Data and Security (IEEE IDS)

作者： Taslimasa, Hamideh Dadkhah, Sajjad Pinto Neto, Euclides Carlos Xiong, Pulei Iqbal, Shahrear Ray, Suprio Ghorbani, Ali A. UNB Fac Comp Sci Fredericton NB Canada Natl Res Council Canada Cybersecur Ottawa ON Canada Natl Res Council Fredericton New Brunswick NJ USA

ISBN: (纸本)9798350312935

In vehiclenetworks handle the communication among Electronic Control Units (ECUs) and sensors with a serial protocol called Controller Area Network (CAN), which takes advantage of a broadcast mechanism liar transferring data. However, the lack of authentication and encryption in CAN bus might lead to the vehicle being compromised by an untrusted agent. Meanwhile, AI -based Network Intrusion Detection systems (NIDS) introduced in the literature require a centralized cloud server to undertake the resource -consuming task of processing network data, which also occupies the bandwidth while transferring data from vehicles to the remote server. Moreover, many car manufacturers avoid sharing information with the remote server to prevent further exposure of vehicles' private data. We utilize Federated Learning (FL) as a suitable solution to previous challenges, where data is trained locally on the vehicle side, and partially trained models are sent to the server. Nevertheless, private training inside the vehicle requires low memory utilization while achieving low latency and high performance compared to Centralized Learning (CL) approaches. In this study, we propose a practical privacy-preserving IDS (ImageFed) approach by employing federated Convolutional Neural Network (CNN). To evaluate the robustness of ImageFed in real -world applications, we investigate two scenarios that may cause performance degradation in FL, such as non-independent and identically distributed (non-iid) clients and scarcity of training data during the process. the resulting analysis proves the robustness of ImageFed while delivering an average 99.54% fl -score, 99.87% accuracy, and low detection latency.

关键词： Terms internet of Vehicles (IoV) Security Intrusion Detection systems (IDS) Federated Learning (FL) I)eep Learning (DL) Convolutional Neural Networks (CNN)

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Design of A distributed Intrusion Detection System for Streaming Data in IoT Environments 9

Design of A Distributed Intrusion Detection System for Strea...

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9th international conference on Optimization and Applications, ICOA 2023

作者： Atbib, Souad Saadi, Chaimae Chaoui, Habiba Kenitra Morocco Salé Morocco

ISBN: (纸本)9798350312546

Nowadays, IoT devices produce a large amount of streaming data that requires real-time analysis to ensure its security. Traditional security solutions are no longer sufficient to counter recent IoT attacks and to analyze the huge amount of data. In recent years, intrusion detection systems have been the subject of much research to evaluate their performance in detecting intrusions in IoT, especially since they are based on Artificial Intelligence and allow real-time intrusion detection. In this paper, we present the architecture of an intrusion detection system for streaming data analysis. We implemented our IDS in a Fog computing architecture and developed using Apache Spark. We applied several machine-learning algorithms to the NF-ToN-IoT-v2 dataset, and the results were promising. © 2023 IEEE.

关键词： internet of things

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Identification of Important Features at Different IoT layers for Dynamic Attack Detection 9

Identification of Important Features at Different IoT layers...

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9th IEEE international conference on Big Data Security on Cloud, 9th IEEE international conference on High Performance and Smart computing and 9th IEEE international conference on Intelligent Data and Security, BigDataSecurity-HPSC-IDS 2023

作者： Haque, Safwana El-Moussa, Fadi Komninos, Nikos Muttukrishnan, Rajarajan City University of London London United Kingdom Bt Group Plc Ipswich United Kingdom

ISBN: (纸本)9798350312935

With the growth of internet of things (IoT), it is essential to devise mitigation techniques to curb related attacks to prevent losses and network disruptions. One popular method of achieving this is with the use of intrusion detection systems (IDSs) that incorporate machine learning (ML) techniques to automatically detect attacks and anomalies. the main purpose of this study was to compare attacks within the same layer (e.g. network layer) or across two different layers (e.g. network and application layers) in order to determine features that are common or unique to each attack and to further determine if attacks belonging to different layers have distinguishing features. To achieve this outcome, the Edge-IIoT dataset was taken into consideration, a recent IoT-related dataset with a varied collection of attack signatures collected using IoT and IIoT (Industrial IoT) sensors. this study mainly focuses on home IoT networks and therefore network traffic flows primarily from five sensors that can be commonly found in a home IoT network were selected from the Edge-IIoT dataset for the ML processes. For the scope of this study, transmission control protocol (TCP) and user datagram protocol (UDP) distributed denial of service (DDoS) attacks were selected as the network layer attacks, while structured query language (SQL) injection and cross-site scripting (XSS) attacks were selected as the application layer attacks. the study identified three features as unique to application layer attacks, four to the XSS attack only, and three to the UDP DDoS attack only. this study also showed that the Edge-IIoT dataset can be reduced to 20 features for optimal ML results (Naïve Bayes [NB] improved to 89.2%, J48 to 94.7% and k-nearest neighbour [k-NN] to 91.7%) using the gain ratio feature selection technique. © 2023 IEEE.

关键词： internet of things

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2022 9th international conference on internet of things, systems, Management and Security, IOTSMS 2022

2022 9th International Conference on Internet of Things, Sys...

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9th international conference on internet of things, systems, Management and Security, IOTSMS 2022

ISBN: (纸本)9798350320459

the proceedings contain 35 papers. the topics discussed include: robust semantic communication driven by knowledge graph;impact of big data analytics and managerial support on CRM: exploring mediating role of marketing analytics;an improved and stable routing protocol for cognitive radio based IoT networks;a smart approach using multi–agent system for big data security;integrating trusted computing mechanisms with trust models to achieve zero trust principles;distributed ledger technologies for managing heterogenous computing systems at the edge;secure sparse gradient aggregation in distributed architectures;and internet of things (IoT): a way to expedite production and service performance empirical, evidence from textile industry of United Arab Emirates (UAE).

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MODGF: Multi-Objective Optimization Approach for Directed Greybox Fuzzing 25

MODGF: Multi-Objective Optimization Approach for Directed Gr...

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作者： Zuo, Xueying Song, Yubo Mao, Run Qin, Zhongyuan Sun, Xin School of Cyberspace Security Southeast University Nanjing China Internet Technology Center State Grid Zhejiang Electric Power Co. Ltd. Research Institute Hangzhou China

ISBN: (纸本)9798350330014

Directed Greybox Fuzzing employs code analysis to predict critical paths or potential vulnerabilities. Multi-objective optimization is a challenge to Directed Greybox Fuzzing. Current approaches employ evolutionary algorithms which may trap in local optima during mutation operation procedure. In this paper, we propose MODGF, a Directed Greybox Fuzzing method based on the elitist multi-objective evolutionary algorithm. the key insight of our approach is to get a more diverse distribution along the Pareto front during evolutionary iterations, which speed the convergence of the test cases. In MODGF, we use function distance, basic block distance, and seed distance as objective functions, and calculate distance metrics of the nearest neighbor solutions to infer the distribution density of solutions. We further introduce convergence measure and diversity measure as fitness values to ensure that the generated test cases are more uniformly distributed with more vulnerability types. In comparison to AFL and AFL-GO, the experiment reveals that MODGF exhibits significant performance enhancements in terms of convergence time, target function coverage, and vulnerability triggering efficiency. © 2023 IEEE.

关键词： Multiobjective optimization

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