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4th international conference on networking, Intelligent Systems and Security, NISS 2021

4th International Conference on Networking, Intelligent Syst...

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4th international conference on networking, Intelligent Systems and Security, NISS 2021

ISBN: (纸本)9789811636363

The proceedings contain 62 papers. The special focus in this conference is on networking, Intelligent Systems and Security. The topics include: Channel Estimation in Massive MIMO Systems for Spatially Correlated Channels with Pilot Contamination;On Channel Estimation of Uplink TDD Massive MIMO Systems Through Different Pilot Structures;NarrowBand-IoT and eMTC Towards Massive MTC: Performance Evaluation and Comparison for 5G mMTC;integrating Business Intelligence with Cloud computing: State of the Art and Fundamental Concepts;distributed Architecture for Interoperable Signaling Interlocking;A New Design of an Ant Colony Optimization (ACO) Algorithm for Optimization of Ad Hoc Network;real-Time distributed Pipeline Architecture for Pedestrians’ Trajectories;Reconfiguration of the Radial Distribution for Multiple DGs by Using an Improved PSO;on the Performance of 5G Narrow-Band Internet of Things for Industrial Applications;The Evolution of the Traffic Congestion Prediction and AI Application;a Novel Design of Frequency Reconfigurable Antenna for 5G Mobile Phones;a Real-Time Smart Agent for Network Traffic Profiling and Intrusion Detection Based on Combined Machine Learning Algorithms;privacy Threat Modeling in Personalized Search Systems;enhanced Intrusion Detection System Based on AutoEncoder Network and Support Vector Machine;comparative Study of Keccak and Blake2 Hash Functions;cryptography Over the Twisted Hessian Curve Ha,d3;Method for Designing Countermeasures for Crypto-Ransomware Based on the NIST CSF;comparative Study Between Network Layer Attacks in Mobile Ad Hoc Networks;security of Deep Learning Models in 5G Networks: Proposition of Security Assessment Process;effects of Jamming Attack on the Internet of Things;image-Based Malware Classification Using Multi-layer Perceptron.

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Multi-Agent Deep Reinforcement Learning Based Consistency Control in Train Platoon Systems 23

Multi-Agent Deep Reinforcement Learning Based Consistency Co...

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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, Xinran Nie, Gaofeng Ni, Wanli Tian, Hui Beijing University of Posts and Telecommunications State Key Laboratory of Networking and Switching Technology Beijing100876 China

ISBN: (纸本)9781665494571

The train platoon (TP) in the urban rail transit (URT) system has been regarded as a promising solution to improve the transportation capacity between metro stations. Moreover, TP has a great potential to effectively adapt to the high dynamic flow of passengers during major events or festivals. Unfortunately, the promotion of TP is challenged due to the auto-block system in the traditional URT system as well as the complicated and dynamic operation environment. This paper investigates a cooperative control method to realize the consistency of TP in the premise of anti-interference. The multi-agent system of TP based on leader-follower typology is designed to achieve the operation goals in terms of distance and velocity. A novel distributed multi-agent deep reinforcement learning algorithm is proposed to obtain the optimal control strategy, where the artificial potential field is structured to provide prior knowledge of the environment. Compared with the conventional Q-learning based methods, simulation results validate that the proposed algorithm not only benefits the achievement of the operation goal, but also prevents the unfavorable interference from the external system. © 2021 IEEE.

关键词： Rails Q-learning Simulation Optimal control Transportation Bidirectional control Security

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ProgrammabilityMedic: Predictable Path Programmability Recovery under Multiple Controller Failures in SD-WANs 41

ProgrammabilityMedic: Predictable Path Programmability Recov...

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41st IEEE international conference on distributed computing Systems (ICDCS)

作者： Dou, Songshi Guo, Zehua Xia, Yuanqing Beijing Inst Technol Beijing Peoples R China

ISBN: (纸本)9781665445139

Software-Defined networking (SDN) promises good network performance in Wide Area Networks (WANs) with the logically centralized control using physically distributed controllers. In Software-Defined WANs (SD-WANs), maintaining path programmability, which enables flexible path change on flows, is crucial for maintaining network performance under traffic variation. However, when controllers fail, existing solutions are essentially coarse-grained switch-controller mapping solutions and only recover the path programmability of a limited number of offline flows, which traverse offline switches controlled by failed controllers. In this paper, we propose ProgrammabilityMedic (PM) to provide predictable path programmability recovery under controller failures in SD-WANs. The key idea of PM is to approximately realize flow-controller mappings using hybrid SDN/legacy routing supported by high-end commercial SDN switches. Using the hybrid routing, we can recover programmability by fine-grainedly selecting a routing mode for each offline flow at each offline switch to fit the given control resource from active controllers. Thus, PM can effectively map offline switches to active controllers to improve recovery efficiency. Simulation results show that PM outperforms existing switch-level solutions by maintaining balanced programmability and increasing the total programmability of recovered offline flows up to 315% under two controller failures and 340% under three controller failures.

关键词： Wide area networks Simulation conferences Switches Control systems Routing distributed computing

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Customizable DDoS Attack Data Generation in SDN Environments for Enhanced Machine Learning Detection Models

Customizable DDoS Attack Data Generation in SDN Environments...

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Advanced Research in computing (ICARC), international conference on

作者： Nadeera Gayantha Chathura Rajapakse Janaka Senanayake Dept. of Industrial Management University of Kelaniya Sri Lanka School of Computing Engineering and Technology Robert Gordon University Aberdeen United Kingdom

ISBN: (数字)9798331530983

ISBN: (纸本)9798331530990

distributed Denial of Service (DDoS) attacks are a critical threat to the security and reliability of Software-Defined networking (SDN) environments. Existing datasets for training machine learning (ML) models, such as KDDCup ’99 and CICIDS 2017, are either outdated or fail to capture SDN-specific characteristics, limiting their effectiveness in detecting modern DDoS attacks. This paper proposes a framework for generating a comprehensive, SDN-specific dataset using a virtual environment that integrates Mininet, the Ryu controller, and Python-based automation. The dataset incorporates advanced flow-level metrics, including SYN counts, queue lengths, and real-time traffic dynamics, reflecting contemporary attack scenarios such as ICMP floods, TCP SYN floods, and UDP floods. By addressing the limitations of traditional datasets, this custom dataset enhances ML model training for DDoS detection in SDN environments, providing improved accuracy and adaptability. Contributions include a scalable SDN-based dataset generation framework, enriched feature sets for ML training, and a comprehensive approach to capturing both legitimate and malicious traffic dynamics. This study highlights the potential of SDN programmability in advancing security research and offers a robust tool for the development of reliable DDoS detection mechanisms.

关键词： Training Adaptation models Accuracy Virtual environments Denial-of-service attack Vectors Robustness Real-time systems Computer crime Software defined networking

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Proceedings of PAW-ATM 2020: 3rd Annual Parallel Applications Workshop, Alternatives to MPI+X, Held in conjunction with SC 2020: The international conference for High Performance computing, networking, Storage and Analysis

Proceedings of PAW-ATM 2020: 3rd Annual Parallel Application...

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3rd IEEE/ACM Annual Parallel Applications Workshop, Alternatives to MPI+X, PAW-ATM 2020

ISBN: (纸本)9781728154503

The proceedings contain 6 papers. The topics discussed include: hedgehog: understandable scheduler-free heterogeneous asynchronous multithreaded data-flow graphs;TaskTorrent: a lightweight distributed task-based runtime system in C++;evaluation of multiple HPC parallelization frameworks in a shallow water proxy application with multi-rate local time stepping;an implicitly parallel meshfree solver in regent;HOOVER: leveraging OpenSHMEM for high performance, flexible streaming graph applications;and exploring hybrid MPI+Kokkos tasks programming model.

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Fed-MoE: Efficient Federated Learning for Mixture-of-Experts Models via Empirical Pruning 25th

Fed-MoE: Efficient Federated Learning for Mixture-of-Expert...

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25th international conference on Parallel and distributed computing, Applications and Technologies, PDCAT 2024

作者： Zou, Yifei Qi, Senmao Yuan, Yuan Wang, Dawei Shen, Shikun Wu, Libing Guo, Shaoyong Yu, Dongxiao School of Computer Science and Technology Shandong University Qingdao China School of Cyber Science and Engineering Wuhan University Wuhan China State Key Laboratory of Networking and Switching Technology Beijing University of Posts and Telecommunications Beijing China

ISBN: (纸本)9789819642069

In recent years, the combination of Federated Learning (FL) framework and Mixture of Experts (MoE) architecture has shown promise for large-scale model pre-training while preserving data privacy. However, the significant computational and communication requirement of MoE-based models presents substantial challenges for FL systems. To address this, we propose Fed-MoE, an efficient federated learning framework designed for MoE models through empirical pruning. Fed-MoE reduces computational overhead by pruning multiple experts at the client side while retaining general components, thus converting the model into dense sub-models. These sub-models are locally trained and later aggregated by the parameter server into a sparse MoE model. Our method optimizes the use of sparse activation in MoE models and reduces resource consumption without compromising performance. Extensive experimental results demonstrate that Fed-MoE significantly improves the efficiency of FL without compromising model performance, offering an effective solution for deploying large-scale MoE models in federated learning environments. © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2025.

关键词： Federated learning

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Service Discovery and Provisioning in Social Digital Twin Networks: a Name-based Approach

Service Discovery and Provisioning in Social Digital Twin Ne...

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IEEE international conference on Wireless and Mobile computing, networking And Communications (WiMob)

作者： Marica Amadeo Giuseppe Ruggeri Claudio Marche Michele Nitti university mediterranea of reggio calabria italy university of cagliari italy

ISBN: (数字)9798350387445

ISBN: (纸本)9798350387452

Digital Twin (DT) technology is expected to cover a crucial role in a variety of 6G application scenarios, including smart automotive, smart home and smart city. By leveraging advanced Artificial Intelligence (AI) modules alongside cutting-edge communication and networking architectures, DTs will be able to develop cognitive and social skills and build relationships with each other, thus facilitating the sharing of services and experience. However, in current implementations, DTs typically engage with their physical counterpart only, for predictive main-tenance and optimization, while protocols for inter-twin commu-nications and service discovery are still unexplored. In this paper, we focus on service discovery and provisioning in DT networks hosted at the network edge. In our design, the Social Internet of Things (SIoT) notion is applied to build social networks among DTs and, in parallel, name-based primitives, according to the Information Centric networking (ICN) paradigm, are consid-ered to support inter-twin interactions. Two distributed name-based service discovery mechanisms are envisioned: a social-driven scheme, leveraging friendship and similarities among DTs' names, and a network-driven scheme, leveraging the ICN forwarding fabric only. A performance evaluation shows the benefits of the conceived solution in terms of reduced discovery latency compared to legacy centralized approaches.

关键词： Wireless communication Performance evaluation Protocols Social networking (online) Smart cities Smart homes Social Internet of Things Digital twins Artificial intelligence Optimization

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Arbitrary Pattern Formation on Infinite Regular Tessellation Graphs 21

Arbitrary Pattern Formation on Infinite Regular Tessellation...

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22nd international conference on distributed computing and networking (ICDCN)

作者： Cicerone, Serafino Di Fonso, Alessia Di Stefano, Gabriele Navarra, Alfredo Univ Aquila Dip Ingn & Sci Informaz & Matemat Laquila Italy Univ Perugia Dip Matemat & Informat Perugia Italy

ISBN: (纸本)9781450389334

Given a set R of robots, each one located at a different vertex of an infinite regular tessellation graph, we aim to explore the Arbitrary Pattern Formation (APF) problem. Given a multiset F of grid vertices such that vertical bar R vertical bar = vertical bar F vertical bar, APF asks for a distributed algorithm that moves robots so as to reach a configuration similar to F. Similarity means that robots must be disposed as F regardless of translations, rotations, reflections. So far, as possible discretization of the Euclidean plane only the standard square grid has been considered in the context of the classical Look-Compute-Move model. However, it is natural to consider the other regular tessellation graphs, that are triangular and hexagonal grids. For any regular tessellation graph, we provide a resolution algorithm for APF when the initial configuration is asymmetric.

关键词： distributed algorithms Mobile and collaborative systems Asynchrony Self-organization Pattern formation Graphs

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Secure Conflict-free Replicated Data Types 21

Secure Conflict-free Replicated Data Types

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22nd international conference on distributed computing and networking (ICDCN)

作者： Barbosa, Manuel Ferreira, Bernardo Marques, Joao Portela, Bernardo Preguica, Nuno DCC FC UP Porto Portugal INESC TEC Porto Portugal DI FC UL Lisbon Portugal LASIGE Lisbon Portugal DI FCT UNL Lisbon Portugal NOVA LINCS Costa Da Caparica Portugal

ISBN: (纸本)9781450389334

Conflict-free Replicated Data Types (CRDTs) are abstract data types that support developers when designing and reasoning about distributed systems with eventual consistency guarantees. In their core they solve the problem of how to deal with concurrent operations, in a way that is transparent for developers. However in the real world, distributed systems also suffer from other relevant problems, including security and privacy issues and especially when participants can be untrusted. In this paper we present new privacy-preserving CRDT protocols that can be used to help secure distributed cloud-backed applications, including NoSQL geo-replicated databases. Our proposals are based on standard CRDTs, such as sets and counters, augmented with cryptographic mechanisms that allow their operations to be performed on encrypted data. We accompany our proposals with formal security proofs and implement and integrate them in An-tidoteDB, a geo-replicated NoSQL database that leverages CRDTs for its operations. Experimental evaluations based on the Danish Shared Medication Record dataset (FMK) exhibit the tradeoffs that our different proposals make and show that they are ready to be used in practical applications.

关键词： distributed Systems Cloud computing Security

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distributed Learning for Application Placement at the Edge Minimizing Active Nodes

Distributed Learning for Application Placement at the Edge M...

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6G networking (6GNet), international conference on

作者： Claudia Torres-Pérez Estefanía Coronado Cristina Cervelló-Pastor Juan Sebastian Camargo Muhammad Shuaib Siddiqui i2CAT Foundation Barcelona Spain Universidad de Castilla-La Mancha Albacete Spain Universitat Politécnica de Catalunya Castelldefels Barcelona Spain

The main goal of application placement in Multi-Access Edge computing (MEC) is to map their requirements to the infrastructure for desired Service Level Agreement (SLA). In highly distributed infrastructures in beyond 5G and 6G networks, meeting this need and minimizing energy use are crucial. Fo-cusing solely on meeting SLA requirements can lead to resource fragmentation and reduced energy efficiency, as nodes utilize only a small portion of their resources. Furthermore, when multiple orchestrators govern MEC nodes, achieving optimal efficiency becomes a more complex challenge. This paper addresses the application placement problem by employing distributed deep reinforcement learning to efficiently minimize the overall cost of active MEC nodes in a distributed scenario involving multiple MEC systems. Our technique reduces the number of active nodes maintaining an average accuracy of up to 98%, meets SLA requirements, and is scalable for hosting several MEC nodes.

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