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检索条件"丛书名=Computer Communications and Networks"

共 4934 条记录，以下是61-70 订阅

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In-network Reinforcement Learning for Attack Mitigation using Programmable Data Plane in SDN 33

In-network Reinforcement Learning for Attack Mitigation usin...

引用

33rd IEEE International Conference on computer communications and networks (ICCCN)

作者： Ganesan, Aparna Sarac, Kamil Univ Texas Dallas Dept Comp Sci Richardson TX 75080 USA

ISBN: (纸本)9798350348439;9798350384611

The development of reinforcement learning (RL) algorithms has created a paradigm where the agents are trained to learn directly by observing the environment and learning policies to perform tasks autonomously. In the case of network environments, these agents can control and monitor the traffic as well as help preserve the confidentiality, integrity, and availability of resources and services in the network. In the case of software-defined networks (SDN), the centralized controller in the control plane has become the single point of failure for the entire network. Reactive routing in SDNs makes such networks vulnerable to denial-of-service (DoS) attacks that aim to overwhelm switch memory and the control channel between SDN switches and controllers. One potential solution to cope with such attacks is to use an intelligent mechanism to detect and block them with minimal performance overhead for the controller and control channel. In this work, we investigate the practicality and effectiveness of a reinforcement learning (RL) approach to cope with DoS attacks in SDN networks that utilize programmable switches. Assuming the existence of a reliable reward function, we demonstrate that an RL-based approach can successfully adapt to the changing nature of attack traffic to detect and mitigate attacks without overwhelming switch memory and the control channel in SDN.

关键词： Reinforcement learning software-defined networks programmable data planes SDN security

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Decentralized Multi-agent Reinforcement Learning for Large-scale Mobile Wireless Sensor Network Control Using Mean Field Games 33

Decentralized Multi-agent Reinforcement Learning for Large-s...

引用

33rd IEEE International Conference on computer communications and networks (ICCCN)

作者： Zhou, Zejian Qian, Lijun Xu, Hao Univ Wyoming Dept EECS Laramie WY 82071 USA Prairie View A&M Univ Credit Ctr Prairie View TX 77446 USA Univ Nevada Dept EBME Reno NV 89557 USA

ISBN: (纸本)9798350348439;9798350384611

In this paper, the real-time optimal transmission power control problem is investigated for large-scale mobile wireless sensor networks (MWSN). Controlling large-scale MWSN has two novel challenges, i.e., 1) increasing navigation complexity due to a large number of mobile wireless sensors, and 2) limited energy prohibits peer-to-peer communication between large-scale mobile sensors. To overcome these challenges, the novel mean field game theory is adopted and integrated along with the emerging decentralized reinforcement learning technique. Specifically, the optimal transmission control problem and the optimal navigation problem are formulated as mean field games with two objectives. Then, a novel Actor-Critic-Mass multi-agent reinforcement learning algorithm is developed to learn the decentralized optimal transmission power control and motion control. To learn the decentralized optimal navigation and transmission power control policies, the coupled Hamiltonian-Jacobian-Bellman (HJB) and Fokker-Planck-Kolmogorov (FPK) equations are derived in mean field game formulation. The learned decentralized policies can be guaranteed to converge close to the optimal value, i.e., the Nash Equilibrium, even with large-scale MWSNs in uncertain environments. Finally, the numerical simulations have been provided to demonstrate the effectiveness of the proposed design.

关键词： Mobile wireless sensor network multi-agent reinforcement learning mean field games

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Data Security Approaches to Support Open Science on High-Performance Computing Systems 33

Data Security Approaches to Support Open Science on High-Per...

引用

33rd IEEE International Conference on computer communications and networks (ICCCN)

作者： Chowdhury, Md Kamal Hossain Bangalore, Purushotham, V Univ Alabama Coll Engn Dept Comp Sci Tuscaloosa AL 35487 USA

ISBN: (纸本)9798350348439;9798350384611

Research data must be shared with other researchers for further collaboration to support open science. Sharing data with other researchers will save time and money instead of replicating the same research and also enables collaboration. However, open science data are vulnerable to attacks, and attackers can exploit the system, which could lead to harmful consequences from data sharing. This paper explores open science data security challenges and prevention techniques. First, we will review state-of-the-art algorithms and techniques that have been proposed to secure data. Second, we show the difference between generic data security and HPC security and compare the strengths and weaknesses of these approaches. Finally, we suggest some options to address these problems.

关键词： Open Science Data Security HPC systems

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The Remote Networking Lab Manager 33

The Remote Networking Lab Manager

引用

33rd IEEE International Conference on computer communications and networks (ICCCN)

作者： Michalski, Marek Poznan Univ Tech Fac Comp & Telecommun Piotrowo 3 PL-60965 Poznan Poland

ISBN: (数字)9798350384611

ISBN: (纸本)9798350348439;9798350384611

This paper presents an application - a system of tools for remotely managing a computer networking laboratory. This laboratory contains virtual and physical resources that are interconnected, creating a flexible topology that can be easily modified, primarily through VLAN assignments. Virtual resources (VMs) from multiple servers and physical devices from various rooms are connected using VLANs/VPLS/VPN technologies, forming a unified and flexible laboratory environment. To enhance user-friendliness, a range of interesting mechanisms and tools have been incorporated.

关键词： Performance evaluation Program processors Network topology Laboratories Topology Virtual private networks Servers

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A Novel Neural networks-based Framework for APT Detection in Networked Autonomous Systems 33

A Novel Neural Networks-based Framework for APT Detection in...

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33rd IEEE International Conference on computer communications and networks (ICCCN)

作者： El Alami, Hassan Rawat, Danda B. Howard Univ Washington DC 20059 USA

ISBN: (纸本)9798350348439;9798350384611

Networked Autonomous Systems (AS) represent a networking paradigm where nodes autonomously make decisions, operating independently or collaboratively to achieve network goals. Unlike traditional networks, networked AS emphasizes decentralized control, enabling nodes to adapt dynamically without relying heavily on centralized controllers. This autonomy promotes agility, scalability, and resilience, making networked AS applicable to various domains like IoT and wireless communications. However, this architecture is vulnerable to various threats, including Advanced Persistent Threat (APT), which exploits vulnerabilities leading to resource depletion and hindering legitimate network operations. APTs, particularly prevalent in IoT-enabled networked AS, pose a significant risk by overwhelming network resources and disrupting services. To address these challenges, this paper introduces a new neural network-based framework tailored to detecting APT in networked AS environments. The proposed framework leverages generative adversarial networks and Deep Learning (DL) to accurately identify malicious activities in networked AS. We evaluated our framework using the newly released dataset TON IoT, which contains a comprehensive variety of APT and addresses gaps in existing datasets in the context of AS. We obtained a significant improvement in APT detection by comparing DL techniques such as Conventional Neural networks, Gated Recurrent Units, Long Short-Term Memory, Multilayer Perceptron (MLP), and combining MLP with Auto-Encoder (MLP+AE). The proposed framework demonstrates important accuracy and robustness by analyzing the selected DL techniques' performance as influenced by variations in the training set size.

关键词： APT detection cybersecurity artificial intelligence deep learning networking autonomous systems

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Detecting IP Prefix Mismatches on SDN Data Plane 33

Detecting IP Prefix Mismatches on SDN Data Plane

引用

33rd IEEE International Conference on computer communications and networks (ICCCN)

作者： Tung, Shu-Po Lin, Yu-Min Chang, Keng-Lun Hsiao, Hsu-Chun Kim, Tiffany Hyun-Jin Natl Taiwan Univ Taipei Taiwan Acad Sinica Taipei Taiwan HRL Labs Malibu CA USA

ISBN: (纸本)9798350348439;9798350384611

Software-defined networking (SDN) enables centralized network management by separating the control and data plane. However, the actual packet behavior on data-plane may deviate from the control-plane rules sometimes. Many probe-based tools have been developed to verify the data plane's correctness and detect forwarding errors by sending test packets. However, they all assume simple fault models, such as incorrect action fields in the forwarding rules. To address this gap, this paper identifies a new class of error affecting the IP match field called IP prefix mismatch, which previous tools failed to identify thoroughly. We categorize IP prefix mismatches into prefix shrinkage and prefix expansion. We then present novel test packet generation algorithms to construct test packets designed to exhibit different behaviors depending on the presence of these errors. Using these algorithms, we develop a system that guarantees the discovery of at least one prefix mismatch in every detection round, even in the worst-case scenario. Several experiments were conducted to compare our system to a conventional probe-based method that sends a single test packet per rule. The results demonstrate that our system achieves perfect fault coverage within a minimal number of detection rounds. Even when faced with a network containing 50% erroneous rules, it successfully identifies all prefix mismatches within an average of only two detection rounds. In contrast, the conventional method fails to uncover all errors, even after spending on additional detection rounds.

关键词： Software-defined network Data plane security Test packet generation Match field error IP prefix

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CasePad: Privacy-preserving Finger Activity Sensing via Passive Acoustic Signals Enhanced by Mini-Structures in Smartphone Cases 33

CasePad: Privacy-preserving Finger Activity Sensing via Pass...

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33rd IEEE International Conference on computer communications and networks (ICCCN)

作者： Ye, Zhengkun Wang, Yan Chen, Yingying Temple Univ Philadelphia PA 19122 USA Rutgers State Univ New Brunswick NJ 08901 USA

ISBN: (纸本)9798350348439;9798350384611

Smartphones have emerged as indispensable devices, seamlessly integrating into our daily lives. However, traditional smartphone interfaces, primarily relying on touchscreens, raise privacy concerns and are susceptible to privacy leakages. We thus propose CasePad, an innovative system that leverages low-cost smartphone cases to achieve fine-grained finger activity sensing while preserving users' privacy. Toward this end, we devise a passive system to exploit acoustic signals generated from finger interactions on the back of the smartphone case. Our novel approach leverages acoustic mini-structures embedded within the smartphone case to regulate the acoustic signals from finger interactions and enhance their diversity. We further develop a multi-task learning framework including a multi-scale shared encoder and task-specific decoders to extract comprehensive acoustic features of finger activities. To achieve precise predictions, we utilize the Multilayer Perceptron (MLP) as an encoder and design a series of loss functions in decoding tailored to the specific characteristics of finger activities. During the offline training, CasePad utilizes raw passive finger activity sound as input and leverages the camera for supervision. With the use of Siamese network to extract feature files that only contain finger activity-specific information, the user does not need to collect data to train their own model. Extensive experimental evaluations with different smartphone models validate CasePad's high performance, achieving 98.76% classification accuracy in detecting finger activity direction. Additionally, CasePad demonstrates remarkable precision in deriving detailed finger activity characteristics that closely match the ground truth measurements across various finger activities, including position tracking with a mean squared error (MSE) of 10.28 mm, distance estimation with an MSE of 9.32 mm, and speed derivation with a mean absolute error (MAE) of 7.29 mm/s, respectively.

关键词： Human-computer Interaction Acoustic Sensing Mobile System

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Energy-Efficient Deployment of Stateful FaaS Vertical Applications on Edge Data networks 33

Energy-Efficient Deployment of Stateful FaaS Vertical Applic...

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33rd IEEE International Conference on computer communications and networks (ICCCN)

作者： Cicconetti, Claudio Bruno, Raffaele Passarella, Andrea CNR IIT Pisa Italy

ISBN: (纸本)9798350348439;9798350384611

5G and beyond support the deployment of vertical applications, which is particularly appealing in combination with network slicing and edge computing to create a logically isolated environment for executing customer services. Even if serverless computing has gained significant interest as a cloud-native technology its adoption at the edge is lagging, especially because of the need to support stateful tasks, which are commonplace in, e.g., cognitive services, but not fully amenable to being deployed on limited and decentralized computing infrastructures. In this work, we study the emerging paradigm of stateful Function as a Service (FaaS) with lightweight task abstractions in WebAssembly. Specifically, we assess the implications of deploying inter-dependent tasks with an internal state on edge computing resources using a stateless vs. stateful approach and then derive a mathematical model to estimate the energy consumption of a workload with given characteristics, considering the power used for both processing and communication. The model is used in extensive simulations to determine the impact of key factors and assess the energy trade-offs of stateless vs. stateful.

关键词： Stateful FaaS Local Data networks Beyond 5G Vertical Applications Serverless Computing

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An Edge Resource Selection Scheme Considering QoS and Computational Parameters 33

An Edge Resource Selection Scheme Considering QoS and Comput...

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33rd IEEE International Conference on computer communications and networks (ICCCN)

作者： Frei, Matthias German, Reinhard Djanatliev, Anatoli Fraunhofer Inst Integrated Circuits IIS Erlangen Germany Univ Erlangen Nurnberg Dept Comp Sci Comp Networks & Commun Syst Erlangen Germany

ISBN: (纸本)9798350348439;9798350384611

Edge computing enhances cloud architectures through the integration of distributed platforms that operate closer to end-users. With dynamically changing network topologies, user mobility, multiple access networks, demand-driven service orchestration, and consequently several distributed service instances that are available, methods for selecting edge resources must adapt to these conditions. This consideration is particularly relevant for edge computing, where target applications often have strict Quality of Service (QoS) constraints. This work proposes an edge resource selection scheme that considers several QoS parameters, computing load, and service usage cost. These metrics are employed to derive a selection score through four distinct algorithms. The overall performance of each algorithm is evaluated by scoring examples, a sensitivity analysis, and in a system-level simulation of a fifth generation (5G) cellular network with user mobility and multiple Multi-Access Edge Computing (MEC) platforms. The results show that all approaches perform well under unambiguous QoS and load conditions, i.e., very good or very poor, but some lose accuracy under moderate, but still acceptable, conditions. For the goal of cost-optimized selection while meeting QoS constraints, one novel method presented is significantly superior to the others. Used in simulation, it generates 12% more computing load on cheaper platforms than its closest competitor, and maintains significantly lower loads on high-cost platforms.

关键词： edge cloud MEC resources selection scoring QoS latency performance load cost 5G simulation

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33rd IEEE International Conference on computer communications and networks (ICCCN)

作者： Akin, Hamdi Selim Aktas, Mehmet Emin Islam, Muhammed Ifte Hossain, Tanvir Akbas, Esra Georgia State Univ Dept Comp Sci Atlanta GA 30303 USA Georgia State Univ Inst Insight Atlanta GA 30303 USA

ISBN: (纸本)9798350348439;9798350384611

Network analysis is an emerging field with a wide spectrum of applications across many disciplines such as social networks, computer networks, and healthcare. However, the ever-increasing size of real-world networks is a major challenge for network analysis due to their high computational and space costs. In this paper, we utilize a node similarity-based graph compression method, SGC, and investigate the effect of various node similarity measures on graph compression. SGC compresses the input graph to a smaller graph without losing any/much information about its global structure and the local proximity of its vertices. We apply our compression method to the network embedding problem to study its effectiveness and efficiency. Our experimental results on four real-world networks show that each similarity measure has a different effect on graph compression and embedding, where some yield an improvement up to 70% network embedding time without decreasing classification accuracy as evaluated on single and multi-label classification tasks.

关键词： graph compression network embedding node similarity node classification

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