Edutech Pub Date : 2023-06-12 DOI: 10.23919/IFIPNetworking57963.2023.10186435

Hongwu Li, XingYu Fu, Yujia Zhu, Rong Yang, C. Li

{"title":"MetaIoT: Few Shot Malicious Traffic Detection in Internet of Things Networks Based on HIN","authors":"Hongwu Li, XingYu Fu, Yujia Zhu, Rong Yang, C. Li","doi":"10.23919/IFIPNetworking57963.2023.10186435","DOIUrl":"https://doi.org/10.23919/IFIPNetworking57963.2023.10186435","url":null,"abstract":"Identification of abnormal and malicious traffic in the Internet-of-Things (IoT) network is critical for IoT security. However, it is worth noting that the majority of recent efforts demand a large amount of tagged traffic to train a machine-learning model. In this paper, we develop MetaIoT, an intelligent approach for identifying malicious traffic. MetaIoT is more accurate and more difficult for attackers to circumvent by taking into account both the local attributes of each traffic source and their global relationships. In MetaIoT, we begin by considering the heterogeneous and dynamic nature of traffic. Then, we introduce a heterogeneous graph (HG) to model the relationships between traffic and employ a relation-based heterogeneous graph attention network to learn node (i.e., traffic) representations over the built HG. Alternatively, MetaIoT addresses the issue of needing enough data for model training through the meta-learning technique. After conducting a comprehensive comparison with the baseline through experiments, our model demonstrated superior performance in few-shot learning scenarios, obtaining an accuracy score of 91.65% and an F1 score of 90.33%. When compared with current state-of-the-art IoT traffic detection models, our model showed the best results.","PeriodicalId":31737,"journal":{"name":"Edutech","volume":"11 1","pages":"1-9"},"PeriodicalIF":0.0,"publicationDate":"2023-06-12","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"79507548","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

Placement of UAVs to Reconnect Lost Subnetworks in Wireless Sensor Networks 安置无人机重新连接丢失的子网在无线传感器网络

Edutech Pub Date : 2023-06-12 DOI: 10.23919/IFIPNetworking57963.2023.10186442

Lu Lin, Xiaojun Zhu, Ji'ao Tang, Chaoyu Dong

引用次数: 0

Simulation and Practice: A Hybrid Experimentation Platform for TSN 仿真与实践:TSN混合实验平台

Edutech Pub Date : 2023-06-12 DOI: 10.23919/IFIPNetworking57963.2023.10186364

Marcin Bosk, F. Rezabek, Johannes Abel, Kilian Holzinger, Max Helm, Georg Carle, J. Ott

{"title":"Simulation and Practice: A Hybrid Experimentation Platform for TSN","authors":"Marcin Bosk, F. Rezabek, Johannes Abel, Kilian Holzinger, Max Helm, Georg Carle, J. Ott","doi":"10.23919/IFIPNetworking57963.2023.10186364","DOIUrl":"https://doi.org/10.23919/IFIPNetworking57963.2023.10186364","url":null,"abstract":"Real-time systems rely on deterministic, reliable, and low-latency networks. Ethernet with Time Sensitive Networking (TSN) is used to enhance these systems' robustness while fulfilling their increasing requirements. Instead of introducing a single solution offering low latency, jitter, and packet loss, TSN provides a set of mechanisms that can be selectively combined for each specific use case. Having the means to assess various TSN standards and their configuration is crucial for successful deployments, with hardware (HW) infrastructure and simulators being common approaches. Each method presents challenges, which we aim to tackle with this work by unifying the experiment configuration and its deployment in respective environments. As a base, we use an open-source TSN framework called EnGINE. We extend the framework's functionality and provide a replacement for its HW deployment using the OMNeT++ simulator. A simulated environment is integrated via a translation layer that converts an EnGINE configuration into an OMNeT++ one. We provide design and implementation details and verify the functionality of our approach by running initial experiments and comparing them to previous results by the EnGINE authors. We show that simulation generally achieves lower delay and jitter due to its idealistic nature without typical system artifacts. However, some HW infrastructure and software-dependent configurations may unintentionally impact simulation results. Furthermore, we open-source our contributions enabling an easy way to configure once but evaluate twice while providing additional insights into HW and simulator deployments.","PeriodicalId":31737,"journal":{"name":"Edutech","volume":"49 1","pages":"1-9"},"PeriodicalIF":0.0,"publicationDate":"2023-06-12","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"80311613","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 2

A cheap and accurate delay-based IP Geolocation method using Machine Learning and Looking Glass 一种使用机器学习和Looking Glass的廉价且精确的基于延迟的IP地理定位方法

Edutech Pub Date : 2023-06-12 DOI: 10.23919/IFIPNetworking57963.2023.10186436

Allen Hong, Yahui Li, Han Zhang, Min Wang, Changqing An, Jilong Wang

{"title":"A cheap and accurate delay-based IP Geolocation method using Machine Learning and Looking Glass","authors":"Allen Hong, Yahui Li, Han Zhang, Min Wang, Changqing An, Jilong Wang","doi":"10.23919/IFIPNetworking57963.2023.10186436","DOIUrl":"https://doi.org/10.23919/IFIPNetworking57963.2023.10186436","url":null,"abstract":"Predicting the geographical location of an IP host is a fundamental and valuable but long-standing challenge in the field of network research. Although delay-based methods have relatively high coverage and low time consumption, currently this type of method is not accurate enough and requires a large number of vantage points, making its cost high. In this paper, we propose a novel delay-based framework to make IP geolocation more accurate and cheap. Firstly, we collect 373 Looking Glass with known geographical addresses and overcome the high cost problem by using them as vantage points. Secondly, we make the prediction of geographical coordinates more accurate by using the machine learning algorithm and regional information of the target IP. Finally, we propose a method based on machine learning to supplement missing values in the delay data and improve the accuracy of geolocation successfully. Our experiment results validate the feasibility and improvement of our method. Using our method, we have an average error of 69.49 km for the geolocation of our test set, which is approximately 160 km less than the state-of-art work.","PeriodicalId":31737,"journal":{"name":"Edutech","volume":"291 1","pages":"1-9"},"PeriodicalIF":0.0,"publicationDate":"2023-06-12","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"76473476","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 1

TPC Members and Reviewers TPC成员和审稿人

Edutech Pub Date : 2023-06-12 DOI: 10.23919/ifipnetworking57963.2023.10186358

引用次数: 0

On Real-time Failure Localization via Instance Correlation in Optical Transport Networks 基于实例关联的光传输网络实时故障定位研究

Edutech Pub Date : 2023-06-12 DOI: 10.23919/IFIPNetworking57963.2023.10186406

Yan Jiao, P. Ho, Xiangzhu Lu, Kairan Liang, Yuren You, János Tapolcai, Bingbing Li, Limei Peng

{"title":"On Real-time Failure Localization via Instance Correlation in Optical Transport Networks","authors":"Yan Jiao, P. Ho, Xiangzhu Lu, Kairan Liang, Yuren You, János Tapolcai, Bingbing Li, Limei Peng","doi":"10.23919/IFIPNetworking57963.2023.10186406","DOIUrl":"https://doi.org/10.23919/IFIPNetworking57963.2023.10186406","url":null,"abstract":"Failure localization serves as a key to an effective fault management plane in the Internet backbone. This paper investigates a novel failure localization approach, namely Instance Correlation based Fault Diagnosis (IC-FD), for achieving efficient fault management in Optical Transport Networks (OTN). The IC-FD is aimed at real-time localization of failed components in the optical layer of OTN through correlation of alarms and status changes of network devices (referred to as instances) via a learned binary classifier. The outcome of IC-FD is one or multiple instance correlation trees (ICT) where the instances corresponding to the faulty network devices are taken as the tree roots. Notably, the proposed binary classifier is characterized by an intelligent feature extraction of historical instance correlation in dimensions of time, board/alarm attribute, network topology, and traffic distribution. Extensive case studies are conducted to demonstrate the advantages gained by IC-FD in terms of its high precision and low computation complexity, as well as analysis of its performance due to various environmental turbulence such as network topology, traffic diversity and noise alarms.","PeriodicalId":31737,"journal":{"name":"Edutech","volume":"121 1","pages":"1-9"},"PeriodicalIF":0.0,"publicationDate":"2023-06-12","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"86159064","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

GNSGA: A Decentralized Data Replication Algorithm for Big Science Data GNSGA:面向大科学数据的分散式数据复制算法

Edutech Pub Date : 2023-06-12 DOI: 10.23919/IFIPNetworking57963.2023.10186357

Xi Wang, Xusheng Ai, F. Feltus, Susmit Shannigrahi

{"title":"GNSGA: A Decentralized Data Replication Algorithm for Big Science Data","authors":"Xi Wang, Xusheng Ai, F. Feltus, Susmit Shannigrahi","doi":"10.23919/IFIPNetworking57963.2023.10186357","DOIUrl":"https://doi.org/10.23919/IFIPNetworking57963.2023.10186357","url":null,"abstract":"Domain science applications in fields such as Genomics and High-Energy Particle Physics use geographically distributed data federations for publishing and accessing datasets. Data is typically replicated among data federation nodes to improve efficiency and fault tolerance. While replication strategies are well documented in distributed database instances (e.g., Apache Cassandra), replication among distributed data storage nodes can be ad-hoc. Replication over wide area networks can also require global coordination (or global shared state) which is not ideal when multiple organizations are involved. In this paper, we introduce GNSGA, which stands for Greedy Non-dominated Sorting Genetic Algorithm II. It is an optimization algorithm that combines greedy and non-dominated sorting genetic algorithms to solve multi-objective optimization problems. The “greedy” aspect of the algorithm refers to the use of a greedy strategy in the selection of nodes, while the “Non-dominated Sorting Genetic Algorithm II (NSGA-II)” is a fast non-dominated multi-objective optimization algorithm with an elite retention strategy. Replication decisions in GNSGA are based on the local properties and resource availability of the data storage nodes. By incorporating Greedy and NSGA-II algorithms, GNSGA optimizes multiple conflicting objectives to satisfy replica placement constraints such as cost, time, and storage capacity. We compared GNSGA with popular replica placement strategies, such as closest node replication, shortest transfer time, and a Particle Swarm Optimization (PSO)-based replication algorithm. We performed simulations and an actual deployment on the NSF's FABRIC testbed for evaluation. The results demonstrate that GNSGA consistently selects nodes to reduce replication time by 5.8%-15.4% while satisfying replication constraints (i.e., cost, time, and storage). We also show that GNSGA is beneficial for replicating large files over wide area networks.","PeriodicalId":31737,"journal":{"name":"Edutech","volume":"20 1","pages":"1-9"},"PeriodicalIF":0.0,"publicationDate":"2023-06-12","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"73454846","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

Longitudinal Analysis of Wildcard Certificates in the WebPKI WebPKI中通配符证书的纵向分析

Edutech Pub Date : 2023-06-12 DOI: 10.23919/IFIPNetworking57963.2023.10186356

David Hasselquist, Ludvig Bolin, Emil Carlsson, Adam Hylander, Martin Larsson, Erik Voldstad, Niklas Carlsson

引用次数: 0

Tactical Traffic Engineering with Segment Routing Midpoint Optimization 基于分段路由中点优化的战术交通工程

Edutech Pub Date : 2023-06-12 DOI: 10.23919/IFIPNetworking57963.2023.10186413

Alexander Brundiers, Timmy Schüller, N. Aschenbruck

{"title":"Tactical Traffic Engineering with Segment Routing Midpoint Optimization","authors":"Alexander Brundiers, Timmy Schüller, N. Aschenbruck","doi":"10.23919/IFIPNetworking57963.2023.10186413","DOIUrl":"https://doi.org/10.23919/IFIPNetworking57963.2023.10186413","url":null,"abstract":"Tactical Traffic Engineering (TE) plays a crucial role in the operation of modern backbone networks as it enables operators to quickly react to failures or unforeseen traffic changes. In this paper, we propose a new Segment Routing (SR)-based optimization algorithm called MOLS. It is the first algorithm that applies the recent concept of Midpoint Optimization (MO) for SR to the use case of fast, tactical TE. In an extensive evaluation, based on various real-world topologies, we show that our algorithm performs virtually on par or better than comparable state-of-the-art tactical TE approaches that rely on conventional SR. However, especially for larger networks, the number of SR policies required by our algorithm is substantially lower. This not only reduces the introduced overhead but also allows for faster deployment of the computed configurations since less changes have to be applied to the network. Furthermore, we show that MOLS is able to remove congestion in sub-second fashion for multiple TE use cases. Lastly, MOLS also is the first algorithm in literature to fully utilize the capabilities of MO without any artificial limitations. This enables it to find similar or even better solutions than the only other MO-capable algorithm in literature in just a fraction of the time.","PeriodicalId":31737,"journal":{"name":"Edutech","volume":"20 1","pages":"1-9"},"PeriodicalIF":0.0,"publicationDate":"2023-06-12","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"73949068","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 2

FedABR: A Personalized Federated Reinforcement Learning Approach for Adaptive Video Streaming 自适应视频流的个性化联邦强化学习方法

Edutech Pub Date : 2023-06-12 DOI: 10.23919/IFIPNetworking57963.2023.10186404

Yeting Xu, Xiang Li, Yezhou Yang, Zhenjie Lin, Liming Wang, Wenzhong Li

{"title":"FedABR: A Personalized Federated Reinforcement Learning Approach for Adaptive Video Streaming","authors":"Yeting Xu, Xiang Li, Yezhou Yang, Zhenjie Lin, Liming Wang, Wenzhong Li","doi":"10.23919/IFIPNetworking57963.2023.10186404","DOIUrl":"https://doi.org/10.23919/IFIPNetworking57963.2023.10186404","url":null,"abstract":"Modern video streaming applications apply adaptive bitrate (ABR) algorithms to enhance user quality of experience (QoE). The existing model-based ABR algorithms failed to generalize to diverse network conditions and personalized QoE objectives due to their fixed control rules. The learning-based ABR algorithms required significant tuning to learn a well-performed model which can cause a QoE degradation during the model testing phase. In this paper, we propose FedABR, a novel ABR algorithm based on personalized federated learning to address the above challenges. To enable clients' local model dealing with network environment changes, we introduce a federated learning approach to train a global model using all the clients' local model without gathering their data together to protect clients' privacy. We also introduced an adaptation phase to train a personalized model for each client to maximize their individual QoE. By jointly training multiple learning tasks with a global model, it has the ability to provide transferable knowledge to supervise bitrate selection, and can be efficiently adapted to a new task in unseen environment with much fewer data samples and training epochs. We implement the proposed FedABR based on an emulation platform which connects to the Linux network protocol stack through a virtual network interface to send real data packets for evaluation. Extensive experiments based on real-world traces show that FedABR achieves the best comprehensive QoE compared with the state-of-the-art ABR algorithms in a variety of network environments.","PeriodicalId":31737,"journal":{"name":"Edutech","volume":"129 1","pages":"1-9"},"PeriodicalIF":0.0,"publicationDate":"2023-06-12","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"85747871","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

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