Pub Date : 2022-09-26DOI: 10.1109/lcn53696.2022.9843809
{"title":"Message from the Demo Chair","authors":"","doi":"10.1109/lcn53696.2022.9843809","DOIUrl":"https://doi.org/10.1109/lcn53696.2022.9843809","url":null,"abstract":"","PeriodicalId":303965,"journal":{"name":"2022 IEEE 47th Conference on Local Computer Networks (LCN)","volume":"17 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2022-09-26","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"124149151","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}
Pub Date : 2022-09-26DOI: 10.1109/LCN53696.2022.9843689
Hua Wu, Xingmeng Fan, Guang Cheng, Xiaoyan Hu
Due to the heterogeneity, fragmentation, and lack of visibility, Internet of Things has become the new target for attacks. Therefore, it is necessary for Internet Service Providers to identify IoT devices to prevent attacks and protect the entire network in time. In this paper, we propose an IoT device identification approach based on lightweight deep learning models using a single feature. Specifically, we analyze the traffic pattern specific to IoT devices and use one feature to characterize this pattern, reducing the time consumption. Moreover, we select multiple time scales to extract this feature for different IoT devices, achieving an accurate characterization and improving the accuracy. Furthermore, we use unidirectional flows as analysis objects, suitable for backbone networks. The evaluation results on real-world datasets show that our approach achieves an accuracy of over 99%, with one-seventeenth of the time consumption of the state-of-the-art approach, realizing the lightweight and real-time requirements.
{"title":"Identify IoT Devices from Backbone Networks Using Lightweight Neural Networks","authors":"Hua Wu, Xingmeng Fan, Guang Cheng, Xiaoyan Hu","doi":"10.1109/LCN53696.2022.9843689","DOIUrl":"https://doi.org/10.1109/LCN53696.2022.9843689","url":null,"abstract":"Due to the heterogeneity, fragmentation, and lack of visibility, Internet of Things has become the new target for attacks. Therefore, it is necessary for Internet Service Providers to identify IoT devices to prevent attacks and protect the entire network in time. In this paper, we propose an IoT device identification approach based on lightweight deep learning models using a single feature. Specifically, we analyze the traffic pattern specific to IoT devices and use one feature to characterize this pattern, reducing the time consumption. Moreover, we select multiple time scales to extract this feature for different IoT devices, achieving an accurate characterization and improving the accuracy. Furthermore, we use unidirectional flows as analysis objects, suitable for backbone networks. The evaluation results on real-world datasets show that our approach achieves an accuracy of over 99%, with one-seventeenth of the time consumption of the state-of-the-art approach, realizing the lightweight and real-time requirements.","PeriodicalId":303965,"journal":{"name":"2022 IEEE 47th Conference on Local Computer Networks (LCN)","volume":"21 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2022-09-26","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"123598715","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}
Pub Date : 2022-09-26DOI: 10.1109/LCN53696.2022.9843211
S. Sharma, S. K. Peddoju
Explosive traffic and service delay are bottlenecks in providing Quality of Service (QoS) to the Internet of Things (IoT) end-users. Edge caching emerged as a promising solution, but data transiency, limited caching capability, and network volatility trigger the dimensionality curse. Therefore, we propose a Deep Reinforcement Learning (DRL) approach, named IoT-Cache, to caching action optimization. An appropriate reward function is designed to increase the cache hit rate and optimize the overall data-cache allocation. A practical scenario with inconsistent requests and data item sizes is considered, and a Distributed Proximal Policy Optimization (DPPO) algorithm is proposed, enabling IoT edge nodes to learn caching policy. RLlib framework is used to scale the training in distributed Publish/Subscribe network. The performance evaluation demonstrates a significant improvement and faster convergence for IoT-Cache cost function, a trade-off between communication cost and data freshness over existing DRL and baseline caching solutions.
{"title":"IoT-Cache: Caching Transient Data at the IoT Edge","authors":"S. Sharma, S. K. Peddoju","doi":"10.1109/LCN53696.2022.9843211","DOIUrl":"https://doi.org/10.1109/LCN53696.2022.9843211","url":null,"abstract":"Explosive traffic and service delay are bottlenecks in providing Quality of Service (QoS) to the Internet of Things (IoT) end-users. Edge caching emerged as a promising solution, but data transiency, limited caching capability, and network volatility trigger the dimensionality curse. Therefore, we propose a Deep Reinforcement Learning (DRL) approach, named IoT-Cache, to caching action optimization. An appropriate reward function is designed to increase the cache hit rate and optimize the overall data-cache allocation. A practical scenario with inconsistent requests and data item sizes is considered, and a Distributed Proximal Policy Optimization (DPPO) algorithm is proposed, enabling IoT edge nodes to learn caching policy. RLlib framework is used to scale the training in distributed Publish/Subscribe network. The performance evaluation demonstrates a significant improvement and faster convergence for IoT-Cache cost function, a trade-off between communication cost and data freshness over existing DRL and baseline caching solutions.","PeriodicalId":303965,"journal":{"name":"2022 IEEE 47th Conference on Local Computer Networks (LCN)","volume":"111 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2022-09-26","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"124762808","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}
Pub Date : 2022-09-26DOI: 10.1109/LCN53696.2022.9843310
Esmaeil Amiri, Ning Wang, M. Shojafar, R. Tafazolli
Open radio access network (Open-RAN) is becoming a key component of cellular networks, and therefore optimizing its architecture is vital. The Open-RAN is a distributed architecture that lets the virtualized networking functions be split between Distributed Units (DU) and Centralized Units (CUs); as a result, there is a wide range of design options. We propose an optimization problem to choose the split points. The objective is to balance the load across CUs as well as midhaul links by considering delay requirements. The resulting formulation is an NP-hard problem that is solved with a novel heuristic algorithm. Performance evaluation shows that the gap between optimal and heuristic solutions does not exceed 2%. An in-depth analysis of different centralization levels shows that using multi-CUs could reduce the total bandwidth usage by up to 20%. Moreover, multipath routing can improve the result of load balancing between midhaul links while increasing bandwidth usage.
开放式无线接入网（Open-RAN）正在成为蜂窝网络的关键组成部分，因此优化其架构至关重要。开放式无线接入网是一种分布式架构，可在分布式单元（DU）和集中式单元（CU）之间拆分虚拟化网络功能，因此有多种设计方案可供选择。我们提出了一个选择分割点的优化问题。目标是通过考虑延迟要求，平衡各 CU 和中途链路的负载。由此产生的公式是一个 NP 难问题，可通过一种新颖的启发式算法来解决。性能评估表明，最优解与启发式解之间的差距不超过 2%。对不同集中程度的深入分析表明，使用多 CU 可以将总带宽使用量最多减少 20%。此外，多路径路由可以在提高带宽使用率的同时，改善中途链路之间的负载平衡效果。
{"title":"Optimizing Virtual Network Function Splitting in Open-RAN Environments","authors":"Esmaeil Amiri, Ning Wang, M. Shojafar, R. Tafazolli","doi":"10.1109/LCN53696.2022.9843310","DOIUrl":"https://doi.org/10.1109/LCN53696.2022.9843310","url":null,"abstract":"Open radio access network (Open-RAN) is becoming a key component of cellular networks, and therefore optimizing its architecture is vital. The Open-RAN is a distributed architecture that lets the virtualized networking functions be split between Distributed Units (DU) and Centralized Units (CUs); as a result, there is a wide range of design options. We propose an optimization problem to choose the split points. The objective is to balance the load across CUs as well as midhaul links by considering delay requirements. The resulting formulation is an NP-hard problem that is solved with a novel heuristic algorithm. Performance evaluation shows that the gap between optimal and heuristic solutions does not exceed 2%. An in-depth analysis of different centralization levels shows that using multi-CUs could reduce the total bandwidth usage by up to 20%. Moreover, multipath routing can improve the result of load balancing between midhaul links while increasing bandwidth usage.","PeriodicalId":303965,"journal":{"name":"2022 IEEE 47th Conference on Local Computer Networks (LCN)","volume":"1 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2022-09-26","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"129343206","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}
Pub Date : 2022-09-26DOI: 10.1109/LCN53696.2022.9843700
Akanksha Dixit, Max Smith-Creasey, M. Rajarajan
The fundamental requirement for interaction between digital entities is a secure and privacy-preserving digital identity infrastructure. Traditional approaches rely heavily on centralized architectural components such as Certificate Authorities (CAs) and credential storage databases that have drawbacks like a single point of failure, attack prone honeypot databases and poor scalability. Self-Sovereign Identity (SSI) is a novel decentralized digital identity model that uses Decentralized Identifiers (DIDs) and Verifiable Credentials (VCs). In this work, we propose a novel decentralized identity framework for Industrial Internet-of-Things (IIoT) based on SSI model. The proposed framework is implemented on two blockchain platforms namely Ethereum and Hyperledger Indy to study the underlying overheads.
{"title":"A Decentralized IIoT Identity Framework based on Self-Sovereign Identity using Blockchain","authors":"Akanksha Dixit, Max Smith-Creasey, M. Rajarajan","doi":"10.1109/LCN53696.2022.9843700","DOIUrl":"https://doi.org/10.1109/LCN53696.2022.9843700","url":null,"abstract":"The fundamental requirement for interaction between digital entities is a secure and privacy-preserving digital identity infrastructure. Traditional approaches rely heavily on centralized architectural components such as Certificate Authorities (CAs) and credential storage databases that have drawbacks like a single point of failure, attack prone honeypot databases and poor scalability. Self-Sovereign Identity (SSI) is a novel decentralized digital identity model that uses Decentralized Identifiers (DIDs) and Verifiable Credentials (VCs). In this work, we propose a novel decentralized identity framework for Industrial Internet-of-Things (IIoT) based on SSI model. The proposed framework is implemented on two blockchain platforms namely Ethereum and Hyperledger Indy to study the underlying overheads.","PeriodicalId":303965,"journal":{"name":"2022 IEEE 47th Conference on Local Computer Networks (LCN)","volume":"4 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2022-09-26","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"127191867","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}
Pub Date : 2022-09-26DOI: 10.1109/LCN53696.2022.9843373
Eryk Schiller, Timo Surbeck, Mikael Gasparyan, B. Stiller, T. Braun
Information-Centric Network (ICN) architectures, such as Named Data Networking (NDN), can improve content delivery on the Internet by deploying in-network caching techniques. Replacing the entire established Internet with a novel architecture is a non-trivial task, which is why this work develops a layered network architecture consisting of several smaller NDN-based mobile networks (resp., domains), interconnected using a Distributed Hash Table (DHT)-based network running as an overlay on top of existing Internet infrastructures. Using simulations, we model real-world network characteristics to evaluate the proposed architecture’s performance successfully.
{"title":"ICN With DHT Support in Mobile Networks","authors":"Eryk Schiller, Timo Surbeck, Mikael Gasparyan, B. Stiller, T. Braun","doi":"10.1109/LCN53696.2022.9843373","DOIUrl":"https://doi.org/10.1109/LCN53696.2022.9843373","url":null,"abstract":"Information-Centric Network (ICN) architectures, such as Named Data Networking (NDN), can improve content delivery on the Internet by deploying in-network caching techniques. Replacing the entire established Internet with a novel architecture is a non-trivial task, which is why this work develops a layered network architecture consisting of several smaller NDN-based mobile networks (resp., domains), interconnected using a Distributed Hash Table (DHT)-based network running as an overlay on top of existing Internet infrastructures. Using simulations, we model real-world network characteristics to evaluate the proposed architecture’s performance successfully.","PeriodicalId":303965,"journal":{"name":"2022 IEEE 47th Conference on Local Computer Networks (LCN)","volume":"41 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2022-09-26","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"129104316","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}
Pub Date : 2022-09-26DOI: 10.1109/ipdps.2004.1303202
A. Omicini, Julia Radoszycki, N. Peyrard, Mirko Viroli, Danilo Pianini, A. Ricci, Pietro Brunetti, Takamasa Ihara, Shunsuke Tsuruta, Taiki Todo, Yuko Sakurai
{"title":"List of Accepted Papers","authors":"A. Omicini, Julia Radoszycki, N. Peyrard, Mirko Viroli, Danilo Pianini, A. Ricci, Pietro Brunetti, Takamasa Ihara, Shunsuke Tsuruta, Taiki Todo, Yuko Sakurai","doi":"10.1109/ipdps.2004.1303202","DOIUrl":"https://doi.org/10.1109/ipdps.2004.1303202","url":null,"abstract":"","PeriodicalId":303965,"journal":{"name":"2022 IEEE 47th Conference on Local Computer Networks (LCN)","volume":"49 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2022-09-26","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"126599226","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}
Pub Date : 2022-09-26DOI: 10.1109/LCN53696.2022.9843273
Jiayi Liu, Shan Lu, Qinghai Yang
On one hand, the congestion notification mechanism Explicit Congestion Notification (ECN) can only provide coarse-grained congestion signal, which is not sufficient to indicate accurate network and congestion status. On the other hand, the emerging learning-based intelligent congestion control and route selection mechanisms require fine-grained network states information to take accurate actions. This calls for the development of enhanced ECN mechanism to provide precise congestion information and network states. In this work, we design Accurate-ECN, an enhancement of ECN with Inband Network Telemetry (INT) to collect and report detailed network congestion states by attaching network state metadata to the data packets and send back to the sender through TCP ACK by the packet receiver. We designed the Accurate-ECN frame format and the data packet parsing process, and implement the mechanism through the P4 language. Finally, through evaluation, Accurate-ECN is demonstrated to provide various precise network states under different congestion levels.
{"title":"Accurate-ECN: An ECN Enhancement with Inband Network Telemetry","authors":"Jiayi Liu, Shan Lu, Qinghai Yang","doi":"10.1109/LCN53696.2022.9843273","DOIUrl":"https://doi.org/10.1109/LCN53696.2022.9843273","url":null,"abstract":"On one hand, the congestion notification mechanism Explicit Congestion Notification (ECN) can only provide coarse-grained congestion signal, which is not sufficient to indicate accurate network and congestion status. On the other hand, the emerging learning-based intelligent congestion control and route selection mechanisms require fine-grained network states information to take accurate actions. This calls for the development of enhanced ECN mechanism to provide precise congestion information and network states. In this work, we design Accurate-ECN, an enhancement of ECN with Inband Network Telemetry (INT) to collect and report detailed network congestion states by attaching network state metadata to the data packets and send back to the sender through TCP ACK by the packet receiver. We designed the Accurate-ECN frame format and the data packet parsing process, and implement the mechanism through the P4 language. Finally, through evaluation, Accurate-ECN is demonstrated to provide various precise network states under different congestion levels.","PeriodicalId":303965,"journal":{"name":"2022 IEEE 47th Conference on Local Computer Networks (LCN)","volume":"27 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2022-09-26","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"124978544","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}
Pub Date : 2022-09-26DOI: 10.1109/LCN53696.2022.9843704
Amir Mohamad, H. Hassanein
The demand for ultra-low latency requirements is fueled by the growing popularity of time-sensitive applications including virtual, augmented and mixed reality, and industrial IoT. Edge computing is positioned to fulfill such stringent latency requirements. Addressing the increasing demand for time-sensitive applications becomes challenging due to limited resource at the edge. Even though virtual network function (VNF) sharing is known to improve the utilization of the service providers’ resources, service requests -including time-sensitive ones- can nevertheless be rejected. This paper proposes PSVS: a Prediction-based Service placement scheme with VNF Sharing at the edge. PSVS utilizes the predicted required resources in a defined lookahead window to minimize the rejection rate of premium services. A safety-margin is empirically-defined and used to add resiliency against prediction errors. Results show more than a 50% reduction in the rejection rate of premium services. Moreover, PSVS is resilient to prediction errors.
{"title":"Prediction-based SFC Placement with VNF Sharing at the Edge","authors":"Amir Mohamad, H. Hassanein","doi":"10.1109/LCN53696.2022.9843704","DOIUrl":"https://doi.org/10.1109/LCN53696.2022.9843704","url":null,"abstract":"The demand for ultra-low latency requirements is fueled by the growing popularity of time-sensitive applications including virtual, augmented and mixed reality, and industrial IoT. Edge computing is positioned to fulfill such stringent latency requirements. Addressing the increasing demand for time-sensitive applications becomes challenging due to limited resource at the edge. Even though virtual network function (VNF) sharing is known to improve the utilization of the service providers’ resources, service requests -including time-sensitive ones- can nevertheless be rejected. This paper proposes PSVS: a Prediction-based Service placement scheme with VNF Sharing at the edge. PSVS utilizes the predicted required resources in a defined lookahead window to minimize the rejection rate of premium services. A safety-margin is empirically-defined and used to add resiliency against prediction errors. Results show more than a 50% reduction in the rejection rate of premium services. Moreover, PSVS is resilient to prediction errors.","PeriodicalId":303965,"journal":{"name":"2022 IEEE 47th Conference on Local Computer Networks (LCN)","volume":"103 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2022-09-26","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"133784057","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}
Pub Date : 2022-09-26DOI: 10.1109/LCN53696.2022.9843419
Sebastian Schäfer, Alexander Löbel, Ulrike Meyer
In this paper, we present the design and implementation of ATLAS, a novel tool for automatically labeling network packets with the process responsible for them. Our tool is able to label all kinds of outbound packets based on Windows events and TCP stream information with ground-truth accuracy. Additionally, it is able to label DNS packets with the correct process name instead of just the DNS resolver. Using ATLAS, it is possible to create large datasets, e.g., to create software fingerprints or train machine learning classifiers. Another use-case is to inspect the network traffic of a machine to determine which application is communicating with whom. We evaluate the performance considering different load scenarios to demonstrate the real-time capacity of ATLAS. Additionally, we analyze the communication endpoints of a Windows 10 host and compare the results before and after disabling all privacy related settings.
{"title":"Accurate Real-Time Labeling of Application Traffic","authors":"Sebastian Schäfer, Alexander Löbel, Ulrike Meyer","doi":"10.1109/LCN53696.2022.9843419","DOIUrl":"https://doi.org/10.1109/LCN53696.2022.9843419","url":null,"abstract":"In this paper, we present the design and implementation of ATLAS, a novel tool for automatically labeling network packets with the process responsible for them. Our tool is able to label all kinds of outbound packets based on Windows events and TCP stream information with ground-truth accuracy. Additionally, it is able to label DNS packets with the correct process name instead of just the DNS resolver. Using ATLAS, it is possible to create large datasets, e.g., to create software fingerprints or train machine learning classifiers. Another use-case is to inspect the network traffic of a machine to determine which application is communicating with whom. We evaluate the performance considering different load scenarios to demonstrate the real-time capacity of ATLAS. Additionally, we analyze the communication endpoints of a Windows 10 host and compare the results before and after disabling all privacy related settings.","PeriodicalId":303965,"journal":{"name":"2022 IEEE 47th Conference on Local Computer Networks (LCN)","volume":"56 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2022-09-26","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"124969126","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}
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