Pub Date : 2022-09-26DOI: 10.1109/LCN53696.2022.9843536
Patrick Lampe, Markus Sommer, Artur Sterz, Jonas Hochst, Christian Uhl, Bernd Freisleben
Networked systems and applications are often based on proprietary hardware/software components that manufacturers might not be willing to adapt or update if new requirements arise. We present mechanism interception, a novel approach to unobtrusively add or modify functionality to/of an existing networked system or application without touching any proprietary components. Behavioral changes are achieved by functionality-enhancing yet unobtrusive interceptors, i.e., components introduced between systems and their environments adding or updating mechanisms. We illustrate our approach by unobtrusively adding a vertical handover mechanism between Wi-Fi and LTE to a mobile end device without disconnecting TCP sessions. Our results indicate that mechanism interception is a compelling approach to achieve improved service quality and provide previously unavailable functionality.
{"title":"Unobtrusive Mechanism Interception","authors":"Patrick Lampe, Markus Sommer, Artur Sterz, Jonas Hochst, Christian Uhl, Bernd Freisleben","doi":"10.1109/LCN53696.2022.9843536","DOIUrl":"https://doi.org/10.1109/LCN53696.2022.9843536","url":null,"abstract":"Networked systems and applications are often based on proprietary hardware/software components that manufacturers might not be willing to adapt or update if new requirements arise. We present mechanism interception, a novel approach to unobtrusively add or modify functionality to/of an existing networked system or application without touching any proprietary components. Behavioral changes are achieved by functionality-enhancing yet unobtrusive interceptors, i.e., components introduced between systems and their environments adding or updating mechanisms. We illustrate our approach by unobtrusively adding a vertical handover mechanism between Wi-Fi and LTE to a mobile end device without disconnecting TCP sessions. Our results indicate that mechanism interception is a compelling approach to achieve improved service quality and provide previously unavailable functionality.","PeriodicalId":303965,"journal":{"name":"2022 IEEE 47th Conference on Local Computer Networks (LCN)","volume":"37 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":"132900249","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/LCN53696.2022.9843526
Aakash Soni, Jean-Luc Scharbarg
Deficit Round-Robin (DRR) is a promising service discipline for real-time Ethernet without a global synchronisation. Two improved Network Calculus approaches have been proposed to provide the required bounds on end-to-end delays. The first one is fast but can be optimistic for cornet cases. The second one is safe but highly time consuming. In this paper, we remove the potential optimism of the first approach while keeping its low complexity.
{"title":"Deficit Round-Robin: Network Calculus based Worst-Case Traversal Time Analysis Revisited","authors":"Aakash Soni, Jean-Luc Scharbarg","doi":"10.1109/LCN53696.2022.9843526","DOIUrl":"https://doi.org/10.1109/LCN53696.2022.9843526","url":null,"abstract":"Deficit Round-Robin (DRR) is a promising service discipline for real-time Ethernet without a global synchronisation. Two improved Network Calculus approaches have been proposed to provide the required bounds on end-to-end delays. The first one is fast but can be optimistic for cornet cases. The second one is safe but highly time consuming. In this paper, we remove the potential optimism of the first approach while keeping its low complexity.","PeriodicalId":303965,"journal":{"name":"2022 IEEE 47th Conference on Local Computer Networks (LCN)","volume":"3 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":"124922687","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}
Packet classification shows a key role in kinds of network functions, such as access control, routing, and quality of service (QoS). With the rapid growth of the network size, users have to ignore some fields in packet classification due to resource constraints. In addition, some fields may not always be available in some networks. However, traditional packet classification algorithms can hardly handle packet classification if some fields are missing. In this paper, we propose a novel model to build a robust classifier. In the classifier, we utilize the advantage of Recursive Flow Classification (RFC) in handling fields concurrently. Then, we design a new workflow to deal with field missing based on flows. In addition, two complementary bitmap models are designed to accelerate matching packets to flows, and a buffer mechanism is introduced to further improve the classification accuracy. Our experiments show that the proposed classifier can classify packets with an accuracy of 94%-99.5% when the field missing probability is lower than 0.3.
{"title":"Robust Packet Classification with Field Missing","authors":"Jiayao Wang, Ziling Wei, Baosheng Wang, Bao-kang Zhao, Jincheng Zhong","doi":"10.1109/LCN53696.2022.9843560","DOIUrl":"https://doi.org/10.1109/LCN53696.2022.9843560","url":null,"abstract":"Packet classification shows a key role in kinds of network functions, such as access control, routing, and quality of service (QoS). With the rapid growth of the network size, users have to ignore some fields in packet classification due to resource constraints. In addition, some fields may not always be available in some networks. However, traditional packet classification algorithms can hardly handle packet classification if some fields are missing. In this paper, we propose a novel model to build a robust classifier. In the classifier, we utilize the advantage of Recursive Flow Classification (RFC) in handling fields concurrently. Then, we design a new workflow to deal with field missing based on flows. In addition, two complementary bitmap models are designed to accelerate matching packets to flows, and a buffer mechanism is introduced to further improve the classification accuracy. Our experiments show that the proposed classifier can classify packets with an accuracy of 94%-99.5% when the field missing probability is lower than 0.3.","PeriodicalId":303965,"journal":{"name":"2022 IEEE 47th Conference on Local Computer Networks (LCN)","volume":"25 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":"125096138","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/LCN53696.2022.9843690
Bjørn Ivar Teigen, N. Davies, K. Ellefsen, T. Skeie, J. Tørresen
WiFi is one of the most widely deployed networking technologies, and understanding WiFi performance is therefore of great importance. The WiFi MAC layer sometimes introduces significant and variable delays. No existing models of the WiFi protocol describe WiFi performance in terms of complete latency distributions. In this work, we present a novel model of WiFi performance. We explicitly define our model in terms of the latency introduced at each step in the protocol state machine, and the model produces complete latency distributions. We validate the model by comparing its outputs to previous modeling work and real-world measurements. Finally, we use our results to quantify the latency distribution of WiFi as a function of the duration of transmit opportunities and the number of stations competing for the channel. Quantifying this relation represents a significant improvement in our understanding of WiFi performance that would not be possible with existing models.
{"title":"Quantifying the Quality Attenuation of WiFi","authors":"Bjørn Ivar Teigen, N. Davies, K. Ellefsen, T. Skeie, J. Tørresen","doi":"10.1109/LCN53696.2022.9843690","DOIUrl":"https://doi.org/10.1109/LCN53696.2022.9843690","url":null,"abstract":"WiFi is one of the most widely deployed networking technologies, and understanding WiFi performance is therefore of great importance. The WiFi MAC layer sometimes introduces significant and variable delays. No existing models of the WiFi protocol describe WiFi performance in terms of complete latency distributions. In this work, we present a novel model of WiFi performance. We explicitly define our model in terms of the latency introduced at each step in the protocol state machine, and the model produces complete latency distributions. We validate the model by comparing its outputs to previous modeling work and real-world measurements. Finally, we use our results to quantify the latency distribution of WiFi as a function of the duration of transmit opportunities and the number of stations competing for the channel. Quantifying this relation represents a significant improvement in our understanding of WiFi performance that would not be possible with existing models.","PeriodicalId":303965,"journal":{"name":"2022 IEEE 47th Conference on Local Computer Networks (LCN)","volume":"99 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":"123162622","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/LCN53696.2022.9843683
G. Dandachi, Anouar Rkhami, Y. H. Aoul, A. Outtagarts
Network slicing is one of the building blocks in Zero Touch Networks. It mainly consists in a dynamic deployment of services in a substrate network. However, the Virtual Network Embedding (VNE) algorithms used generally follow a static mechanism, which results in sub-optimal embedding strategies and less robust decisions. Some reinforcement learning algorithms have been conceived for a dynamic decision, while being time-costly. In this paper, we propose a combination of deep Q-Network and a Monte Carlo (MC) approach. The idea is to learn, using DQN, a distribution of the placement solution, on which a MC-based search technique is applied. This improves the solution space exploration, and achieves a faster convergence of the placement decision, and thus a safer learning. The obtained results show that DQN with only 8 MC iterations achieves up to 44% improvement compared with a baseline First-Fit strategy, and up to 15% compared to a MC strategy.
{"title":"A Robust Monte-Carlo-Based Deep Learning Strategy for Virtual Network Embedding","authors":"G. Dandachi, Anouar Rkhami, Y. H. Aoul, A. Outtagarts","doi":"10.1109/LCN53696.2022.9843683","DOIUrl":"https://doi.org/10.1109/LCN53696.2022.9843683","url":null,"abstract":"Network slicing is one of the building blocks in Zero Touch Networks. It mainly consists in a dynamic deployment of services in a substrate network. However, the Virtual Network Embedding (VNE) algorithms used generally follow a static mechanism, which results in sub-optimal embedding strategies and less robust decisions. Some reinforcement learning algorithms have been conceived for a dynamic decision, while being time-costly. In this paper, we propose a combination of deep Q-Network and a Monte Carlo (MC) approach. The idea is to learn, using DQN, a distribution of the placement solution, on which a MC-based search technique is applied. This improves the solution space exploration, and achieves a faster convergence of the placement decision, and thus a safer learning. The obtained results show that DQN with only 8 MC iterations achieves up to 44% improvement compared with a baseline First-Fit strategy, and up to 15% compared to a MC strategy.","PeriodicalId":303965,"journal":{"name":"2022 IEEE 47th Conference on Local Computer Networks (LCN)","volume":"35 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":"124313452","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/LCN53696.2022.9843405
Mhd Saria Allahham, Amr Mohamed, H. Hassanein
Mobile Edge Learning (MEL) is a learning paradigm that facilitates training of Machine Learning (ML) models over resource-constrained edge devices. MEL consists of an orchestrator, which represents the model owner of the learning task, and learners, which own the data locally. Enabling the learning process requires the model owner to motivate learners to train the ML model on their local data and allocate sufficient resources. The time limitations and the possible existence of multiple orchestrators open the doors for the resource allocation problem. As such, we model the incentive mechanism and resource allocation as a multi-round Stackelberg game, and propose a Payment-based Time Allocation (PBTA) algorithm to solve the game. In PBTA, orchestrators first determine the pricing, then the learners allocate each orchestrator a timeslot and determine the amount of data and resources for each orchestrator. Finally, we evaluate the PBTA performance and compare it against a recent state-of-the-art approach.
{"title":"Incentive-based Resource Allocation for Mobile Edge Learning","authors":"Mhd Saria Allahham, Amr Mohamed, H. Hassanein","doi":"10.1109/LCN53696.2022.9843405","DOIUrl":"https://doi.org/10.1109/LCN53696.2022.9843405","url":null,"abstract":"Mobile Edge Learning (MEL) is a learning paradigm that facilitates training of Machine Learning (ML) models over resource-constrained edge devices. MEL consists of an orchestrator, which represents the model owner of the learning task, and learners, which own the data locally. Enabling the learning process requires the model owner to motivate learners to train the ML model on their local data and allocate sufficient resources. The time limitations and the possible existence of multiple orchestrators open the doors for the resource allocation problem. As such, we model the incentive mechanism and resource allocation as a multi-round Stackelberg game, and propose a Payment-based Time Allocation (PBTA) algorithm to solve the game. In PBTA, orchestrators first determine the pricing, then the learners allocate each orchestrator a timeslot and determine the amount of data and resources for each orchestrator. Finally, we evaluate the PBTA performance and compare it against a recent state-of-the-art approach.","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":"130314043","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/LCN53696.2022.9843271
João Pedro Meira, Rui Pedro C. Monteiro, J. M. Silva
With continuous technological advancement, multihomed devices are becoming common. They can connect simultaneously to multiple networks through different interfaces. However, since TCP sessions are bound to one interface per device, it hampers applications from taking advantage of all the available connected networks. This has been solved by MPTCP, introduced as a seamless extension to TCP, allowing more reliable sessions and enhanced throughput. However, MPTCP comes with an inherent risk, as it becomes easier to fragment attacks towards evading NIDS. This paper presents a study of how MPTCP can be used to evade NIDS through simple cross-path attacks. It also introduces tools to facilitate assessing MPTCP-based services in diverse network topologies using an emulation environment. Finally, a new solution is proposed to prevent cross-path attacks through uncoordinated networks. This solution consists of a host-level plugin that allows MPTCP sessions only through trusted networks, even in the presence of a NAT.
{"title":"Securing MPTCP Connections: A Solution for Distributed NIDS Environments","authors":"João Pedro Meira, Rui Pedro C. Monteiro, J. M. Silva","doi":"10.1109/LCN53696.2022.9843271","DOIUrl":"https://doi.org/10.1109/LCN53696.2022.9843271","url":null,"abstract":"With continuous technological advancement, multihomed devices are becoming common. They can connect simultaneously to multiple networks through different interfaces. However, since TCP sessions are bound to one interface per device, it hampers applications from taking advantage of all the available connected networks. This has been solved by MPTCP, introduced as a seamless extension to TCP, allowing more reliable sessions and enhanced throughput. However, MPTCP comes with an inherent risk, as it becomes easier to fragment attacks towards evading NIDS. This paper presents a study of how MPTCP can be used to evade NIDS through simple cross-path attacks. It also introduces tools to facilitate assessing MPTCP-based services in diverse network topologies using an emulation environment. Finally, a new solution is proposed to prevent cross-path attacks through uncoordinated networks. This solution consists of a host-level plugin that allows MPTCP sessions only through trusted networks, even in the presence of a NAT.","PeriodicalId":303965,"journal":{"name":"2022 IEEE 47th Conference on Local Computer Networks (LCN)","volume":"33 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":"115961764","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/LCN53696.2022.9843432
Hossein Doroud, Ahmad Alaswad, F. Dressler
Internet service providers (ISP) rely on network traffic classifiers to provide secure and reliable connectivity for their users. Encrypted traffic introduces a challenge as attacks are no longer viable using classic Deep Packet Inspection (DPI) techniques. Distinguishing encrypted from non-encrypted traffic is the first step in addressing this challenge. Several attempts have been conducted to identify encrypted traffic. In this work, we compare the detection performance of DPI, traffic pattern, and randomness tests to identify encrypted traffic in different levels of granularity. In an experimental study, we evaluate these candidates and show that a traffic pattern-based classifier outperforms others for encryption detection.
{"title":"Encrypted Traffic Detection: Beyond the Port Number Era","authors":"Hossein Doroud, Ahmad Alaswad, F. Dressler","doi":"10.1109/LCN53696.2022.9843432","DOIUrl":"https://doi.org/10.1109/LCN53696.2022.9843432","url":null,"abstract":"Internet service providers (ISP) rely on network traffic classifiers to provide secure and reliable connectivity for their users. Encrypted traffic introduces a challenge as attacks are no longer viable using classic Deep Packet Inspection (DPI) techniques. Distinguishing encrypted from non-encrypted traffic is the first step in addressing this challenge. Several attempts have been conducted to identify encrypted traffic. In this work, we compare the detection performance of DPI, traffic pattern, and randomness tests to identify encrypted traffic in different levels of granularity. In an experimental study, we evaluate these candidates and show that a traffic pattern-based classifier outperforms others for encryption detection.","PeriodicalId":303965,"journal":{"name":"2022 IEEE 47th Conference on Local Computer Networks (LCN)","volume":"11 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":"121866232","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/LCN53696.2022.9843411
M. Frank
This demo paper presents an update on ILDA (International Laser Display Association) audio stream specification in the context of the ILDA Digital Network (IDN). A framework of software elements is introduced to interface with audio hardware on a computer and other software that is able to handle multi-channel audio data. The demo will showcase the new elements in action and also will point out the interoperability to existing IDN elements.
{"title":"A Framework for Flexible ILDA Digital Network (IDN) Audio Streaming","authors":"M. Frank","doi":"10.1109/LCN53696.2022.9843411","DOIUrl":"https://doi.org/10.1109/LCN53696.2022.9843411","url":null,"abstract":"This demo paper presents an update on ILDA (International Laser Display Association) audio stream specification in the context of the ILDA Digital Network (IDN). A framework of software elements is introduced to interface with audio hardware on a computer and other software that is able to handle multi-channel audio data. The demo will showcase the new elements in action and also will point out the interoperability to existing IDN elements.","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":"125554370","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}
Optical wireless communication (OWC) is an emerging technology for direct communication through the water-air interface. However, due to the high directionality of optical beams and the harsh oceanic environment, it faces significant challenges to achieve the alignment and preserve the link availability, as the waves cause beam deflection and the mobility of the transceivers makes the link worse. To tackle these challenges and achieve reliable optical communication between autonomous underwater vehicles and unmanned aerial vehicles, we propose a deep reinforcement learning algorithm assisted by an extended Kalman filter to solve the alignment issue. To improve the reliability of communication, we present an algorithm to obtain the optimal beam divergence angle to maximize the link availability. The numerical simulations demonstrate that the proposed scheme achieves better performance in terms of energy consumption and alignment accuracy, and the link availability is increased by 25% compared to that without adjustment.
{"title":"Reliable Water-Air Direct Wireless Communication: Kalman Filter-Assisted Deep Reinforcement Learning Approach","authors":"Jinglong Wang, Hanjiang Luo, Rukhsana Ruby, Jiangang Liu, Kai Guo, Kaishun Wu","doi":"10.1109/LCN53696.2022.9843503","DOIUrl":"https://doi.org/10.1109/LCN53696.2022.9843503","url":null,"abstract":"Optical wireless communication (OWC) is an emerging technology for direct communication through the water-air interface. However, due to the high directionality of optical beams and the harsh oceanic environment, it faces significant challenges to achieve the alignment and preserve the link availability, as the waves cause beam deflection and the mobility of the transceivers makes the link worse. To tackle these challenges and achieve reliable optical communication between autonomous underwater vehicles and unmanned aerial vehicles, we propose a deep reinforcement learning algorithm assisted by an extended Kalman filter to solve the alignment issue. To improve the reliability of communication, we present an algorithm to obtain the optimal beam divergence angle to maximize the link availability. The numerical simulations demonstrate that the proposed scheme achieves better performance in terms of energy consumption and alignment accuracy, and the link availability is increased by 25% compared to that without adjustment.","PeriodicalId":303965,"journal":{"name":"2022 IEEE 47th Conference on Local Computer Networks (LCN)","volume":"75 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":"115668330","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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