Pub Date : 2020-11-16DOI: 10.1109/LCN48667.2020.9314800
Théo Docquier, Yeqiong Song, V. Chevrier, Ludovic Pontnau, Abdelaziz Ahmed Nacer
IEC 61850 has become the reference standard for Substation Automation Systems (SAS) in smart power grids. Switched Ethernet is used for machine to machine communication within SAS. In order to meet stringent real-time constraints, the IEC 61850 application layer protocols can be mapped into different IEEE802.1Q priorities according to their real-time constraints and application criticality. However, the delay evaluation to guarantee real-time requirements can be difficult to perform, especially for lower priority but still real-time constrained traffic. In fact, most existing end-to-end worst-case delay analyses provide upper-bounds, leading to some pessimism and consequently network resource over-provision. In this paper, we present a new method for determining a tight worst-case delay. This method is based on the study of flow characteristics from a given network path. As a flow is interfered by other concurrent flows on its path, their relative offsets with the considered flow greatly impact on its delay. Studying all combinations to find the actual worst-case delay results in high complexity. We show that this complexity can be reduced by only analysing local worst-case delay at each switch in stead of the whole path where the change at each switch would need re-analysing the already analysed switches. An algorithm is also proposed to perform the analysis. An illustrating example shows that our method can reduce the pessimism as it provides the tight worst-case delay instead of the upper-bound of the worst-case delay.
{"title":"Determining a tight worst-case delay of switched Ethernet network in IEC 61850 architectures","authors":"Théo Docquier, Yeqiong Song, V. Chevrier, Ludovic Pontnau, Abdelaziz Ahmed Nacer","doi":"10.1109/LCN48667.2020.9314800","DOIUrl":"https://doi.org/10.1109/LCN48667.2020.9314800","url":null,"abstract":"IEC 61850 has become the reference standard for Substation Automation Systems (SAS) in smart power grids. Switched Ethernet is used for machine to machine communication within SAS. In order to meet stringent real-time constraints, the IEC 61850 application layer protocols can be mapped into different IEEE802.1Q priorities according to their real-time constraints and application criticality. However, the delay evaluation to guarantee real-time requirements can be difficult to perform, especially for lower priority but still real-time constrained traffic. In fact, most existing end-to-end worst-case delay analyses provide upper-bounds, leading to some pessimism and consequently network resource over-provision. In this paper, we present a new method for determining a tight worst-case delay. This method is based on the study of flow characteristics from a given network path. As a flow is interfered by other concurrent flows on its path, their relative offsets with the considered flow greatly impact on its delay. Studying all combinations to find the actual worst-case delay results in high complexity. We show that this complexity can be reduced by only analysing local worst-case delay at each switch in stead of the whole path where the change at each switch would need re-analysing the already analysed switches. An algorithm is also proposed to perform the analysis. An illustrating example shows that our method can reduce the pessimism as it provides the tight worst-case delay instead of the upper-bound of the worst-case delay.","PeriodicalId":245782,"journal":{"name":"2020 IEEE 45th Conference on Local Computer Networks (LCN)","volume":"2018 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2020-11-16","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"128783362","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 : 2020-11-11DOI: 10.1109/LCN48667.2020.9314828
Kalana Abeywardena, Jiawei Zhao, Lexi Brent, Suranga Seneviratne, Ralph Holz
Phishing web pages impersonate legitimate websites to trick users into entering sensitive information such as their credentials. In many high profile data breaches, the initial entry points have been traced back to phishing attacks. Attackers are using increasingly sophisticated methods such as code obfuscation to bypass existing phishing detection systems. Since phishing websites show very high visual similarity to the respective target pages, recent advances in Convolutional Neural Networks (CNN) can be leveraged to build better phishing detection systems. In this work, we propose a novel CNN architecture consisting of two paths to capture the content similarity and structural similarity between web pages. Leveraging the fact that web pages of the same web site are visually similar, we use triplet learning to train our model without any labelled phishing examples.
{"title":"Triplet Mining-based Phishing Webpage Detection","authors":"Kalana Abeywardena, Jiawei Zhao, Lexi Brent, Suranga Seneviratne, Ralph Holz","doi":"10.1109/LCN48667.2020.9314828","DOIUrl":"https://doi.org/10.1109/LCN48667.2020.9314828","url":null,"abstract":"Phishing web pages impersonate legitimate websites to trick users into entering sensitive information such as their credentials. In many high profile data breaches, the initial entry points have been traced back to phishing attacks. Attackers are using increasingly sophisticated methods such as code obfuscation to bypass existing phishing detection systems. Since phishing websites show very high visual similarity to the respective target pages, recent advances in Convolutional Neural Networks (CNN) can be leveraged to build better phishing detection systems. In this work, we propose a novel CNN architecture consisting of two paths to capture the content similarity and structural similarity between web pages. Leveraging the fact that web pages of the same web site are visually similar, we use triplet learning to train our model without any labelled phishing examples.","PeriodicalId":245782,"journal":{"name":"2020 IEEE 45th Conference on Local Computer Networks (LCN)","volume":"46 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2020-11-11","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"134308988","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 : 2020-07-08DOI: 10.1109/LCN48667.2020.9314784
Weixian Yao, Yexuan Li, Weiye Lin, Tianhui Hu, I. Chowdhury, Rahat Masood, Suranga Seneviratne
Third-party security apps are an integral part of the Android app ecosystem. Many users install them as an extra layer of protection for their devices. By installing security apps, the smartphone users place a significant amount of trust on them allowing access to many smartphone resources that contain personal information such as the storage, text messages, email, and browser history. As such, it is essential to understand the mobile security apps ecosystem. In this paper, we present the first empirical study of Android security apps. We analyse 100 Android security apps from multiple aspects and offer insights to their operations and behaviours. Our results show that 20% of the security apps resell the data they collect to third parties; in some cases, even without the user consent. Also, we show that around 50% of the security apps fail to identify known malware.
{"title":"Security Apps under the Looking Glass: An Empirical Analysis of Android Security Apps","authors":"Weixian Yao, Yexuan Li, Weiye Lin, Tianhui Hu, I. Chowdhury, Rahat Masood, Suranga Seneviratne","doi":"10.1109/LCN48667.2020.9314784","DOIUrl":"https://doi.org/10.1109/LCN48667.2020.9314784","url":null,"abstract":"Third-party security apps are an integral part of the Android app ecosystem. Many users install them as an extra layer of protection for their devices. By installing security apps, the smartphone users place a significant amount of trust on them allowing access to many smartphone resources that contain personal information such as the storage, text messages, email, and browser history. As such, it is essential to understand the mobile security apps ecosystem. In this paper, we present the first empirical study of Android security apps. We analyse 100 Android security apps from multiple aspects and offer insights to their operations and behaviours. Our results show that 20% of the security apps resell the data they collect to third parties; in some cases, even without the user consent. Also, we show that around 50% of the security apps fail to identify known malware.","PeriodicalId":245782,"journal":{"name":"2020 IEEE 45th Conference on Local Computer Networks (LCN)","volume":"87 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2020-07-08","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"127180619","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 : 2020-07-06DOI: 10.1109/LCN48667.2020.9314771
Maximilian Bachl, J. Fabini, T. Zseby
The increasing number of different, incompatible congestion control algorithms has led to an increased deployment of fair queuing. Fair queuing isolates each network flow and can thus guarantee fairness for each flow even if the flows’ congestion controls are not inherently fair. So far, each queue in the fair queuing system either has a fixed, static maximum size or is managed by an Active Queue Management (AQM) algorithm like CoDel. In this paper we design an AQM mechanism (Learning Fair Qdisc (LFQ)) that dynamically learns the optimal buffer size for each flow according to a specified reward function online. We show that our Deep Learning based algorithm can dynamically assign the optimal queue size to each flow depending on its congestion control, delay and bandwidth. Comparing to competing fair AQM schedulers, it provides significantly smaller queues while achieving the same or higher throughput.
{"title":"LFQ: Online Learning of Per-flow Queuing Policies using Deep Reinforcement Learning","authors":"Maximilian Bachl, J. Fabini, T. Zseby","doi":"10.1109/LCN48667.2020.9314771","DOIUrl":"https://doi.org/10.1109/LCN48667.2020.9314771","url":null,"abstract":"The increasing number of different, incompatible congestion control algorithms has led to an increased deployment of fair queuing. Fair queuing isolates each network flow and can thus guarantee fairness for each flow even if the flows’ congestion controls are not inherently fair. So far, each queue in the fair queuing system either has a fixed, static maximum size or is managed by an Active Queue Management (AQM) algorithm like CoDel. In this paper we design an AQM mechanism (Learning Fair Qdisc (LFQ)) that dynamically learns the optimal buffer size for each flow according to a specified reward function online. We show that our Deep Learning based algorithm can dynamically assign the optimal queue size to each flow depending on its congestion control, delay and bandwidth. Comparing to competing fair AQM schedulers, it provides significantly smaller queues while achieving the same or higher throughput.","PeriodicalId":245782,"journal":{"name":"2020 IEEE 45th Conference on Local Computer Networks (LCN)","volume":"5 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2020-07-06","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"131195843","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 : 2020-07-01DOI: 10.1109/lanman49260.2020.9153255
E. Bulut, Tina Gaerlan, Lanman, K. Kar
LANMAN 2020 features a central theme chosen to illustrate some of the key challenges facing networks today. This year’s theme of “Ultra-Broadband Wireless Networks: 5G and Beyond” emphasizes emerging communication technologies such as mmWave and THz communications, that offer a tremendous opportunity for unprecedented data rates and low latency. At the same time, they raise numerous challenges–such as positioning, localization, intermittency–that impact networking, communication, and hardware design, often requiring cross-layer approaches to solve them. We have selected a program that broadly reflects this theme, its key challenges, and broader topics in networking.
{"title":"Message from the TPC Chairs","authors":"E. Bulut, Tina Gaerlan, Lanman, K. Kar","doi":"10.1109/lanman49260.2020.9153255","DOIUrl":"https://doi.org/10.1109/lanman49260.2020.9153255","url":null,"abstract":"LANMAN 2020 features a central theme chosen to illustrate some of the key challenges facing networks today. This year’s theme of “Ultra-Broadband Wireless Networks: 5G and Beyond” emphasizes emerging communication technologies such as mmWave and THz communications, that offer a tremendous opportunity for unprecedented data rates and low latency. At the same time, they raise numerous challenges–such as positioning, localization, intermittency–that impact networking, communication, and hardware design, often requiring cross-layer approaches to solve them. We have selected a program that broadly reflects this theme, its key challenges, and broader topics in networking.","PeriodicalId":245782,"journal":{"name":"2020 IEEE 45th Conference on Local Computer Networks (LCN)","volume":"91 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2020-07-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"114485139","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 : 2020-05-19DOI: 10.1109/LCN48667.2020.9314831
A. Dorri, R. Jurdak
Blockchain has received tremendous attention in non-monetary applications including the Internet of Things (IoT) due to its salient features including decentralization, security, auditability, and anonymity. Most conventional blockchains rely on computationally expensive validator selection and consensus algorithms, have limited throughput, and high transaction delays. In this paper, we propose tree-chain a scalable fast blockchain instantiation that introduces two levels of randomization among the validators: i) transaction level where the validator of each transaction is selected randomly based on the most significant characters of the hash function output (known as consensus code), and ii) blockchain level where validator is randomly allocated to a particular consensus code based on the hash of their public key. Tree-chain introduces parallel chain branches where each validator commits the corresponding transactions in a unique ledger.
{"title":"Tree-Chain: A Fast Lightweight Consensus Algorithm for IoT Applications","authors":"A. Dorri, R. Jurdak","doi":"10.1109/LCN48667.2020.9314831","DOIUrl":"https://doi.org/10.1109/LCN48667.2020.9314831","url":null,"abstract":"Blockchain has received tremendous attention in non-monetary applications including the Internet of Things (IoT) due to its salient features including decentralization, security, auditability, and anonymity. Most conventional blockchains rely on computationally expensive validator selection and consensus algorithms, have limited throughput, and high transaction delays. In this paper, we propose tree-chain a scalable fast blockchain instantiation that introduces two levels of randomization among the validators: i) transaction level where the validator of each transaction is selected randomly based on the most significant characters of the hash function output (known as consensus code), and ii) blockchain level where validator is randomly allocated to a particular consensus code based on the hash of their public key. Tree-chain introduces parallel chain branches where each validator commits the corresponding transactions in a unique ledger.","PeriodicalId":245782,"journal":{"name":"2020 IEEE 45th Conference on Local Computer Networks (LCN)","volume":"127 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2020-05-19","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"115201791","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 : 2019-10-01DOI: 10.1109/lcn48667.2020.9314783
{"title":"Best Paper Awards for Prior IEEE Local Computer Networks (LCN) Conferences","authors":"","doi":"10.1109/lcn48667.2020.9314783","DOIUrl":"https://doi.org/10.1109/lcn48667.2020.9314783","url":null,"abstract":"","PeriodicalId":245782,"journal":{"name":"2020 IEEE 45th Conference on Local Computer Networks (LCN)","volume":"10 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2019-10-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"129861589","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 : 2018-10-01DOI: 10.1109/lcnw.2018.8628583
K. Akkaya
Nils Aschenbruck, University of Osnabrück, Germany Joe Bumblis, University of Wisconsin-Stout, USA Ken Christensen, University of South Florida, USA Ehab Elmallah, University of Alberta, Canada Matthias Frank, University of Bonn, Germany Anura Jayasumana, Colorado State University, USA Gary Kessler, Embry-Riddle Aeronautical University, USA Burkhard Stiller, University of Zürich and ETH Zürich, Switzerland Tim Strayer, BBN, USA Damla Turgut, University of Central Florida, USA Mohamed Younis, University of Maryland, Baltimore County, USA
{"title":"LCN Steering Committee","authors":"K. Akkaya","doi":"10.1109/lcnw.2018.8628583","DOIUrl":"https://doi.org/10.1109/lcnw.2018.8628583","url":null,"abstract":"Nils Aschenbruck, University of Osnabrück, Germany Joe Bumblis, University of Wisconsin-Stout, USA Ken Christensen, University of South Florida, USA Ehab Elmallah, University of Alberta, Canada Matthias Frank, University of Bonn, Germany Anura Jayasumana, Colorado State University, USA Gary Kessler, Embry-Riddle Aeronautical University, USA Burkhard Stiller, University of Zürich and ETH Zürich, Switzerland Tim Strayer, BBN, USA Damla Turgut, University of Central Florida, USA Mohamed Younis, University of Maryland, Baltimore County, USA","PeriodicalId":245782,"journal":{"name":"2020 IEEE 45th Conference on Local Computer Networks (LCN)","volume":"50 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2018-10-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"116155537","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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