Pub Date : 2023-10-02DOI: 10.1109/lcn58197.2023.10223330
Jose A. Gonzalez Nuñez, M. Akbaş
{"title":"Demo: Agent-Based Crowdsensing Simulation for Urban Meteorological Data Collection and Hybrid Aerial-Terrestrial Route Determination","authors":"Jose A. Gonzalez Nuñez, M. Akbaş","doi":"10.1109/lcn58197.2023.10223330","DOIUrl":"https://doi.org/10.1109/lcn58197.2023.10223330","url":null,"abstract":"","PeriodicalId":178458,"journal":{"name":"2023 IEEE 48th Conference on Local Computer Networks (LCN)","volume":"27 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-10-02","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"123895377","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 : 2023-10-02DOI: 10.1109/lcn58197.2023.10223401
Eric Lanfer, Sophia Sylvester, Nils Aschenbruck, Martin Atzmueller
{"title":"Leveraging Explainable AI Methods Towards Identifying Classification Issues on IDS Datasets","authors":"Eric Lanfer, Sophia Sylvester, Nils Aschenbruck, Martin Atzmueller","doi":"10.1109/lcn58197.2023.10223401","DOIUrl":"https://doi.org/10.1109/lcn58197.2023.10223401","url":null,"abstract":"","PeriodicalId":178458,"journal":{"name":"2023 IEEE 48th Conference on Local Computer Networks (LCN)","volume":"4 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-10-02","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"128589582","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 : 2023-10-02DOI: 10.1109/lcn58197.2023.10223405
Konrad Wolsing, Antoine Saillard, Elmar Padilla, Jan Bauer
—Roughly two-thirds of our planet is covered with water, and so far, the oceans have predominantly been used at their surface for the global transport of our goods and commodities. Today, there is a rising trend toward subsea infrastructures such as pipelines, telecommunication cables, or wind farms which demands potent vehicles for underwater work. To this end, a new generation of vehicles, large and Extra-Large Unmanned Underwater Vehicles (XLUUVs), is currently being engineered that allow for long-range, remotely controlled, and semi-autonomous missions in the deep sea. However, although these vehicles are already heavily developed and demand state-of-the-art communication technologies to realize their autonomy, no dedicated test and development environments exist for research, e.g., to assess the implications on cybersecurity. Therefore, in this paper, we present XLab-UUV, a virtual testbed for XLUUVs that allows researchers to identify novel challenges, possible bottlenecks, or vulnerabilities, as well as to develop effective technologies, protocols, and procedures.
{"title":"XLab-UUV – A Virtual Testbed for Extra-Large Uncrewed Underwater Vehicles","authors":"Konrad Wolsing, Antoine Saillard, Elmar Padilla, Jan Bauer","doi":"10.1109/lcn58197.2023.10223405","DOIUrl":"https://doi.org/10.1109/lcn58197.2023.10223405","url":null,"abstract":"—Roughly two-thirds of our planet is covered with water, and so far, the oceans have predominantly been used at their surface for the global transport of our goods and commodities. Today, there is a rising trend toward subsea infrastructures such as pipelines, telecommunication cables, or wind farms which demands potent vehicles for underwater work. To this end, a new generation of vehicles, large and Extra-Large Unmanned Underwater Vehicles (XLUUVs), is currently being engineered that allow for long-range, remotely controlled, and semi-autonomous missions in the deep sea. However, although these vehicles are already heavily developed and demand state-of-the-art communication technologies to realize their autonomy, no dedicated test and development environments exist for research, e.g., to assess the implications on cybersecurity. Therefore, in this paper, we present XLab-UUV, a virtual testbed for XLUUVs that allows researchers to identify novel challenges, possible bottlenecks, or vulnerabilities, as well as to develop effective technologies, protocols, and procedures.","PeriodicalId":178458,"journal":{"name":"2023 IEEE 48th Conference on Local Computer Networks (LCN)","volume":"112 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-10-02","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"116392028","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 : 2023-10-02DOI: 10.1109/lcn58197.2023.10223347
Jeroen Pijpker, Stephen James McCombie
{"title":"A Ship Honeynet to Gather Cyber Threat Intelligence for the Maritime Sector","authors":"Jeroen Pijpker, Stephen James McCombie","doi":"10.1109/lcn58197.2023.10223347","DOIUrl":"https://doi.org/10.1109/lcn58197.2023.10223347","url":null,"abstract":"","PeriodicalId":178458,"journal":{"name":"2023 IEEE 48th Conference on Local Computer Networks (LCN)","volume":"12 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-10-02","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"117201314","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 : 2023-10-02DOI: 10.1109/lcn58197.2023.10223319
Xuejian Li, Mingguang Wang
{"title":"Trusted Sharing of Data Under Cloud-Edge-End Collaboration and Its Formal Verification","authors":"Xuejian Li, Mingguang Wang","doi":"10.1109/lcn58197.2023.10223319","DOIUrl":"https://doi.org/10.1109/lcn58197.2023.10223319","url":null,"abstract":"","PeriodicalId":178458,"journal":{"name":"2023 IEEE 48th Conference on Local Computer Networks (LCN)","volume":"6 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-10-02","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"121945345","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 : 2023-10-02DOI: 10.1109/lcn58197.2023.10223382
Nikolas Wintering, Jannis Mast, T. Hänel, Nils Aschenbruck
{"title":"Trade-Off Between Compression and FEC in Image Transmission Over Wifibroadcast","authors":"Nikolas Wintering, Jannis Mast, T. Hänel, Nils Aschenbruck","doi":"10.1109/lcn58197.2023.10223382","DOIUrl":"https://doi.org/10.1109/lcn58197.2023.10223382","url":null,"abstract":"","PeriodicalId":178458,"journal":{"name":"2023 IEEE 48th Conference on Local Computer Networks (LCN)","volume":"24 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-10-02","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"122476738","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 : 2023-10-02DOI: 10.1109/lcn58197.2023.10223369
Gunnar Schneider, Michael Goetz, I. Nissen
{"title":"Design and Implementation of a Gateway Buoy for the Underwater-IoT","authors":"Gunnar Schneider, Michael Goetz, I. Nissen","doi":"10.1109/lcn58197.2023.10223369","DOIUrl":"https://doi.org/10.1109/lcn58197.2023.10223369","url":null,"abstract":"","PeriodicalId":178458,"journal":{"name":"2023 IEEE 48th Conference on Local Computer Networks (LCN)","volume":"105 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-10-02","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"131516217","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 : 2023-10-02DOI: 10.1109/lcn58197.2023.10223403
Hauton Tsang, M. A. Salahuddin, Noura Limam, R. Boutaba
—As cyber threats become increasingly common, automated threat mitigation solutions are more necessary than ever. Conventional threat mitigation frameworks are difficult to tune for different network environments, but frameworks utilizing deep reinforcement learning (RL) have been proven to be an effective approach that can adapt to different networks automatically. Existing RL-based frameworks have shown to be generalizable to different network sizes and threats, and robust to false positives. However, training RL agents for these frameworks can be challenging in a production environment as the training process is time-consuming and disruptive to the production network. Hence, a staging environment is required to effectively train them. In this paper, we propose Meta-ATMoS+, a meta-RL framework for threat mitigation in software-defined networks. We leverage Model-Agnostic Meta-Learning (MAML) to find an initialization for the RL agent that generalizes to a variety of different network configurations. We show that the RL agent with MAML-learned initialization can accomplish few-shot learning on a target network with comparable performance to training on a staging environment. Few-shot learning not only allows the model to be trainable directly in the production environment but also enables human-in-the-loop RL for the mitigation of threats that do not have an easily-definable reward function.
{"title":"Meta-ATMoS+: A Meta-Reinforcement Learning Framework for Threat Mitigation in Software-Defined Networks","authors":"Hauton Tsang, M. A. Salahuddin, Noura Limam, R. Boutaba","doi":"10.1109/lcn58197.2023.10223403","DOIUrl":"https://doi.org/10.1109/lcn58197.2023.10223403","url":null,"abstract":"—As cyber threats become increasingly common, automated threat mitigation solutions are more necessary than ever. Conventional threat mitigation frameworks are difficult to tune for different network environments, but frameworks utilizing deep reinforcement learning (RL) have been proven to be an effective approach that can adapt to different networks automatically. Existing RL-based frameworks have shown to be generalizable to different network sizes and threats, and robust to false positives. However, training RL agents for these frameworks can be challenging in a production environment as the training process is time-consuming and disruptive to the production network. Hence, a staging environment is required to effectively train them. In this paper, we propose Meta-ATMoS+, a meta-RL framework for threat mitigation in software-defined networks. We leverage Model-Agnostic Meta-Learning (MAML) to find an initialization for the RL agent that generalizes to a variety of different network configurations. We show that the RL agent with MAML-learned initialization can accomplish few-shot learning on a target network with comparable performance to training on a staging environment. Few-shot learning not only allows the model to be trainable directly in the production environment but also enables human-in-the-loop RL for the mitigation of threats that do not have an easily-definable reward function.","PeriodicalId":178458,"journal":{"name":"2023 IEEE 48th Conference on Local Computer Networks (LCN)","volume":"83 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-10-02","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"124609935","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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