Pub Date : 2022-09-26DOI: 10.1109/LCN53696.2022.9843597
E. Scheid, M. Franco, Fabian Küffer, Niels Kübler, Pascal Kiechl, B. Stiller
Network Functions Virtualization (NFV) has been a key part of evolving communication systems in the last few years. However, the life-cycle management of Virtual Network Functions (VNF) is still a not trivial task. Blockchains (BC), due to their decentralization and immutability characteristics, together with the automation provided by Smart Contracts (SC), can be employed to enable such automated and trustworthy VNF management.Thus, this paper proposes VeNiCE to automate the deployment and life-cycle management of VNFs using events emitted on SCs. VeNiCE provides automation and auditability by relying on a BC to provide a decentralized approach for VNF management, which performs management actions, such as VNF deployment and deletion, and based on events and communicates with an SC to provide immutable logging of the VNF life-cycle. VeNiCE provides (i) a frontend for user interaction, (ii) a backend implementing the communication with the NFV framework, and (iii) an SC that emits events, stores VNF allocations, and authenticates users. A prototype of VeNiCE was developed and deployed in the Ethereum BC using OpenStack Tacker as an NFV platform. Experiments were conducted in a real-world deployment of such a prototype to analyze the economic costs of using SCs and the time required to process requests by each component of VeNiCE and the BC. Those results obtained show VeNiCE’s feasibility, highlight its benefits achieved with the automation and provide insights on reducing costs by exploring additional BC platforms and different deployment types, which introduce centralization and management concerns.
{"title":"VeNiCE: Enabling Automatic VNF Management based on Smart Contract Events","authors":"E. Scheid, M. Franco, Fabian Küffer, Niels Kübler, Pascal Kiechl, B. Stiller","doi":"10.1109/LCN53696.2022.9843597","DOIUrl":"https://doi.org/10.1109/LCN53696.2022.9843597","url":null,"abstract":"Network Functions Virtualization (NFV) has been a key part of evolving communication systems in the last few years. However, the life-cycle management of Virtual Network Functions (VNF) is still a not trivial task. Blockchains (BC), due to their decentralization and immutability characteristics, together with the automation provided by Smart Contracts (SC), can be employed to enable such automated and trustworthy VNF management.Thus, this paper proposes VeNiCE to automate the deployment and life-cycle management of VNFs using events emitted on SCs. VeNiCE provides automation and auditability by relying on a BC to provide a decentralized approach for VNF management, which performs management actions, such as VNF deployment and deletion, and based on events and communicates with an SC to provide immutable logging of the VNF life-cycle. VeNiCE provides (i) a frontend for user interaction, (ii) a backend implementing the communication with the NFV framework, and (iii) an SC that emits events, stores VNF allocations, and authenticates users. A prototype of VeNiCE was developed and deployed in the Ethereum BC using OpenStack Tacker as an NFV platform. Experiments were conducted in a real-world deployment of such a prototype to analyze the economic costs of using SCs and the time required to process requests by each component of VeNiCE and the BC. Those results obtained show VeNiCE’s feasibility, highlight its benefits achieved with the automation and provide insights on reducing costs by exploring additional BC platforms and different deployment types, which introduce centralization and management concerns.","PeriodicalId":303965,"journal":{"name":"2022 IEEE 47th Conference on Local Computer Networks (LCN)","volume":"135 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":"132218945","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/LCN53696.2022.9843558
Manuel Jiménez-Lázaro, J. Berrocal, J. Galán-Jiménez
A TCAM (Ternary Content-Addressable Memory) is a type of memory used in the flow tables of Software Defined Networking (SDN) nodes. Although these memories are very fast, their size is limited. This has an impact on the number of rules that can be installed, and an inefficient rule management can lead to a degradation of the network quality of service. In this work, an heuristic algorithm named Active Traffic First (ATF) is proposed to efficiently manage the content of the flow tables of the SDN nodes in order to maximize the Global Service Time (GST) of the active flows in the network. The idea behind ATF is adopted by deleting flows that are not being used in case a new flow aims to be served and there is no space available. Experimental results show that ATF outperforms other state-of-the-art solutions by improving GST and reducing re-installations.
TCAM(三元内容可寻址内存)是软件定义网络(SDN)节点流表中使用的一种内存。虽然这些存储器非常快，但它们的大小是有限的。这对可以安装的规则数量有影响，并且规则管理效率低下会导致网络服务质量的降低。本文提出一种主动流量优先(Active Traffic First, ATF)的启发式算法，对SDN节点流表内容进行有效管理，使网络中主动流的全局服务时间(Global Service Time, GST)最大化。ATF背后的思想是通过删除未被使用的流来实现的，以防新流的目标是服务，并且没有可用的空间。实验结果表明，通过提高GST和减少重新安装，ATF优于其他最先进的解决方案。
{"title":"Improving the Global Service Time in SDN Through the Use of the Active Traffic First Approach: A Heuristic Solution","authors":"Manuel Jiménez-Lázaro, J. Berrocal, J. Galán-Jiménez","doi":"10.1109/LCN53696.2022.9843558","DOIUrl":"https://doi.org/10.1109/LCN53696.2022.9843558","url":null,"abstract":"A TCAM (Ternary Content-Addressable Memory) is a type of memory used in the flow tables of Software Defined Networking (SDN) nodes. Although these memories are very fast, their size is limited. This has an impact on the number of rules that can be installed, and an inefficient rule management can lead to a degradation of the network quality of service. In this work, an heuristic algorithm named Active Traffic First (ATF) is proposed to efficiently manage the content of the flow tables of the SDN nodes in order to maximize the Global Service Time (GST) of the active flows in the network. The idea behind ATF is adopted by deleting flows that are not being used in case a new flow aims to be served and there is no space available. Experimental results show that ATF outperforms other state-of-the-art solutions by improving GST and reducing re-installations.","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":"132333492","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/LCN53696.2022.9843450
Ji Li, Chunxiang Gu, Luan Luan, Fushan Wei, Wenfen Liu
Encrypted traffic classification is a key technology for network monitoring and management, and its recent research results are mostly based on deep learning. Due to the difficulty in obtaining sufficient labeled data, few-shot traffic classification has received considerable attention. However, most of the existing results have two defects. First, they are mostly based on the assumption of a labeled base dataset for pre-training. Second, they neglect the problem of unknown traffic discovery under open-set conditions. In this paper, aiming at the problem of few-shot open-set encrypted traffic classification, a corresponding framework FSOSTC is constructed under the condition of unsupervised pre-training. Two data augmentation methods for packet feature map are proposed to assist the pre-training through self-supervised learning, which is combined with parameter fine-tuning, unknown discovery and class extension strategies. Experiments on public datasets verify the effectiveness of FSOSTC. For the few-shot open-set malicious traffic classification task, the CSA reaches 95.41% and the AUROC reaches 0.8664.
{"title":"Few-Shot Open-Set Traffic Classification Based on Self-Supervised Learning","authors":"Ji Li, Chunxiang Gu, Luan Luan, Fushan Wei, Wenfen Liu","doi":"10.1109/LCN53696.2022.9843450","DOIUrl":"https://doi.org/10.1109/LCN53696.2022.9843450","url":null,"abstract":"Encrypted traffic classification is a key technology for network monitoring and management, and its recent research results are mostly based on deep learning. Due to the difficulty in obtaining sufficient labeled data, few-shot traffic classification has received considerable attention. However, most of the existing results have two defects. First, they are mostly based on the assumption of a labeled base dataset for pre-training. Second, they neglect the problem of unknown traffic discovery under open-set conditions. In this paper, aiming at the problem of few-shot open-set encrypted traffic classification, a corresponding framework FSOSTC is constructed under the condition of unsupervised pre-training. Two data augmentation methods for packet feature map are proposed to assist the pre-training through self-supervised learning, which is combined with parameter fine-tuning, unknown discovery and class extension strategies. Experiments on public datasets verify the effectiveness of FSOSTC. For the few-shot open-set malicious traffic classification task, the CSA reaches 95.41% and the AUROC reaches 0.8664.","PeriodicalId":303965,"journal":{"name":"2022 IEEE 47th Conference on Local Computer Networks (LCN)","volume":"46 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":"123723346","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/LCN53696.2022.9843593
T. Dahanayaka, Zhiyi Wang, Guillaume Jourjon, Suranga Seneviratne
Even though end-to-end encryption was introduced to Domain Name System (DNS) communications to ensure user privacy and there is an increase in adoption of DNS over HTTPS (DoH), prior research has demonstrated that encrypted DNS traffic is vulnerable to traffic analysis attacks. However, these attacks were demonstrated under strong assumptions such as handling only closed-set classification or doing only post-event analysis. In this work we demonstrate traffic analysis attacks on DoH without such strong assumptions. We first show the feasibility of website fingerprinting over DoH traffic and present an inline traffic analysis attack that achieve over 90% accuracy using DoH traces of length as short as ten packets. Next, we propose a novel open-set classification method and achieve over 75% accuracy on both closed-set and open-set samples for the open-set scenario. Finally, we demonstrate that the same attack can be performed without any knowledge on the start of the activity.
{"title":"Inline Traffic Analysis Attacks on DNS over HTTPS","authors":"T. Dahanayaka, Zhiyi Wang, Guillaume Jourjon, Suranga Seneviratne","doi":"10.1109/LCN53696.2022.9843593","DOIUrl":"https://doi.org/10.1109/LCN53696.2022.9843593","url":null,"abstract":"Even though end-to-end encryption was introduced to Domain Name System (DNS) communications to ensure user privacy and there is an increase in adoption of DNS over HTTPS (DoH), prior research has demonstrated that encrypted DNS traffic is vulnerable to traffic analysis attacks. However, these attacks were demonstrated under strong assumptions such as handling only closed-set classification or doing only post-event analysis. In this work we demonstrate traffic analysis attacks on DoH without such strong assumptions. We first show the feasibility of website fingerprinting over DoH traffic and present an inline traffic analysis attack that achieve over 90% accuracy using DoH traces of length as short as ten packets. Next, we propose a novel open-set classification method and achieve over 75% accuracy on both closed-set and open-set samples for the open-set scenario. Finally, we demonstrate that the same attack can be performed without any knowledge on the start of the activity.","PeriodicalId":303965,"journal":{"name":"2022 IEEE 47th Conference on Local Computer Networks (LCN)","volume":"12 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":"123377732","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/LCN53696.2022.9843606
P. Teymoori, A. Boukerche, Feng Liang
This paper considers the problem of mobile computation offloading under stochastic wireless channels while task completion times are subject to deadline constraints. Our objective is to conserve energy for the mobile device by making an optimal decision to execute the task either locally or remotely. In the case of computation offloading, we dynamically vary the data transmission rate, in response to channel conditions. The wireless transmission channel is modelled using a Finite-State Markov Chain (FSMC). We formulate the problem of computation offloading as a constrained optimization problem, and develop an online algorithm to derive the optimal offloading policy. Moreover, to reduce the complexity, we estimate a suboptimal solution of the proposed online algorithm by reducing the size of the FSMC with the help of Markovian aggregation. The numerical results indicate that by applying Markovian aggregation, the running time of the algorithm can be significantly reduced without suffering unreasonable performance degradation.
{"title":"Efficient Mobile Computation Offloading over a Finite-State Markovian Channel using Spectral State Aggregation","authors":"P. Teymoori, A. Boukerche, Feng Liang","doi":"10.1109/LCN53696.2022.9843606","DOIUrl":"https://doi.org/10.1109/LCN53696.2022.9843606","url":null,"abstract":"This paper considers the problem of mobile computation offloading under stochastic wireless channels while task completion times are subject to deadline constraints. Our objective is to conserve energy for the mobile device by making an optimal decision to execute the task either locally or remotely. In the case of computation offloading, we dynamically vary the data transmission rate, in response to channel conditions. The wireless transmission channel is modelled using a Finite-State Markov Chain (FSMC). We formulate the problem of computation offloading as a constrained optimization problem, and develop an online algorithm to derive the optimal offloading policy. Moreover, to reduce the complexity, we estimate a suboptimal solution of the proposed online algorithm by reducing the size of the FSMC with the help of Markovian aggregation. The numerical results indicate that by applying Markovian aggregation, the running time of the algorithm can be significantly reduced without suffering unreasonable performance degradation.","PeriodicalId":303965,"journal":{"name":"2022 IEEE 47th Conference on Local Computer Networks (LCN)","volume":"48 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":"129042287","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/LCN53696.2022.9843465
Samuel Kopmann, Hauke Heseding, M. Zitterbart
Fast detection of Distributed Denial of Service attacks is key for establishing appropriate countermeasures in order to protect potential targets. HollywooDDoS applies well-known techniques from movie classification to the challenge of DDoS detection. The proposed approach utilizes a traffic aggregation scheme representing traffic volumes between IP subnets as two-dimensional images, while preserving detection relevant traffic characteristics. These images serve as input for a convolutional neural network, learning IP address space distributions of both background and attack traffic intensities. It is shown that a real-world DDoS attack can be precisely detected on the time scale of milliseconds. We evaluate classification of images without temporal information about attack traffic development to outline the impact of image resolution and aggregation time frames. We then show that attack detection further improves by 17% when utilizing a consecutive series of images capturing traffic dynamics.
{"title":"HollywooDDoS: Detecting Volumetric Attacks in Moving Images of Network Traffic","authors":"Samuel Kopmann, Hauke Heseding, M. Zitterbart","doi":"10.1109/LCN53696.2022.9843465","DOIUrl":"https://doi.org/10.1109/LCN53696.2022.9843465","url":null,"abstract":"Fast detection of Distributed Denial of Service attacks is key for establishing appropriate countermeasures in order to protect potential targets. HollywooDDoS applies well-known techniques from movie classification to the challenge of DDoS detection. The proposed approach utilizes a traffic aggregation scheme representing traffic volumes between IP subnets as two-dimensional images, while preserving detection relevant traffic characteristics. These images serve as input for a convolutional neural network, learning IP address space distributions of both background and attack traffic intensities. It is shown that a real-world DDoS attack can be precisely detected on the time scale of milliseconds. We evaluate classification of images without temporal information about attack traffic development to outline the impact of image resolution and aggregation time frames. We then show that attack detection further improves by 17% when utilizing a consecutive series of images capturing traffic dynamics.","PeriodicalId":303965,"journal":{"name":"2022 IEEE 47th Conference on Local Computer Networks (LCN)","volume":"31 2 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":"123542703","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/LCN53696.2022.9843624
J. Garcia-Luna-Aceves
A new approach to loop-free shortest-path routing is introduced that uses distance vouchers that attest to the acyclic nature of paths. Routers search and find new shortest paths to destinations without ever creating routing loops by trusting updates originated by routers that vouch being closer to destinations. The new approach is shown to converge faster than prior loop-free shortest-path routing methods.
{"title":"Safe, Fast, and Cycle-Free Multi-Path Routing Using Vouchers","authors":"J. Garcia-Luna-Aceves","doi":"10.1109/LCN53696.2022.9843624","DOIUrl":"https://doi.org/10.1109/LCN53696.2022.9843624","url":null,"abstract":"A new approach to loop-free shortest-path routing is introduced that uses distance vouchers that attest to the acyclic nature of paths. Routers search and find new shortest paths to destinations without ever creating routing loops by trusting updates originated by routers that vouch being closer to destinations. The new approach is shown to converge faster than prior loop-free shortest-path routing methods.","PeriodicalId":303965,"journal":{"name":"2022 IEEE 47th Conference on Local Computer Networks (LCN)","volume":"69 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":"121158528","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/LCN53696.2022.9843406
R. Seeliger, Louay Bassbouss
Technology environments are fragmented, hybrid and broadcast merge along online and digital paths, orchestration and distribution of individual content streams are evolving rapidly; DAI, SSAI, CSAI, DAS, targeted advertising and addressable TV are terms with different techniques but at the same time often used for similar use cases. There are standards such as HbbTV and MPEG-DASH available, but they meet these requirements only partially and require enhancements before they can be adopted by the industry. While HbbTV 2.0.3 is the latest version of the specification, it still has limitations in providing a seamless and personalized ad experience across broadcast and broadband. This demo showcases a real world, deployed and almost product ready implementation of Dynamic Ad Substitution for HbbTV Versions 1.5 and 2.0.X showcasing the feasibility for individual content replacements via broadband in broadcast environments.
{"title":"Dynamic Ad Substitution in Hybrid Broadband Broadcast Environments","authors":"R. Seeliger, Louay Bassbouss","doi":"10.1109/LCN53696.2022.9843406","DOIUrl":"https://doi.org/10.1109/LCN53696.2022.9843406","url":null,"abstract":"Technology environments are fragmented, hybrid and broadcast merge along online and digital paths, orchestration and distribution of individual content streams are evolving rapidly; DAI, SSAI, CSAI, DAS, targeted advertising and addressable TV are terms with different techniques but at the same time often used for similar use cases. There are standards such as HbbTV and MPEG-DASH available, but they meet these requirements only partially and require enhancements before they can be adopted by the industry. While HbbTV 2.0.3 is the latest version of the specification, it still has limitations in providing a seamless and personalized ad experience across broadcast and broadband. This demo showcases a real world, deployed and almost product ready implementation of Dynamic Ad Substitution for HbbTV Versions 1.5 and 2.0.X showcasing the feasibility for individual content replacements via broadband in broadcast environments.","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":"127463710","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/LCN53696.2022.9843633
Hsiang-Jen Hong, Sang-Yoon Chang, Xiaobo Zhou
Payment Channel Network (PCN) is a scaling solution for Cryptocurrency networks. We advance the practicality of the PCN multi-path routing by better modeling the system to incorporate the cost of routing fee and the privacy requirement of the channel balance. We design our Auto-Tune algorithm to optimize the routing concerning both the success rate and the routing fee and utilizing the limited channel capacity information (due to the privacy of the PCN user, the channel balance information is withheld). The simulation result shows Auto-Tune outperforms the current PCN implementation based on single-path routing in the success rate. We compare Auto-Tune against the state-of-the-art Flash algorithm, utilizing the channel-balance information, violating the PCN user privacy, and diverging from current implementation practices. Auto-Tune achieves the routing fee close to the optimal fee obtained by Flash, and its success rate is also close to the success rate achieved by Flash.
{"title":"Auto-Tune: Efficient Autonomous Routing for Payment Channel Networks","authors":"Hsiang-Jen Hong, Sang-Yoon Chang, Xiaobo Zhou","doi":"10.1109/LCN53696.2022.9843633","DOIUrl":"https://doi.org/10.1109/LCN53696.2022.9843633","url":null,"abstract":"Payment Channel Network (PCN) is a scaling solution for Cryptocurrency networks. We advance the practicality of the PCN multi-path routing by better modeling the system to incorporate the cost of routing fee and the privacy requirement of the channel balance. We design our Auto-Tune algorithm to optimize the routing concerning both the success rate and the routing fee and utilizing the limited channel capacity information (due to the privacy of the PCN user, the channel balance information is withheld). The simulation result shows Auto-Tune outperforms the current PCN implementation based on single-path routing in the success rate. We compare Auto-Tune against the state-of-the-art Flash algorithm, utilizing the channel-balance information, violating the PCN user privacy, and diverging from current implementation practices. Auto-Tune achieves the routing fee close to the optimal fee obtained by Flash, and its success rate is also close to the success rate achieved by Flash.","PeriodicalId":303965,"journal":{"name":"2022 IEEE 47th Conference on Local Computer Networks (LCN)","volume":"461 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":"125810753","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/LCN53696.2022.9843315
Zakaria Abou El Houda, L. Khoukhi, B. Brik
Attacks against the IoT network are increasing rapidly, leading to an exponential growth in the number of unsecured IoT devices. Existing security mechanisms are facing several issues due to the lack of real-time decisions, high energy consumption, and high time delays. In this context, we propose a novel Low-Latency Fog-based Framework, called FogFed, to secure IoT applications using Fog computing and Federated Learning (FL). The fog brings security mechanisms near IoT devices reducing delays in communication, while FL enables a privacy-aware collaborative learning between IoT while preserving their privacy. FogFed combines two levels of detection, Fog-based IoT attack detection using a binary FL classifier and cloud-based IoT attack detection using a Multiclass FL classifier. The in-depth experiments results with well-known IoT attack/malware using, the UNSW-NB15 datastet, show the significant accuracy (99%) and detection rate (99%), which outperforms centralized ML/DL models, while significantly reducing delays and preserving the privacy.
{"title":"A Low-Latency Fog-based Framework to secure IoT Applications using Collaborative Federated Learning","authors":"Zakaria Abou El Houda, L. Khoukhi, B. Brik","doi":"10.1109/LCN53696.2022.9843315","DOIUrl":"https://doi.org/10.1109/LCN53696.2022.9843315","url":null,"abstract":"Attacks against the IoT network are increasing rapidly, leading to an exponential growth in the number of unsecured IoT devices. Existing security mechanisms are facing several issues due to the lack of real-time decisions, high energy consumption, and high time delays. In this context, we propose a novel Low-Latency Fog-based Framework, called FogFed, to secure IoT applications using Fog computing and Federated Learning (FL). The fog brings security mechanisms near IoT devices reducing delays in communication, while FL enables a privacy-aware collaborative learning between IoT while preserving their privacy. FogFed combines two levels of detection, Fog-based IoT attack detection using a binary FL classifier and cloud-based IoT attack detection using a Multiclass FL classifier. The in-depth experiments results with well-known IoT attack/malware using, the UNSW-NB15 datastet, show the significant accuracy (99%) and detection rate (99%), which outperforms centralized ML/DL models, while significantly reducing delays and preserving the privacy.","PeriodicalId":303965,"journal":{"name":"2022 IEEE 47th Conference on Local Computer Networks (LCN)","volume":"50 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":"130986609","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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