Pub Date : 2022-10-01DOI: 10.1109/FNWF55208.2022.00079
Yijie Tao, Sampath Edirisinghe, Chathurika Ranaweera, C. Lim, A. Nirmalathas, L. Wosinska
While 5G infrastructure is being rapidly rolled out around the world, it is clear that a key strategy to meet the required high speed, ubiquitous connection is via small cell deployment and cell densification. This results in increased complexity in orchestrating and managing the Radio Access Network (RAN). To this end, we proposed a novel Software Defined Networking (SDN)-enabled reconfigurable crosshaul architecture for supporting heterogeneous hauling technologies and enhancing RAN flexibility and robustness. This is achieved by crosshaul control and data plane separation and a novel control plane. In particular, the link failure recovery procedure in the proposed architecture is evaluated to assess the robustness of the network. Simulation results illustrated that the fast recovery time will not interrupt the mobile users' connectivity with RAN. However, mobile users' data plane shows impacts on different RAN protocol layers due to the failure.
{"title":"Link Failure Recovery in SDN-Enabled Reconfigurable 6G Crosshaul Architecture","authors":"Yijie Tao, Sampath Edirisinghe, Chathurika Ranaweera, C. Lim, A. Nirmalathas, L. Wosinska","doi":"10.1109/FNWF55208.2022.00079","DOIUrl":"https://doi.org/10.1109/FNWF55208.2022.00079","url":null,"abstract":"While 5G infrastructure is being rapidly rolled out around the world, it is clear that a key strategy to meet the required high speed, ubiquitous connection is via small cell deployment and cell densification. This results in increased complexity in orchestrating and managing the Radio Access Network (RAN). To this end, we proposed a novel Software Defined Networking (SDN)-enabled reconfigurable crosshaul architecture for supporting heterogeneous hauling technologies and enhancing RAN flexibility and robustness. This is achieved by crosshaul control and data plane separation and a novel control plane. In particular, the link failure recovery procedure in the proposed architecture is evaluated to assess the robustness of the network. Simulation results illustrated that the fast recovery time will not interrupt the mobile users' connectivity with RAN. However, mobile users' data plane shows impacts on different RAN protocol layers due to the failure.","PeriodicalId":300165,"journal":{"name":"2022 IEEE Future Networks World Forum (FNWF)","volume":"18 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2022-10-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"132700695","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 : 2022-10-01DOI: 10.1109/FNWF55208.2022.00029
C. Tremblay, É. Archambault, Rodney G. Wilson, Stewart Clelland, M. Furdek, L. Wosinska
The tremendous traffic growth generated by video, cloud, future 5G and beyond services is compelling network operators to re-think network architectures to ensure flexible and efficient service support. Filterless optical networking based on broadcast-and-select nodes and coherent transceivers is considered as a disruptive approach for delivering network agility in a cost-effective manner. The filterless network concept has been widely studied for terrestrial and submarine applications. In this paper, we explore the suitability of filterless architectures in metropolitan networks through a comparative performance analysis with a conventional metro network based on active switching. The results show that a filterless solution with lower, but adequate, network connectivity can achieve up to 36% lower power consumption and up to 45.4% cost reduction at the expense of a 19% higher spectrum usage, which makes the filterless architecture an attractive alternative for metro network deployments.
{"title":"Agile Metropolitan Filterless Optical Networking","authors":"C. Tremblay, É. Archambault, Rodney G. Wilson, Stewart Clelland, M. Furdek, L. Wosinska","doi":"10.1109/FNWF55208.2022.00029","DOIUrl":"https://doi.org/10.1109/FNWF55208.2022.00029","url":null,"abstract":"The tremendous traffic growth generated by video, cloud, future 5G and beyond services is compelling network operators to re-think network architectures to ensure flexible and efficient service support. Filterless optical networking based on broadcast-and-select nodes and coherent transceivers is considered as a disruptive approach for delivering network agility in a cost-effective manner. The filterless network concept has been widely studied for terrestrial and submarine applications. In this paper, we explore the suitability of filterless architectures in metropolitan networks through a comparative performance analysis with a conventional metro network based on active switching. The results show that a filterless solution with lower, but adequate, network connectivity can achieve up to 36% lower power consumption and up to 45.4% cost reduction at the expense of a 19% higher spectrum usage, which makes the filterless architecture an attractive alternative for metro network deployments.","PeriodicalId":300165,"journal":{"name":"2022 IEEE Future Networks World Forum (FNWF)","volume":"46 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2022-10-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"133067280","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 : 2022-10-01DOI: 10.1109/FNWF55208.2022.00036
Arpit Tripathi, A. Thakur, T. B. Reddy
Private Networks (also known as Non-Public Net-works) bring significant benefits to Industry 4.0. These networks are typically deployed on-premises of the enterprises, and their isolation from the public (consumer) networks improves the crucial aspects of security and reliability. Despite the isolation, insider attacks can be mounted on these networks. This paper analyses such attacks using attack patterns from Common Attack Pattern Enumerations and Classifications (CAPEC) database. The analysis uses attack graphs, to combine individual domains, in the context of human, device, and network vulner-abilities. The attack graphs help identify paths, the cumulative impact on the system, and possible defense techniques, including security controls to mitigate the impact. Using three sample attack graphs in the context of standalone private 5G networks, this paper analyses possible security mechanisms and captures the difference among legacy enterprise networks (including WiFi for limited mobility), public networks, and private networks.
{"title":"Attack Graphs for Standalone Non-Public 5G Networks","authors":"Arpit Tripathi, A. Thakur, T. B. Reddy","doi":"10.1109/FNWF55208.2022.00036","DOIUrl":"https://doi.org/10.1109/FNWF55208.2022.00036","url":null,"abstract":"Private Networks (also known as Non-Public Net-works) bring significant benefits to Industry 4.0. These networks are typically deployed on-premises of the enterprises, and their isolation from the public (consumer) networks improves the crucial aspects of security and reliability. Despite the isolation, insider attacks can be mounted on these networks. This paper analyses such attacks using attack patterns from Common Attack Pattern Enumerations and Classifications (CAPEC) database. The analysis uses attack graphs, to combine individual domains, in the context of human, device, and network vulner-abilities. The attack graphs help identify paths, the cumulative impact on the system, and possible defense techniques, including security controls to mitigate the impact. Using three sample attack graphs in the context of standalone private 5G networks, this paper analyses possible security mechanisms and captures the difference among legacy enterprise networks (including WiFi for limited mobility), public networks, and private networks.","PeriodicalId":300165,"journal":{"name":"2022 IEEE Future Networks World Forum (FNWF)","volume":"100 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2022-10-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"131561756","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 : 2022-10-01DOI: 10.1109/FNWF55208.2022.00050
Zakaria Abou El Houda, Diala Naboulsi, Georges Kaddoum
Advances in Artificial Intelligence (AI) provide new capabilities to handle network routing problems. However, the lack of up-to-date training data, slow convergence, and low robustness due to the dynamic change of the network topology, makes these AI-based routing systems inefficient. To address this problem, Reinforcement Learning (RL) has been introduced to design more flexible and robust network routing protocols. However, the amount of data ($i$. e., state-action space) shared be- tween agents, in a Multi-Agent Reinforcement Learning (MARL) setup, can consume network bandwidth and may slow down the process of training. Moreover, the curse of dimensionality of RL encompasses the exponential growth of the discrete state-action space, thus limiting its potential benefit. In this paper, we present a novel approach combining Federated Learning (FL) with Deep Reinforcement Learning (D RL) in order to ensure an effective network routing in wireless environment. First, we formalize the problem of network routing as a problem of RL, where multiple agents that are geographically distributed train the policy model in a fully distributed manner. Thus, each agent can quickly obtain the optimal policy that maximizes the cumulative expected reward, while preserving the privacy of each agent's data. Experiments results show that our proposed Federated Reinforcement Learning (FRL) approach is robust and effective.
{"title":"Cost-efficient Federated Reinforcement Learning- Based Network Routing for Wireless Networks","authors":"Zakaria Abou El Houda, Diala Naboulsi, Georges Kaddoum","doi":"10.1109/FNWF55208.2022.00050","DOIUrl":"https://doi.org/10.1109/FNWF55208.2022.00050","url":null,"abstract":"Advances in Artificial Intelligence (AI) provide new capabilities to handle network routing problems. However, the lack of up-to-date training data, slow convergence, and low robustness due to the dynamic change of the network topology, makes these AI-based routing systems inefficient. To address this problem, Reinforcement Learning (RL) has been introduced to design more flexible and robust network routing protocols. However, the amount of data ($i$. e., state-action space) shared be- tween agents, in a Multi-Agent Reinforcement Learning (MARL) setup, can consume network bandwidth and may slow down the process of training. Moreover, the curse of dimensionality of RL encompasses the exponential growth of the discrete state-action space, thus limiting its potential benefit. In this paper, we present a novel approach combining Federated Learning (FL) with Deep Reinforcement Learning (D RL) in order to ensure an effective network routing in wireless environment. First, we formalize the problem of network routing as a problem of RL, where multiple agents that are geographically distributed train the policy model in a fully distributed manner. Thus, each agent can quickly obtain the optimal policy that maximizes the cumulative expected reward, while preserving the privacy of each agent's data. Experiments results show that our proposed Federated Reinforcement Learning (FRL) approach is robust and effective.","PeriodicalId":300165,"journal":{"name":"2022 IEEE Future Networks World Forum (FNWF)","volume":"7 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2022-10-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"132919105","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 : 2022-10-01DOI: 10.1109/FNWF55208.2022.00022
S. Swain, Ashit Subudhi
With more subscribers and a variety in business use cases relying on 5G New Radio (NR) network infrastructure, the access network needs to be scalable. The initial network access procedure comprises of users sending preambles to gNB for granting uplink (UL) resources. However, limited preambles in 5G NR can be a bottleneck on the performance of network access procedures. Preamble collisions during initial Random Access Channel (RACH) procedure can limit the scalability of the network. With the increase in the number of cellular User Equipments (UEs) and Machine Type Communication (MTC) devices, the probability of such preamble collisions further increases, thereby leading to reattempts by UEs. This in turn, results in increased latency and reduced channel utilization. In this work, we have used hash functions for selecting preambles during the RACH process. We have used modulo hash functions to convert device identifiers into preamble indexes such that the collision is reduced. In order to counter collisions while using hash functions, we have adopted standard collision resolution techniques such as linear probing, quadratic probing, and double hashing. On performing extensive simulations, it is observed that the hashing based access scheme performs better than the standard Access Class Barring (ACB) scheme in effectively reducing the number of collisions thereby empowering more users to access the network.
{"title":"A Novel RACH Scheme for Efficient Access in 5G and Beyond Networks using Hash Function","authors":"S. Swain, Ashit Subudhi","doi":"10.1109/FNWF55208.2022.00022","DOIUrl":"https://doi.org/10.1109/FNWF55208.2022.00022","url":null,"abstract":"With more subscribers and a variety in business use cases relying on 5G New Radio (NR) network infrastructure, the access network needs to be scalable. The initial network access procedure comprises of users sending preambles to gNB for granting uplink (UL) resources. However, limited preambles in 5G NR can be a bottleneck on the performance of network access procedures. Preamble collisions during initial Random Access Channel (RACH) procedure can limit the scalability of the network. With the increase in the number of cellular User Equipments (UEs) and Machine Type Communication (MTC) devices, the probability of such preamble collisions further increases, thereby leading to reattempts by UEs. This in turn, results in increased latency and reduced channel utilization. In this work, we have used hash functions for selecting preambles during the RACH process. We have used modulo hash functions to convert device identifiers into preamble indexes such that the collision is reduced. In order to counter collisions while using hash functions, we have adopted standard collision resolution techniques such as linear probing, quadratic probing, and double hashing. On performing extensive simulations, it is observed that the hashing based access scheme performs better than the standard Access Class Barring (ACB) scheme in effectively reducing the number of collisions thereby empowering more users to access the network.","PeriodicalId":300165,"journal":{"name":"2022 IEEE Future Networks World Forum (FNWF)","volume":"24 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2022-10-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"122190415","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 : 2022-10-01DOI: 10.1109/FNWF55208.2022.00117
Md Sajid Khan, Behnam Farzaneh, Nashid Shahriar, Niloy Saha, R. Boutaba
5G Network slicing is one of the key enabling technologies that offer dedicated logical resources to different applications on the same physical network. However, a Denial-of-Service (DoS) or Distributed Denial-of-Service (DDoS) attack can severely damage the performance and functionality of network slices. Furthermore, recent DoS/DDoS attack detection techniques are based on the available data sets which are collected from simulated 5G networks rather than from 5G network slices. In this paper, we first show how DoS/DDoS attacks on network slices can impact slice users' performance metrics such as bandwidth and latency. Then, we present a novel DoS/DDoS attack dataset collected from a simulated 5G network slicing test bed. Finally, we showed a deep-learning-based bidirectional LSTM (Long Short Term Memory) model, namely, SliceSecure can detect DoS/DDoS attacks with an accuracy of 99.99% on the newly created data sets for 5G network slices.
{"title":"SliceSecure: Impact and Detection of DoS/DDoS Attacks on 5G Network Slices","authors":"Md Sajid Khan, Behnam Farzaneh, Nashid Shahriar, Niloy Saha, R. Boutaba","doi":"10.1109/FNWF55208.2022.00117","DOIUrl":"https://doi.org/10.1109/FNWF55208.2022.00117","url":null,"abstract":"5G Network slicing is one of the key enabling technologies that offer dedicated logical resources to different applications on the same physical network. However, a Denial-of-Service (DoS) or Distributed Denial-of-Service (DDoS) attack can severely damage the performance and functionality of network slices. Furthermore, recent DoS/DDoS attack detection techniques are based on the available data sets which are collected from simulated 5G networks rather than from 5G network slices. In this paper, we first show how DoS/DDoS attacks on network slices can impact slice users' performance metrics such as bandwidth and latency. Then, we present a novel DoS/DDoS attack dataset collected from a simulated 5G network slicing test bed. Finally, we showed a deep-learning-based bidirectional LSTM (Long Short Term Memory) model, namely, SliceSecure can detect DoS/DDoS attacks with an accuracy of 99.99% on the newly created data sets for 5G network slices.","PeriodicalId":300165,"journal":{"name":"2022 IEEE Future Networks World Forum (FNWF)","volume":"112 ","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2022-10-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"114003888","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 : 2022-10-01DOI: 10.1109/FNWF55208.2022.00088
Praveen Gupta
Managed Access Systems (MAS) are used extensively in correctional facilities such as prisons in both the US and abroad to restrict cellular access within the facility to authorized devices. As cellular radio-access technologies evolve through 4G into 5G and beyond, there is a corresponding demand to evolve the MAS architecture to prosecute these technologies. This paper describes key technology ideas which can be leveraged to effectively architect a future-proof MAS solution to support correctional-facility needs as well as support new use cases for the emergent In-Building Wireless Managed-Access market.
{"title":"NextG Managed Access Systems (N-MAS) for Correctional-Facility Markets","authors":"Praveen Gupta","doi":"10.1109/FNWF55208.2022.00088","DOIUrl":"https://doi.org/10.1109/FNWF55208.2022.00088","url":null,"abstract":"Managed Access Systems (MAS) are used extensively in correctional facilities such as prisons in both the US and abroad to restrict cellular access within the facility to authorized devices. As cellular radio-access technologies evolve through 4G into 5G and beyond, there is a corresponding demand to evolve the MAS architecture to prosecute these technologies. This paper describes key technology ideas which can be leveraged to effectively architect a future-proof MAS solution to support correctional-facility needs as well as support new use cases for the emergent In-Building Wireless Managed-Access market.","PeriodicalId":300165,"journal":{"name":"2022 IEEE Future Networks World Forum (FNWF)","volume":"9 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2022-10-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"126233005","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 : 2022-10-01DOI: 10.1109/FNWF55208.2022.00062
S. Parsaeefard, A. Leon-Garcia
6G networks will greatly expand the support for data-oriented, autonomous applications for over the top (OTT) and networking use cases. The success of these use cases will depend on the availability of big data sets which is not practical in many real scenarios due to the highly dynamic behavior of systems and the cost of data collection procedures. Transfer learning (TL) is a promising approach to deal with these challenges through the sharing of knowledge among diverse learning algorithms. with TL, the learning rate and learning accuracy can be considerably improved. There are implementation challenges to efficiently deploy and utilize TL in 6G. Here, we initiate this discussion by providing some performance metrics to measure the TL success. We show how infrastructure, application, management, and training planes of 6G can be adapted to handle TL. We provide examples of TL in 6G and highlight the spatio-temporal features of data in 6G that can lead to efficient TL. By simulations, we demonstrate how transferring the quantized neural network weights between two use cases can make a trade-off between overheads and performance and attain more efficient TL in 6G.
{"title":"Efficient Transfer Learning in 6G","authors":"S. Parsaeefard, A. Leon-Garcia","doi":"10.1109/FNWF55208.2022.00062","DOIUrl":"https://doi.org/10.1109/FNWF55208.2022.00062","url":null,"abstract":"6G networks will greatly expand the support for data-oriented, autonomous applications for over the top (OTT) and networking use cases. The success of these use cases will depend on the availability of big data sets which is not practical in many real scenarios due to the highly dynamic behavior of systems and the cost of data collection procedures. Transfer learning (TL) is a promising approach to deal with these challenges through the sharing of knowledge among diverse learning algorithms. with TL, the learning rate and learning accuracy can be considerably improved. There are implementation challenges to efficiently deploy and utilize TL in 6G. Here, we initiate this discussion by providing some performance metrics to measure the TL success. We show how infrastructure, application, management, and training planes of 6G can be adapted to handle TL. We provide examples of TL in 6G and highlight the spatio-temporal features of data in 6G that can lead to efficient TL. By simulations, we demonstrate how transferring the quantized neural network weights between two use cases can make a trade-off between overheads and performance and attain more efficient TL in 6G.","PeriodicalId":300165,"journal":{"name":"2022 IEEE Future Networks World Forum (FNWF)","volume":"29 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2022-10-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"126974673","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 : 2022-10-01DOI: 10.1109/FNWF55208.2022.00089
Girma M. Yilma, Nina Slamnik-Kriještorac, M. Liebsch, A. Francescon, J. Márquez-Barja
One of the major challenges in 5G-based Cooperative Connected and Automated Mobility is to ensure continuity of a service that is deployed on the network edge and used by a moving vehicle. We propose enablers for smart cellular edges, which support service continuity in cross-border scenarios by the timely preparation of a service instance in an anticipated topologically closer target edge, and by connecting the vehicle to such service instance before the cellular handover occurs. In this paper, we use the edge data centers of a German and Austrian mobile operator to showcase two main enabling pillars for edge service continuity, i.e., i) transparent edge bridging by means of a programmable data plane to serve a vehicle from the target edge before the vehicle performs handover to a different operator, and ii) smart applications, which apply data analytics to boost orchestration decisions for target edge preparation.
{"title":"No Limits – Smart Cellular Edges for Cross-Border Continuity of Automotive Services","authors":"Girma M. Yilma, Nina Slamnik-Kriještorac, M. Liebsch, A. Francescon, J. Márquez-Barja","doi":"10.1109/FNWF55208.2022.00089","DOIUrl":"https://doi.org/10.1109/FNWF55208.2022.00089","url":null,"abstract":"One of the major challenges in 5G-based Cooperative Connected and Automated Mobility is to ensure continuity of a service that is deployed on the network edge and used by a moving vehicle. We propose enablers for smart cellular edges, which support service continuity in cross-border scenarios by the timely preparation of a service instance in an anticipated topologically closer target edge, and by connecting the vehicle to such service instance before the cellular handover occurs. In this paper, we use the edge data centers of a German and Austrian mobile operator to showcase two main enabling pillars for edge service continuity, i.e., i) transparent edge bridging by means of a programmable data plane to serve a vehicle from the target edge before the vehicle performs handover to a different operator, and ii) smart applications, which apply data analytics to boost orchestration decisions for target edge preparation.","PeriodicalId":300165,"journal":{"name":"2022 IEEE Future Networks World Forum (FNWF)","volume":"1 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2022-10-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"128941539","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 : 2022-10-01DOI: 10.1109/FNWF55208.2022.00021
Shengli Yuan, Randy Phan-Huynh
As an emerging technology, IoT is rapidly revolutionizing the global communication network with billions of new devices deployed and connected with each other. Many of these devices collect and transfer a large amount of sensitive or mission critical data, making security a top priority. Compared to traditional Internet, IoT networks often operate in open and harsh environment, and may experience frequent delays, traffic loss and attacks; Meanwhile, IoT devices are often severally constrained in computational power, storage space, network bandwidth, and power supply, which prevent them from deploying traditional security schemes. Authentication is an important security mechanism that can be used to identify devices or users. Due to resource constrains of IoT networks, it is highly desirable for the authentication scheme to be lightweight while also being highly effective. In this paper, we developed and evaluated a hash-chain-based multi-node mutual authentication algorithm. Nodes on a network all share a common secret key and broadcast to other nodes in range. Each node may also add to the hash chain and rebroadcast, which will be used to authenticate all nodes in the network. This algorithm has a linear running time and complexity of $O(n)$, a significant improvement from the $O(n^{2})$ running time and complexity of the traditional pairwise multi-node mutual authentication.
{"title":"A Lightweight Hash-Chain-Based Multi-Node Mutual Authentication Algorithm for IoT Networks","authors":"Shengli Yuan, Randy Phan-Huynh","doi":"10.1109/FNWF55208.2022.00021","DOIUrl":"https://doi.org/10.1109/FNWF55208.2022.00021","url":null,"abstract":"As an emerging technology, IoT is rapidly revolutionizing the global communication network with billions of new devices deployed and connected with each other. Many of these devices collect and transfer a large amount of sensitive or mission critical data, making security a top priority. Compared to traditional Internet, IoT networks often operate in open and harsh environment, and may experience frequent delays, traffic loss and attacks; Meanwhile, IoT devices are often severally constrained in computational power, storage space, network bandwidth, and power supply, which prevent them from deploying traditional security schemes. Authentication is an important security mechanism that can be used to identify devices or users. Due to resource constrains of IoT networks, it is highly desirable for the authentication scheme to be lightweight while also being highly effective. In this paper, we developed and evaluated a hash-chain-based multi-node mutual authentication algorithm. Nodes on a network all share a common secret key and broadcast to other nodes in range. Each node may also add to the hash chain and rebroadcast, which will be used to authenticate all nodes in the network. This algorithm has a linear running time and complexity of $O(n)$, a significant improvement from the $O(n^{2})$ running time and complexity of the traditional pairwise multi-node mutual authentication.","PeriodicalId":300165,"journal":{"name":"2022 IEEE Future Networks World Forum (FNWF)","volume":"37 22","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2022-10-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"120855520","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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