Pub Date : 2020-07-01DOI: 10.1109/INFOCOMWKSHPS50562.2020.9162910
Hanxun Ren, Qinghe Du, Yijie Ou, Pinyi Ren
The conventional fountain-coding aided strategies realize transmission secrecy by ensuring the legitimate user to receive the sufficient number of fountain-coded packets ahead of the eavesdropper does, which emphasise on the security of entire file. However, in the process of communication a fraction of confidential message will be recovered by parts of correct decoding of intercepted packets at the eavesdropper, which is forbidden for some scenarios with stringent security requirement. To solve the above mentioned issue, we propose a secure transmission scheme via cross-locking between the fountain-coded data and the codebook information, in which the generating matrix of fountain codes is implicitly transferred between legitimate pairs such that the inter-data codebook information can be protected. Specifically, the transmitter exploits fountain-coded packets legitimate user received to encrypt the key associated with the generating matrix to prevent eavesdropper from decoding any coded packets, thereby the secure transmission is guaranteed. A wireless secure transmission system based on the proposed scheme is developed on the software radio platform and its security performance is evaluated in actual scenarios. The experimental results prove that the proposed scheme outperforms the comparison schemes in the matter of the intercept probability and the recovering proportion for eavesdropper.
{"title":"Fountain-Coding-Aided Secure Delivery via Cross-locking Between Payload Data and Control Information","authors":"Hanxun Ren, Qinghe Du, Yijie Ou, Pinyi Ren","doi":"10.1109/INFOCOMWKSHPS50562.2020.9162910","DOIUrl":"https://doi.org/10.1109/INFOCOMWKSHPS50562.2020.9162910","url":null,"abstract":"The conventional fountain-coding aided strategies realize transmission secrecy by ensuring the legitimate user to receive the sufficient number of fountain-coded packets ahead of the eavesdropper does, which emphasise on the security of entire file. However, in the process of communication a fraction of confidential message will be recovered by parts of correct decoding of intercepted packets at the eavesdropper, which is forbidden for some scenarios with stringent security requirement. To solve the above mentioned issue, we propose a secure transmission scheme via cross-locking between the fountain-coded data and the codebook information, in which the generating matrix of fountain codes is implicitly transferred between legitimate pairs such that the inter-data codebook information can be protected. Specifically, the transmitter exploits fountain-coded packets legitimate user received to encrypt the key associated with the generating matrix to prevent eavesdropper from decoding any coded packets, thereby the secure transmission is guaranteed. A wireless secure transmission system based on the proposed scheme is developed on the software radio platform and its security performance is evaluated in actual scenarios. The experimental results prove that the proposed scheme outperforms the comparison schemes in the matter of the intercept probability and the recovering proportion for eavesdropper.","PeriodicalId":104136,"journal":{"name":"IEEE INFOCOM 2020 - IEEE Conference on Computer Communications Workshops (INFOCOM WKSHPS)","volume":"1 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":"130117594","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/INFOCOMWKSHPS50562.2020.9162965
Sixian Sun, Xiao Fu, B. Luo, Xiaojiang Du
Cloud computing is making a greater impact on internet industry, medical industry, insurance industry, and so on. Due to its influence, cloud computing networking is in great need of security, and protecting cloud environment from diverse attacks has been a hot issue. On the other hand, Software Defined Network (SDN) separates the control plane from the data plane and makes networks programmable, which promotes the centralized management of network devices. Compared to traditional networks, SDN increases the utilization efficiency of resources, increases the flexibility of network services, and reduces the cost of maintenance. Therefore, in this paper, we apply SDN to protect cloud computing networking from Address Resolution Protocol (ARP) attacks. In the proposed approach, a cluster of controllers detects ARP packets that hosts send, in order to find out the forged ones and to prevent ARP spoofing attacks. Also, controllers monitor statistical data of ARP packets once in a while to detect ARP flooding attacks. Once an attack is detected, controllers install flow entries on corresponding switches, to block flow for a specific time. Finally, we conduct experiments to show that our approach is useful to detect and mitigate ARP attacks in SDN-based cloud environment.
{"title":"Detecting and Mitigating ARP Attacks in SDN-Based Cloud Environment","authors":"Sixian Sun, Xiao Fu, B. Luo, Xiaojiang Du","doi":"10.1109/INFOCOMWKSHPS50562.2020.9162965","DOIUrl":"https://doi.org/10.1109/INFOCOMWKSHPS50562.2020.9162965","url":null,"abstract":"Cloud computing is making a greater impact on internet industry, medical industry, insurance industry, and so on. Due to its influence, cloud computing networking is in great need of security, and protecting cloud environment from diverse attacks has been a hot issue. On the other hand, Software Defined Network (SDN) separates the control plane from the data plane and makes networks programmable, which promotes the centralized management of network devices. Compared to traditional networks, SDN increases the utilization efficiency of resources, increases the flexibility of network services, and reduces the cost of maintenance. Therefore, in this paper, we apply SDN to protect cloud computing networking from Address Resolution Protocol (ARP) attacks. In the proposed approach, a cluster of controllers detects ARP packets that hosts send, in order to find out the forged ones and to prevent ARP spoofing attacks. Also, controllers monitor statistical data of ARP packets once in a while to detect ARP flooding attacks. Once an attack is detected, controllers install flow entries on corresponding switches, to block flow for a specific time. Finally, we conduct experiments to show that our approach is useful to detect and mitigate ARP attacks in SDN-based cloud environment.","PeriodicalId":104136,"journal":{"name":"IEEE INFOCOM 2020 - IEEE Conference on Computer Communications Workshops (INFOCOM WKSHPS)","volume":"7 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":"130269377","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/INFOCOMWKSHPS50562.2020.9162766
Sabarish Krishna Moorthy, Zhangyu Guan
Terahertz (THz)-band communication is a key technology to achieve ultra-high-data-rate wireless links in beyond-5G wireless networks. A main challenge with this frequency band is that the wireless links can be easily disconnected because of beam misalignment in mobile environments. This paper focuses on beam control in THz-band wireless Unmanned Aerial Vehicle (UAV) networks, where perfect beam alignment is hard to achieve in the presence of multi-scale mobility uncertainties of the flying UAVs. We propose a learning-based stochastic beam control scheme called LeTera to reduce the outage probability of the THz-band wireless links based on Echo State Networks (ESN). The scheme dynamically predicts through echo state learning the best beam width based on statistical information of the UAV mobility pattern. The scheme is evaluated using mobility traces collected through a series of UAV flight field experiments in different weather. Results show that LeTera can predict the optimal beam width with 99% accuracy and nearly optimal link capacity can be achieved in the presence of beam alignment latency.
{"title":"LeTera: Stochastic Beam Control Through ESN Learning in Terahertz-Band Wireless UAV Networks","authors":"Sabarish Krishna Moorthy, Zhangyu Guan","doi":"10.1109/INFOCOMWKSHPS50562.2020.9162766","DOIUrl":"https://doi.org/10.1109/INFOCOMWKSHPS50562.2020.9162766","url":null,"abstract":"Terahertz (THz)-band communication is a key technology to achieve ultra-high-data-rate wireless links in beyond-5G wireless networks. A main challenge with this frequency band is that the wireless links can be easily disconnected because of beam misalignment in mobile environments. This paper focuses on beam control in THz-band wireless Unmanned Aerial Vehicle (UAV) networks, where perfect beam alignment is hard to achieve in the presence of multi-scale mobility uncertainties of the flying UAVs. We propose a learning-based stochastic beam control scheme called LeTera to reduce the outage probability of the THz-band wireless links based on Echo State Networks (ESN). The scheme dynamically predicts through echo state learning the best beam width based on statistical information of the UAV mobility pattern. The scheme is evaluated using mobility traces collected through a series of UAV flight field experiments in different weather. Results show that LeTera can predict the optimal beam width with 99% accuracy and nearly optimal link capacity can be achieved in the presence of beam alignment latency.","PeriodicalId":104136,"journal":{"name":"IEEE INFOCOM 2020 - IEEE Conference on Computer Communications Workshops (INFOCOM WKSHPS)","volume":"93 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":"131654389","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/INFOCOMWKSHPS50562.2020.9162699
Chenming Yang, Liang Zhou, Hui Wen, Yue Wu
Semi-supervised machine learning models, especially deep neural networks, have been widely used in network anomaly detection for their capability of capturing patterns in normal data. However, the models face security challenges when an attacker has obtained their full details. In this paper, we propose a universal adversarial sample generator (U-ASG), to perform white-box adversarial attacks on autoencoder-based semi-supervised network anomaly detection (SSNAD) systems. The purpose of adversarial attacks is to generate small adversarial perturbations and add them to targeted anomalous samples to fly under the radar. We model the generation process of adversarial perturbations as an optimization problem, in which we minimize the reconstruction errors of the adversarial samples through the trained autoencoder and approximate it to solve. Furthermore, to improve the attack performance against the variational autoencoder (VAE), which is robust to tiny perturbations through uncertainty modeling, we design a mechanism to weaken its robustness by introducing a variance regularizer to the optimization. Simulation results show that the adversarial attacks generated by our U-ASG can effectively degrade the performance of the autoencoder-based SSNAD systems.
{"title":"U-ASG: A Universal Method to Perform Adversarial Attack on Autoencoder based Network Anomaly Detection Systems","authors":"Chenming Yang, Liang Zhou, Hui Wen, Yue Wu","doi":"10.1109/INFOCOMWKSHPS50562.2020.9162699","DOIUrl":"https://doi.org/10.1109/INFOCOMWKSHPS50562.2020.9162699","url":null,"abstract":"Semi-supervised machine learning models, especially deep neural networks, have been widely used in network anomaly detection for their capability of capturing patterns in normal data. However, the models face security challenges when an attacker has obtained their full details. In this paper, we propose a universal adversarial sample generator (U-ASG), to perform white-box adversarial attacks on autoencoder-based semi-supervised network anomaly detection (SSNAD) systems. The purpose of adversarial attacks is to generate small adversarial perturbations and add them to targeted anomalous samples to fly under the radar. We model the generation process of adversarial perturbations as an optimization problem, in which we minimize the reconstruction errors of the adversarial samples through the trained autoencoder and approximate it to solve. Furthermore, to improve the attack performance against the variational autoencoder (VAE), which is robust to tiny perturbations through uncertainty modeling, we design a mechanism to weaken its robustness by introducing a variance regularizer to the optimization. Simulation results show that the adversarial attacks generated by our U-ASG can effectively degrade the performance of the autoencoder-based SSNAD systems.","PeriodicalId":104136,"journal":{"name":"IEEE INFOCOM 2020 - IEEE Conference on Computer Communications Workshops (INFOCOM WKSHPS)","volume":"9 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":"131116999","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}
Since Bitcoin's seminal work of achieving consensus under the assumption that more than 51% computational power is honest, many researchers have proposed various kinds of consensus protocols. In recent years, there has been a trend in designing proof of stake (PoS) based consensus protocols. Unfortunately, PoS based consensus protocols are inherently not secure and are vulnerable to “long range attacks”. Thus PoS based blockchains have the potential risk of being forked with a minimal cost for double spending. In this paper, we propose a PoS protocol that is secure against long range attack based double spending. The security is achieved using secure randomness beacons generated by tamper proof hardware modules.
{"title":"Sperax: An Approach To Defeat Long Range Attacks In Blockchains","authors":"Yongge Wang, Jingwen Sun, Xin Wang, Yunchuan Wei, Hao Wu, Zhou Yu, Gillian Chu","doi":"10.1109/INFOCOMWKSHPS50562.2020.9163036","DOIUrl":"https://doi.org/10.1109/INFOCOMWKSHPS50562.2020.9163036","url":null,"abstract":"Since Bitcoin's seminal work of achieving consensus under the assumption that more than 51% computational power is honest, many researchers have proposed various kinds of consensus protocols. In recent years, there has been a trend in designing proof of stake (PoS) based consensus protocols. Unfortunately, PoS based consensus protocols are inherently not secure and are vulnerable to “long range attacks”. Thus PoS based blockchains have the potential risk of being forked with a minimal cost for double spending. In this paper, we propose a PoS protocol that is secure against long range attack based double spending. The security is achieved using secure randomness beacons generated by tamper proof hardware modules.","PeriodicalId":104136,"journal":{"name":"IEEE INFOCOM 2020 - IEEE Conference on Computer Communications Workshops (INFOCOM WKSHPS)","volume":"35 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":"133673360","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/infocomwkshps50562.2020.9162732
G. Sviridov, Cedric Beliard, G. Simon, A. Bianco, P. Giaccone, Dario Rossi
Quality of Experience (QoE) assessment in video games is notorious for its burdensomeness. Employing human subjects to understand network impact on the perceived gaming QoE presents major drawbacks in terms of resources requirement, results interpretability and poor transferability across different games. To overcome these shortcomings, we propose to substitute human players with artificial agents trained with state-of-the-art Deep Reinforcement Learning techniques. Equivalently to traditional QoE assessment, we measure the in-game score achieved by an artificial agent for the game of Doom for varying network parameters. Our results show that the proposed methodology can be applied to understand fine-grained impact of network conditions on gaming experience while opening a lot of new opportunities for network operators and game developers.
{"title":"Demo abstract: Leveraging AI players for QoE estimation in cloud gaming","authors":"G. Sviridov, Cedric Beliard, G. Simon, A. Bianco, P. Giaccone, Dario Rossi","doi":"10.1109/infocomwkshps50562.2020.9162732","DOIUrl":"https://doi.org/10.1109/infocomwkshps50562.2020.9162732","url":null,"abstract":"Quality of Experience (QoE) assessment in video games is notorious for its burdensomeness. Employing human subjects to understand network impact on the perceived gaming QoE presents major drawbacks in terms of resources requirement, results interpretability and poor transferability across different games. To overcome these shortcomings, we propose to substitute human players with artificial agents trained with state-of-the-art Deep Reinforcement Learning techniques. Equivalently to traditional QoE assessment, we measure the in-game score achieved by an artificial agent for the game of Doom for varying network parameters. Our results show that the proposed methodology can be applied to understand fine-grained impact of network conditions on gaming experience while opening a lot of new opportunities for network operators and game developers.","PeriodicalId":104136,"journal":{"name":"IEEE INFOCOM 2020 - IEEE Conference on Computer Communications Workshops (INFOCOM WKSHPS)","volume":"12 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":"131442987","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/INFOCOMWKSHPS50562.2020.9162735
Dawid Połap, Gautam Srivastava, A. Jolfaei, R. Parizi
In today's technological climate, users require fast automation and digitization of results for large amounts of data at record speeds. Especially in the field of medicine, where each patient is often asked to undergo many different examinations within one diagnosis or treatment. Each examination can help in the diagnosis or prediction of further disease progression. Furthermore, all produced data from these examinations must be stored somewhere and available to various medical practitioners for analysis who may be in geographically diverse locations. The current medical climate leans towards remote patient monitoring and AI-assisted diagnosis. To make this possible, medical data should ideally be secured and made accessible to many medical practitioners, which makes them prone to malicious entities. Medical information has inherent value to malicious entities due to its privacy-sensitive nature in a variety of ways. Furthermore, if access to data is distributively made available to AI algorithms (particularly neural networks) for further analysis/diagnosis, the danger to the data may increase (e.g., model poisoning with fake data introduction). In this paper, we propose a federated learning approach that uses decentralized learning with blockchain-based security and a proposition that accompanies that training intelligent systems using distributed and locally-stored data for the use of all patients. Our work in progress hopes to contribute to the latest trend of the Internet of Medical Things security and privacy.
{"title":"Blockchain Technology and Neural Networks for the Internet of Medical Things","authors":"Dawid Połap, Gautam Srivastava, A. Jolfaei, R. Parizi","doi":"10.1109/INFOCOMWKSHPS50562.2020.9162735","DOIUrl":"https://doi.org/10.1109/INFOCOMWKSHPS50562.2020.9162735","url":null,"abstract":"In today's technological climate, users require fast automation and digitization of results for large amounts of data at record speeds. Especially in the field of medicine, where each patient is often asked to undergo many different examinations within one diagnosis or treatment. Each examination can help in the diagnosis or prediction of further disease progression. Furthermore, all produced data from these examinations must be stored somewhere and available to various medical practitioners for analysis who may be in geographically diverse locations. The current medical climate leans towards remote patient monitoring and AI-assisted diagnosis. To make this possible, medical data should ideally be secured and made accessible to many medical practitioners, which makes them prone to malicious entities. Medical information has inherent value to malicious entities due to its privacy-sensitive nature in a variety of ways. Furthermore, if access to data is distributively made available to AI algorithms (particularly neural networks) for further analysis/diagnosis, the danger to the data may increase (e.g., model poisoning with fake data introduction). In this paper, we propose a federated learning approach that uses decentralized learning with blockchain-based security and a proposition that accompanies that training intelligent systems using distributed and locally-stored data for the use of all patients. Our work in progress hopes to contribute to the latest trend of the Internet of Medical Things security and privacy.","PeriodicalId":104136,"journal":{"name":"IEEE INFOCOM 2020 - IEEE Conference on Computer Communications Workshops (INFOCOM WKSHPS)","volume":"1 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":"128814671","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/INFOCOMWKSHPS50562.2020.9162663
M. Girmay, Vasilis Maglogiannis, D. Naudts, Jaron Fontaine, A. Shahid, E. D. Poorter, I. Moerman
Recently, the expansion of wireless network deployments is resulting in increased scarcity of available licensed radio spectrum. As the domain of wireless communications is progressing rapidly, many industries are looking into wireless network solutions that can increase their productivity. Private LTE is a promising wireless network solution as it can be customised independently without the control of a mobile network operator while providing reliable and spectrum efficient services. For this reason, the deployment of Private LTE in the unlicensed spectrum and its coexistence with Wi-Fi is becoming a popular topic in research. In this paper, we propose a coexistence scheme for private LTE network in unlicensed spectrum that enables a fair spectrum sharing with co-located Wi-Fi networks. This is achieved by exploiting various LTE frame configurations consisting of different combinations of downlink, uplink, special subframe and muted subframes. The configuration of a single frame is decided based on a rule based algorithm that exploits Wi-Fi spectrum occupancy statistics that is obtained from a technology recognition system which is based on a Convolutional Neural Network. The performance of the proposed private LTE scheme and its coexistence with Wi-Fi is investigated for different traffic scenarios showcasing how the proposed scheme can lead to a harmless coexistence of LTE and Wi-Fi.
{"title":"Adaptive CNN-based Private LTE Solution for Fair Coexistence with Wi-Fi in Unlicensed Spectrum","authors":"M. Girmay, Vasilis Maglogiannis, D. Naudts, Jaron Fontaine, A. Shahid, E. D. Poorter, I. Moerman","doi":"10.1109/INFOCOMWKSHPS50562.2020.9162663","DOIUrl":"https://doi.org/10.1109/INFOCOMWKSHPS50562.2020.9162663","url":null,"abstract":"Recently, the expansion of wireless network deployments is resulting in increased scarcity of available licensed radio spectrum. As the domain of wireless communications is progressing rapidly, many industries are looking into wireless network solutions that can increase their productivity. Private LTE is a promising wireless network solution as it can be customised independently without the control of a mobile network operator while providing reliable and spectrum efficient services. For this reason, the deployment of Private LTE in the unlicensed spectrum and its coexistence with Wi-Fi is becoming a popular topic in research. In this paper, we propose a coexistence scheme for private LTE network in unlicensed spectrum that enables a fair spectrum sharing with co-located Wi-Fi networks. This is achieved by exploiting various LTE frame configurations consisting of different combinations of downlink, uplink, special subframe and muted subframes. The configuration of a single frame is decided based on a rule based algorithm that exploits Wi-Fi spectrum occupancy statistics that is obtained from a technology recognition system which is based on a Convolutional Neural Network. The performance of the proposed private LTE scheme and its coexistence with Wi-Fi is investigated for different traffic scenarios showcasing how the proposed scheme can lead to a harmless coexistence of LTE and Wi-Fi.","PeriodicalId":104136,"journal":{"name":"IEEE INFOCOM 2020 - IEEE Conference on Computer Communications Workshops (INFOCOM WKSHPS)","volume":"4 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":"115206710","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/INFOCOMWKSHPS50562.2020.9162714
Gaurang Bansal, V. Chamola
Over the past few years, with increasing mobile traffic and decreasing revenue per user, Heterogeneous Networks (HetNets) have become a topic of interest to many stakeholders. HetNets is a combination of networks with different access technologies and cell types working with each other. Mobile network operators are keen to reduce operational expenses by deploying HetNets while they provide better QoS to the user anywhere, anytime wireless connectivity. Although HetNets provide various benefits, yet many open issues need to be addressed to harness their impact. They are also prone to several security threats such as physical attacks, man-in-the-middle (MITM) attacks, impersonation attacks, replay attacks, and node tampering attacks. Moreover, due to the different nature and structure of each network in a HetNet, secure handover between various wireless networks is a complex task that is not yet resolved. In this paper, we address the issues mentioned above by designing a secure handover mechanism that is resistant to both passive and active attacks. We also show a performance comparison of our protocol with the state-of-the-art protocols for securing hetnets based on computation, communication, and memory storage cost.
{"title":"Lightweight Authentication Protocol for Inter Base Station Communication in Heterogeneous Networks","authors":"Gaurang Bansal, V. Chamola","doi":"10.1109/INFOCOMWKSHPS50562.2020.9162714","DOIUrl":"https://doi.org/10.1109/INFOCOMWKSHPS50562.2020.9162714","url":null,"abstract":"Over the past few years, with increasing mobile traffic and decreasing revenue per user, Heterogeneous Networks (HetNets) have become a topic of interest to many stakeholders. HetNets is a combination of networks with different access technologies and cell types working with each other. Mobile network operators are keen to reduce operational expenses by deploying HetNets while they provide better QoS to the user anywhere, anytime wireless connectivity. Although HetNets provide various benefits, yet many open issues need to be addressed to harness their impact. They are also prone to several security threats such as physical attacks, man-in-the-middle (MITM) attacks, impersonation attacks, replay attacks, and node tampering attacks. Moreover, due to the different nature and structure of each network in a HetNet, secure handover between various wireless networks is a complex task that is not yet resolved. In this paper, we address the issues mentioned above by designing a secure handover mechanism that is resistant to both passive and active attacks. We also show a performance comparison of our protocol with the state-of-the-art protocols for securing hetnets based on computation, communication, and memory storage cost.","PeriodicalId":104136,"journal":{"name":"IEEE INFOCOM 2020 - IEEE Conference on Computer Communications Workshops (INFOCOM WKSHPS)","volume":"9 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":"115430146","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/infocomwkshps50562.2020.9162968
Yoshiaki Inoue
In this paper, we derive an explicit expression for the probability distribution of the age of information (AoI) in the $mathrm{GI}/mathrm{GI}/infty$ queue with loss. Two special cases $mathrm{M}/mathrm{GI}/infty$ and $mathrm{D}/mathrm{GI}/infty$ are discussed, where the distribution function of the AoI is shown to take a simple closed-form. In addition, a comparison result between the $mathrm{M}/mathrm{GI}/infty$ and $mathrm{D}/mathrm{GI}/infty$ queues in terms of the AoI distribution is presented.
{"title":"The Probability Distribution of the AoI in Queues with Infinitely Many Servers","authors":"Yoshiaki Inoue","doi":"10.1109/infocomwkshps50562.2020.9162968","DOIUrl":"https://doi.org/10.1109/infocomwkshps50562.2020.9162968","url":null,"abstract":"In this paper, we derive an explicit expression for the probability distribution of the age of information (AoI) in the $mathrm{GI}/mathrm{GI}/infty$ queue with loss. Two special cases $mathrm{M}/mathrm{GI}/infty$ and $mathrm{D}/mathrm{GI}/infty$ are discussed, where the distribution function of the AoI is shown to take a simple closed-form. In addition, a comparison result between the $mathrm{M}/mathrm{GI}/infty$ and $mathrm{D}/mathrm{GI}/infty$ queues in terms of the AoI distribution is presented.","PeriodicalId":104136,"journal":{"name":"IEEE INFOCOM 2020 - IEEE Conference on Computer Communications Workshops (INFOCOM WKSHPS)","volume":"49 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":"117272447","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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