Moiré QR Code is a secure encrypted QR code system that can protect the user’s QR code displayed on the screen from being accessed by attackers. However, conventional decryption methods based on image processing techniques suffer from intensive computation and significant decryption latency in practical mobile applications. In this work, we propose a deep learning-based Moiré QR code decryption framework and achieve an excellent decryption performance. Considering the sensitivity of the Moiré phenomenon, collecting training data in the real world is extremely labor and material intensive. To overcome this issue, we develop a physical screen-imaging Moiré simulation methodology to generate a synthetic dataset that covers the entire Moiré-visible area. Extensive experiments show that the proposed decryption network can achieve a low decryption latency (0.02 seconds) and a high decryption rate (98.8%), compared with the previous decryption method with decryption latency (5.4 seconds) and decryption rate (98.6%).
{"title":"Effectively Learning Moiré QR Code Decryption from Simulated Data","authors":"Yu Lu, Hao Pan, Feitong Tan, Yi-Chao Chen, Jiadi Yu, Jinghai He, Guangtao Xue","doi":"10.1109/INFOCOM53939.2023.10229000","DOIUrl":"https://doi.org/10.1109/INFOCOM53939.2023.10229000","url":null,"abstract":"Moiré QR Code is a secure encrypted QR code system that can protect the user’s QR code displayed on the screen from being accessed by attackers. However, conventional decryption methods based on image processing techniques suffer from intensive computation and significant decryption latency in practical mobile applications. In this work, we propose a deep learning-based Moiré QR code decryption framework and achieve an excellent decryption performance. Considering the sensitivity of the Moiré phenomenon, collecting training data in the real world is extremely labor and material intensive. To overcome this issue, we develop a physical screen-imaging Moiré simulation methodology to generate a synthetic dataset that covers the entire Moiré-visible area. Extensive experiments show that the proposed decryption network can achieve a low decryption latency (0.02 seconds) and a high decryption rate (98.8%), compared with the previous decryption method with decryption latency (5.4 seconds) and decryption rate (98.6%).","PeriodicalId":387707,"journal":{"name":"IEEE INFOCOM 2023 - IEEE Conference on Computer Communications","volume":"9 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-05-17","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"117012956","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2023-05-17DOI: 10.1109/INFOCOM53939.2023.10229007
Xiaochan Xue, Shucheng Yu, Min Song
Bootstrapping security among wireless devices without prior-shared secrets is frequently demanded in emerging wireless and mobile applications. One promising approach for this problem is to utilize in-band physical-layer radio-frequency (RF) signals for authenticated key establishment because of the efficiency and high usability. However, existing in-band authenticated key agreement (AKA) protocols are mostly vulnerable to Man-in-the-Middle (MitM) attacks, which can be launched by modifying the transmitted wireless signals over the air. By annihilating legitimate signals and injecting malicious signals, signal modification attackers are able to completely control the communication channels and spoof victim wireless devices. State-of-the-art (SOTA) techniques addressing such attacks require additional auxiliary hardware or are limited to single attackers. This paper proposes a novel in-band security bootstrapping technique that can thwart colluding signal modification attackers. Different from SOTA solutions, our design is compatible with commodity devices without requiring additional hardware. We achieve this based on the internal randomness of each device that is unpredictable to attackers. Any modification to RF signals will be detected with high probabilities. Extensive security analysis and experimentation on the USRP platform demonstrate the effectiveness of our design under various attack strategies.
{"title":"Secure Device Trust Bootstrapping Against Collaborative Signal Modification Attacks","authors":"Xiaochan Xue, Shucheng Yu, Min Song","doi":"10.1109/INFOCOM53939.2023.10229007","DOIUrl":"https://doi.org/10.1109/INFOCOM53939.2023.10229007","url":null,"abstract":"Bootstrapping security among wireless devices without prior-shared secrets is frequently demanded in emerging wireless and mobile applications. One promising approach for this problem is to utilize in-band physical-layer radio-frequency (RF) signals for authenticated key establishment because of the efficiency and high usability. However, existing in-band authenticated key agreement (AKA) protocols are mostly vulnerable to Man-in-the-Middle (MitM) attacks, which can be launched by modifying the transmitted wireless signals over the air. By annihilating legitimate signals and injecting malicious signals, signal modification attackers are able to completely control the communication channels and spoof victim wireless devices. State-of-the-art (SOTA) techniques addressing such attacks require additional auxiliary hardware or are limited to single attackers. This paper proposes a novel in-band security bootstrapping technique that can thwart colluding signal modification attackers. Different from SOTA solutions, our design is compatible with commodity devices without requiring additional hardware. We achieve this based on the internal randomness of each device that is unpredictable to attackers. Any modification to RF signals will be detected with high probabilities. Extensive security analysis and experimentation on the USRP platform demonstrate the effectiveness of our design under various attack strategies.","PeriodicalId":387707,"journal":{"name":"IEEE INFOCOM 2023 - IEEE Conference on Computer Communications","volume":"53 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-05-17","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"115160523","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2023-05-17DOI: 10.1109/INFOCOM53939.2023.10229038
Qiang Yang, Kaiyan Cui, Yuanqing Zheng
Voice assistants are widely integrated into a variety of smart devices, enabling users to easily complete daily tasks and even critical operations like online transactions with voice commands. Thus, once attackers replay a secretly-recorded voice command by loudspeakers to compromise users’ voice assistants, this operation will cause serious consequences, such as information leakage and property loss. Unfortunately, most voice liveness detection approaches against replay attacks mainly rely on detecting lip motions or subtle physiological features in speech, which are limited within a very short range. In this paper, we propose VoShield to check whether a voice command is from a genuine user or a loudspeaker imposter. VoShield measures sound field dynamics, a feature that changes fast as the human mouths dynamically open and close. In contrast, it would remain rather stable for loudspeakers due to the fixed size. This feature enables VoShield to largely extend the working distance and remain resilient to user locations. Besides, sound field dynamics are extracted from the difference between multiple microphone channels, making this feature robust to voice volume. To evaluate VoShield, we conducted comprehensive experiments with various settings in different working scenarios. The results show that VoShield can achieve a detection accuracy of 98.2% and an Equal Error Rate of 2.0%, which serves as a promising complement to current voice authentication systems for smart devices.
{"title":"VoShield: Voice Liveness Detection with Sound Field Dynamics","authors":"Qiang Yang, Kaiyan Cui, Yuanqing Zheng","doi":"10.1109/INFOCOM53939.2023.10229038","DOIUrl":"https://doi.org/10.1109/INFOCOM53939.2023.10229038","url":null,"abstract":"Voice assistants are widely integrated into a variety of smart devices, enabling users to easily complete daily tasks and even critical operations like online transactions with voice commands. Thus, once attackers replay a secretly-recorded voice command by loudspeakers to compromise users’ voice assistants, this operation will cause serious consequences, such as information leakage and property loss. Unfortunately, most voice liveness detection approaches against replay attacks mainly rely on detecting lip motions or subtle physiological features in speech, which are limited within a very short range. In this paper, we propose VoShield to check whether a voice command is from a genuine user or a loudspeaker imposter. VoShield measures sound field dynamics, a feature that changes fast as the human mouths dynamically open and close. In contrast, it would remain rather stable for loudspeakers due to the fixed size. This feature enables VoShield to largely extend the working distance and remain resilient to user locations. Besides, sound field dynamics are extracted from the difference between multiple microphone channels, making this feature robust to voice volume. To evaluate VoShield, we conducted comprehensive experiments with various settings in different working scenarios. The results show that VoShield can achieve a detection accuracy of 98.2% and an Equal Error Rate of 2.0%, which serves as a promising complement to current voice authentication systems for smart devices.","PeriodicalId":387707,"journal":{"name":"IEEE INFOCOM 2023 - IEEE Conference on Computer Communications","volume":"25 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-05-17","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"121367081","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}
Programmable switches allow data plane to program how packets are processed, which enables flexibility for network management tasks, e.g., packet scheduling and flow measurement. Existing studies focus on program deployment at a single switch, while deployment across the whole data plane is still a challenging issue. In this paper, we present RED, a Resource-Efficient and Distributed program deployment solution for programmable switches. First of all, we compile the data plane programs to estimate the resource utilization and divide them into two categories for further processing. Then, the proposed merging and splitting algorithms are selectively applied to merge or split the pending programs. Finally, we consolidate the scarce resources of the whole data plane to deploy the programs. Extensive experiment results show that 1) RED improves the speedup by two orders of magnitude compared to P4Visor and merges 58.64% more nodes than SPEED; 2) RED makes the overwhelmed programs run normally at a single switch and reduces 3% latency of inter-device scheduling; 3) RED achieves network-wide resource balancing in a distributed way.
{"title":"RED: Distributed Program Deployment for Resource-aware Programmable Switches","authors":"Xingxin Jia, Fuliang Li, Songlin Chen, Chengxi Gao, Pengfei Wang, Xingwei Wang","doi":"10.1109/INFOCOM53939.2023.10228974","DOIUrl":"https://doi.org/10.1109/INFOCOM53939.2023.10228974","url":null,"abstract":"Programmable switches allow data plane to program how packets are processed, which enables flexibility for network management tasks, e.g., packet scheduling and flow measurement. Existing studies focus on program deployment at a single switch, while deployment across the whole data plane is still a challenging issue. In this paper, we present RED, a Resource-Efficient and Distributed program deployment solution for programmable switches. First of all, we compile the data plane programs to estimate the resource utilization and divide them into two categories for further processing. Then, the proposed merging and splitting algorithms are selectively applied to merge or split the pending programs. Finally, we consolidate the scarce resources of the whole data plane to deploy the programs. Extensive experiment results show that 1) RED improves the speedup by two orders of magnitude compared to P4Visor and merges 58.64% more nodes than SPEED; 2) RED makes the overwhelmed programs run normally at a single switch and reduces 3% latency of inter-device scheduling; 3) RED achieves network-wide resource balancing in a distributed way.","PeriodicalId":387707,"journal":{"name":"IEEE INFOCOM 2023 - IEEE Conference on Computer Communications","volume":"38 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-05-17","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"124098172","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}
Due to the penetration of edge computing, a wide variety of workloads are sunk down to the network edge to alleviate huge pressure of the cloud. With the presence of high input workload dynamics and intensive edge resource contention, it is highly non-trivial for an edge proxy to optimize the scheduling of heterogeneous services with diverse QoS requirements. In general, online services should be quickly completed in a quite stable running environment to meet their tight latency constraint, while offline services can be processed in a loose manner for their elastic soft deadlines. To well coordinate such services at the resource-limited edge cluster, in this paper, we study an edge-centric resource provisioning optimization for dynamic online and offline services co-location, where the proxy seeks to maximize timely online service performances while maintaining satisfactory long-term offline service performances. However, intricate hybrid couplings for provisioning decisions arise due to heterogeneous constraints of the co-located services and their different time-scale performances. We hence first propose a reactive provisioning approach without requiring a prior knowledge of future system dynamics, which leverages a Lagrange relaxation for devising constraint-aware stochastic subgradient algorithm to deal with the challenge of hybrid couplings. To further boost the performance by integrating the powerful machine learning techniques, we also advocate a predictive provisioning approach, where the future request arrivals can be estimated accurately. With rigorous theoretical analysis and extensive trace-driven evaluations, we show the superior performance of our proposed algorithms for online and offline services co-location at the edge.
{"title":"Dynamic Edge-centric Resource Provisioning for Online and Offline Services Co-location","authors":"Ouyang Tao, Kongyange Zhao, Xiaoxi Zhang, Zhi Zhou, Xu Chen","doi":"10.1109/INFOCOM53939.2023.10228949","DOIUrl":"https://doi.org/10.1109/INFOCOM53939.2023.10228949","url":null,"abstract":"Due to the penetration of edge computing, a wide variety of workloads are sunk down to the network edge to alleviate huge pressure of the cloud. With the presence of high input workload dynamics and intensive edge resource contention, it is highly non-trivial for an edge proxy to optimize the scheduling of heterogeneous services with diverse QoS requirements. In general, online services should be quickly completed in a quite stable running environment to meet their tight latency constraint, while offline services can be processed in a loose manner for their elastic soft deadlines. To well coordinate such services at the resource-limited edge cluster, in this paper, we study an edge-centric resource provisioning optimization for dynamic online and offline services co-location, where the proxy seeks to maximize timely online service performances while maintaining satisfactory long-term offline service performances. However, intricate hybrid couplings for provisioning decisions arise due to heterogeneous constraints of the co-located services and their different time-scale performances. We hence first propose a reactive provisioning approach without requiring a prior knowledge of future system dynamics, which leverages a Lagrange relaxation for devising constraint-aware stochastic subgradient algorithm to deal with the challenge of hybrid couplings. To further boost the performance by integrating the powerful machine learning techniques, we also advocate a predictive provisioning approach, where the future request arrivals can be estimated accurately. With rigorous theoretical analysis and extensive trace-driven evaluations, we show the superior performance of our proposed algorithms for online and offline services co-location at the edge.","PeriodicalId":387707,"journal":{"name":"IEEE INFOCOM 2023 - IEEE Conference on Computer Communications","volume":"1 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-05-17","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"130936883","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2023-05-17DOI: 10.1109/INFOCOM53939.2023.10228906
Lei Zhang, Haotian Guo, Yanjie Dong, Fang Wang, Laizhong Cui, Victor C. M. Leung
Tile-based streaming and super resolution are two representative technologies adopted to improve bandwidth efficiency of immersive video steaming. The former allows selective download of contents in the user viewport by splitting the video into multiple independently decodable tiles. The latter leverages client-side computation to reconstruct the received video into higher quality using advanced neural network models. In this work, we propose CASE, a collaborated adaptive streaming and enhancement framework for mobile immersive videos, which integrates super resolution with tile-based streaming to optimize user experience with dynamic bandwidth and limited computing capability. To coordinate the video transmission and reconstruction in CASE, we identify and address several key design issues including unified video quality assessment, computation complexity model for super resolution, and buffer analysis considering the interplay between transmission and reconstruction. We further formulate the quality-of-experience (QoE) maximization problem for mobile immersive video streaming and propose a rate adaptation algorithm to make the best decisions for download and for reconstruction based on the Lyapunov optimization theory. Extensive evaluation results validate the superiority of our proposed approach, which presents stable performance with considerable QoE improvement, while enabling trade-off between playback smoothness and video quality.
{"title":"Collaborative Streaming and Super Resolution Adaptation for Mobile Immersive Videos","authors":"Lei Zhang, Haotian Guo, Yanjie Dong, Fang Wang, Laizhong Cui, Victor C. M. Leung","doi":"10.1109/INFOCOM53939.2023.10228906","DOIUrl":"https://doi.org/10.1109/INFOCOM53939.2023.10228906","url":null,"abstract":"Tile-based streaming and super resolution are two representative technologies adopted to improve bandwidth efficiency of immersive video steaming. The former allows selective download of contents in the user viewport by splitting the video into multiple independently decodable tiles. The latter leverages client-side computation to reconstruct the received video into higher quality using advanced neural network models. In this work, we propose CASE, a collaborated adaptive streaming and enhancement framework for mobile immersive videos, which integrates super resolution with tile-based streaming to optimize user experience with dynamic bandwidth and limited computing capability. To coordinate the video transmission and reconstruction in CASE, we identify and address several key design issues including unified video quality assessment, computation complexity model for super resolution, and buffer analysis considering the interplay between transmission and reconstruction. We further formulate the quality-of-experience (QoE) maximization problem for mobile immersive video streaming and propose a rate adaptation algorithm to make the best decisions for download and for reconstruction based on the Lyapunov optimization theory. Extensive evaluation results validate the superiority of our proposed approach, which presents stable performance with considerable QoE improvement, while enabling trade-off between playback smoothness and video quality.","PeriodicalId":387707,"journal":{"name":"IEEE INFOCOM 2023 - IEEE Conference on Computer Communications","volume":"120 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-05-17","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"128066081","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2023-05-17DOI: 10.1109/INFOCOM53939.2023.10228881
Ruixiao Zhang, Tianchi Huang, Chen Wu, Lifeng Sun
Streamers are the core competency of the crowd-sourced live streaming (CLS) platform. However, little work has explored how different factors relate to their popularity evolution patterns. In this paper, we will investigate a critical problem, i.e., how to discover the promising streamers in their early stage? To tackle this problem, we first conduct large-scale measurement on a real-world CLS dataset. We find that streamers can indeed be clustered into two evolution types (i.e., rising type and normal type), and these two types of streamers will show differences in some inherent properties. Traditional time-sequential models cannot handle this problem, because they are unable to capture the complicated interactivity and extensive heterogeneity in CLS scenarios. To address their shortcomings, we further propose Niffler, a novel heterogeneous attention temporal graph framework (HATG) for predicting the evolution types of CLS streamers. Specifically, through the graph neural network (GNN) and gated-recurrent-unit (GRU) structure, Niffler can capture both the interactive features and the evolutionary dynamics. Moreover, by integrating the attention mechanism in the model design, Niffler can intelligently preserve the heterogeneity when learning different levels of node representations. We systematically compare Niffler against multiple baselines from different categories, and the experimental results show that our proposed model can achieve the best prediction performance.
{"title":"Who is the Rising Star? Demystifying the Promising Streamers in Crowdsourced Live Streaming","authors":"Ruixiao Zhang, Tianchi Huang, Chen Wu, Lifeng Sun","doi":"10.1109/INFOCOM53939.2023.10228881","DOIUrl":"https://doi.org/10.1109/INFOCOM53939.2023.10228881","url":null,"abstract":"Streamers are the core competency of the crowd-sourced live streaming (CLS) platform. However, little work has explored how different factors relate to their popularity evolution patterns. In this paper, we will investigate a critical problem, i.e., how to discover the promising streamers in their early stage? To tackle this problem, we first conduct large-scale measurement on a real-world CLS dataset. We find that streamers can indeed be clustered into two evolution types (i.e., rising type and normal type), and these two types of streamers will show differences in some inherent properties. Traditional time-sequential models cannot handle this problem, because they are unable to capture the complicated interactivity and extensive heterogeneity in CLS scenarios. To address their shortcomings, we further propose Niffler, a novel heterogeneous attention temporal graph framework (HATG) for predicting the evolution types of CLS streamers. Specifically, through the graph neural network (GNN) and gated-recurrent-unit (GRU) structure, Niffler can capture both the interactive features and the evolutionary dynamics. Moreover, by integrating the attention mechanism in the model design, Niffler can intelligently preserve the heterogeneity when learning different levels of node representations. We systematically compare Niffler against multiple baselines from different categories, and the experimental results show that our proposed model can achieve the best prediction performance.","PeriodicalId":387707,"journal":{"name":"IEEE INFOCOM 2023 - IEEE Conference on Computer Communications","volume":"55 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-05-17","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"126841734","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2023-05-17DOI: 10.1109/INFOCOM53939.2023.10228897
Kengo Nakamura, Takeru Inoue, Masaaki Nishino, Norihito Yasuda, S. Minato
Contemporary society survives on several network infrastructures, such as communication and transportation. These network infrastructures are required to keep all nodes connected, although these nodes are occasionally disconnected due to failures. Thus, the expected number of connected node pairs (ECP) during an operation period is a reasonable reliability measure in network design. However, no work has studied ECP due to its computational hardness; we have to solve the reliability evaluation problem, which is a computationally tough problem, for O(n2) times where n is the number of nodes in a network. This paper proposes an efficient method that exactly computes ECP. Our method performs dynamic programming just once without explicit repetition for each node pair and obtains an exact ECP value weighted by the number of users at each node. A thorough complexity analysis reveals that our method is faster than an existing reliability evaluation method, which can be transferred to ECP computation, by O(n). Numerical experiments using real topologies show great efficiency; e.g., our method computes the ECP of an 821-link network in ten seconds; the existing method cannot complete it in an hour. This paper also presents two applications: critical link identification and optimal resource (e.g., a server) placement.
{"title":"A Fast and Exact Evaluation Algorithm for the Expected Number of Connected Nodes: an Enhanced Network Reliability Measure","authors":"Kengo Nakamura, Takeru Inoue, Masaaki Nishino, Norihito Yasuda, S. Minato","doi":"10.1109/INFOCOM53939.2023.10228897","DOIUrl":"https://doi.org/10.1109/INFOCOM53939.2023.10228897","url":null,"abstract":"Contemporary society survives on several network infrastructures, such as communication and transportation. These network infrastructures are required to keep all nodes connected, although these nodes are occasionally disconnected due to failures. Thus, the expected number of connected node pairs (ECP) during an operation period is a reasonable reliability measure in network design. However, no work has studied ECP due to its computational hardness; we have to solve the reliability evaluation problem, which is a computationally tough problem, for O(n2) times where n is the number of nodes in a network. This paper proposes an efficient method that exactly computes ECP. Our method performs dynamic programming just once without explicit repetition for each node pair and obtains an exact ECP value weighted by the number of users at each node. A thorough complexity analysis reveals that our method is faster than an existing reliability evaluation method, which can be transferred to ECP computation, by O(n). Numerical experiments using real topologies show great efficiency; e.g., our method computes the ECP of an 821-link network in ten seconds; the existing method cannot complete it in an hour. This paper also presents two applications: critical link identification and optimal resource (e.g., a server) placement.","PeriodicalId":387707,"journal":{"name":"IEEE INFOCOM 2023 - IEEE Conference on Computer Communications","volume":"7 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-05-17","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"130006280","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2023-05-17DOI: 10.1109/INFOCOM53939.2023.10228913
Aakriti Adhikari, Sanjib Sur
We propose Argosleep, a millimeter-wave (mmWave) wireless sensors based sleep posture monitoring system that predicts the 3D location of body joints of a person during sleep. Argosleep leverages deep learning models and knowledge of human anatomical features to solve challenges with low-resolution, specularity, and aliasing in existing mmWave devices. Argosleep builds the model by learning the relationship between mmWave reflected signals and body postures from thousands of existing samples. Since practical sleep also involves sudden toss-turns, which could introduce errors in posture prediction, Argosleep designs a state machine based on the reflected signals to classify the sleeping states into rest or toss-turn, and predict the posture only during the rest states. We evaluate Argosleep with real data collected from COTS mmWave devices for 8 volunteers of diverse ages, gender, and height performing different sleep postures. We observe that Argosleep identifies the toss-turn events accurately and predicts 3D location of body joints with accuracy on par with the existing vision-based system, unlocking the potential of mmWave systems for privacy-noninvasive at-home healthcare applications.
{"title":"Argosleep: Monitoring Sleep Posture from Commodity Millimeter-Wave Devices","authors":"Aakriti Adhikari, Sanjib Sur","doi":"10.1109/INFOCOM53939.2023.10228913","DOIUrl":"https://doi.org/10.1109/INFOCOM53939.2023.10228913","url":null,"abstract":"We propose Argosleep, a millimeter-wave (mmWave) wireless sensors based sleep posture monitoring system that predicts the 3D location of body joints of a person during sleep. Argosleep leverages deep learning models and knowledge of human anatomical features to solve challenges with low-resolution, specularity, and aliasing in existing mmWave devices. Argosleep builds the model by learning the relationship between mmWave reflected signals and body postures from thousands of existing samples. Since practical sleep also involves sudden toss-turns, which could introduce errors in posture prediction, Argosleep designs a state machine based on the reflected signals to classify the sleeping states into rest or toss-turn, and predict the posture only during the rest states. We evaluate Argosleep with real data collected from COTS mmWave devices for 8 volunteers of diverse ages, gender, and height performing different sleep postures. We observe that Argosleep identifies the toss-turn events accurately and predicts 3D location of body joints with accuracy on par with the existing vision-based system, unlocking the potential of mmWave systems for privacy-noninvasive at-home healthcare applications.","PeriodicalId":387707,"journal":{"name":"IEEE INFOCOM 2023 - IEEE Conference on Computer Communications","volume":"6 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-05-17","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"127166801","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2023-05-17DOI: 10.1109/INFOCOM53939.2023.10229081
Kentaro Kita, Junji Takemasa, Y. Koizumi, T. Hasegawa
A large portion of Internet traffic passes through middleboxes that read or modify messages. However, as more traffic is protected with TLS, middleboxes are becoming unable to provide their functions. To leverage middlebox functionality while preserving communication security, secure middlebox channel protocols have been designed as extensions of TLS. A key idea is that the endpoints explicitly incorporate middleboxes into the TLS handshake and grant each middlebox either the read or the write permission for their messages. Because each middlebox has the least data access privilege, these protocols are resilient against the compromise of a single middlebox. However, the existing studies have not comprehensively analyzed the communication security under the scenarios where multiple middleboxes are compromised. In this paper, we present novel attacks that break the security of the existing protocols under such scenarios and then modify maTLS, the state-of-the-art protocol, so that all the attacks are prevented with marginal overhead.
{"title":"Secure Middlebox Channel over TLS and its Resiliency against Middlebox Compromise","authors":"Kentaro Kita, Junji Takemasa, Y. Koizumi, T. Hasegawa","doi":"10.1109/INFOCOM53939.2023.10229081","DOIUrl":"https://doi.org/10.1109/INFOCOM53939.2023.10229081","url":null,"abstract":"A large portion of Internet traffic passes through middleboxes that read or modify messages. However, as more traffic is protected with TLS, middleboxes are becoming unable to provide their functions. To leverage middlebox functionality while preserving communication security, secure middlebox channel protocols have been designed as extensions of TLS. A key idea is that the endpoints explicitly incorporate middleboxes into the TLS handshake and grant each middlebox either the read or the write permission for their messages. Because each middlebox has the least data access privilege, these protocols are resilient against the compromise of a single middlebox. However, the existing studies have not comprehensively analyzed the communication security under the scenarios where multiple middleboxes are compromised. In this paper, we present novel attacks that break the security of the existing protocols under such scenarios and then modify maTLS, the state-of-the-art protocol, so that all the attacks are prevented with marginal overhead.","PeriodicalId":387707,"journal":{"name":"IEEE INFOCOM 2023 - IEEE Conference on Computer Communications","volume":"7 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-05-17","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"128958641","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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