Pub Date : 2020-11-16DOI: 10.1109/LCN48667.2020.9314841
Ryosuke Nagayoshi, Y. Tanigawa, H. Tode
Currently, in addition to wireless LAN (WLAN), equipments for longer-distance wireless communication like IEEE 802.11af and 802.11ah are being developed. Thus, in the near future, access points (APs) and wireless stations (STAs) equipped with functions for both WLAN communication and longer-distance communication are expected. Although longer-distance communication enables STAs to connect to their APs even outside the WLAN coverages, the transmission rates are smaller. Multihop WLAN communication via relay STAs provides larger transmission rates. However, frame transmission from relay STAs increases collisions, and additional routing is required. This study proposes collision-protected multihop transmission without preliminary routing in the environment in which both WLAN communication and longer-distance communication are available. Collision protection and routing are processed by a two-way handshake between data frame transmitter and receiver stations with the longer-distance communication. After the handshake, the data frame is transmitted via relay STAs by exclusively using the WLAN channel.
{"title":"A Collision-Protected Multihop Frame Transmission Method with Wireless LAN Communication Supported by Longer-Distance Wireless Communication","authors":"Ryosuke Nagayoshi, Y. Tanigawa, H. Tode","doi":"10.1109/LCN48667.2020.9314841","DOIUrl":"https://doi.org/10.1109/LCN48667.2020.9314841","url":null,"abstract":"Currently, in addition to wireless LAN (WLAN), equipments for longer-distance wireless communication like IEEE 802.11af and 802.11ah are being developed. Thus, in the near future, access points (APs) and wireless stations (STAs) equipped with functions for both WLAN communication and longer-distance communication are expected. Although longer-distance communication enables STAs to connect to their APs even outside the WLAN coverages, the transmission rates are smaller. Multihop WLAN communication via relay STAs provides larger transmission rates. However, frame transmission from relay STAs increases collisions, and additional routing is required. This study proposes collision-protected multihop transmission without preliminary routing in the environment in which both WLAN communication and longer-distance communication are available. Collision protection and routing are processed by a two-way handshake between data frame transmitter and receiver stations with the longer-distance communication. After the handshake, the data frame is transmitted via relay STAs by exclusively using the WLAN channel.","PeriodicalId":245782,"journal":{"name":"2020 IEEE 45th Conference on Local Computer Networks (LCN)","volume":"26 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2020-11-16","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"123440076","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 : 2020-11-16DOI: 10.1109/LCN48667.2020.9314817
Rima Benelmir, S. Bitam, A. Mellouk
Recently, autonomous vehicles navigation (AVN) attracted many researches trying to improve road traffic without the human intervention. One of the main challenges in AVN is allowing a vehicle to discover its moving trajectory with a reduced computational complexity. To cope with this issue, we propose in this paper a new simulated annealing algorithm to discover an optimal trajectory when the vehicle encounters an obstacle using LiDAR perception. The found trajectory is then sent to a roadside unit (RSU), which communicates this discovery to other nodes in the network for further use. During its navigation, the vehicle perceives the environment by a LiDAR sensor to detect an eventual obstacle and launches an optimal path discovery to reach the final destination in a reduced time. The results obtained showed the effectiveness of our proposal to find an optimal route compared to Dijkstra algorithm.
{"title":"An efficient autonomous vehicle navigation scheme based on LiDAR sensor in vehicular network","authors":"Rima Benelmir, S. Bitam, A. Mellouk","doi":"10.1109/LCN48667.2020.9314817","DOIUrl":"https://doi.org/10.1109/LCN48667.2020.9314817","url":null,"abstract":"Recently, autonomous vehicles navigation (AVN) attracted many researches trying to improve road traffic without the human intervention. One of the main challenges in AVN is allowing a vehicle to discover its moving trajectory with a reduced computational complexity. To cope with this issue, we propose in this paper a new simulated annealing algorithm to discover an optimal trajectory when the vehicle encounters an obstacle using LiDAR perception. The found trajectory is then sent to a roadside unit (RSU), which communicates this discovery to other nodes in the network for further use. During its navigation, the vehicle perceives the environment by a LiDAR sensor to detect an eventual obstacle and launches an optimal path discovery to reach the final destination in a reduced time. The results obtained showed the effectiveness of our proposal to find an optimal route compared to Dijkstra algorithm.","PeriodicalId":245782,"journal":{"name":"2020 IEEE 45th Conference on Local Computer Networks (LCN)","volume":"1 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2020-11-16","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"123887682","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 : 2020-11-16DOI: 10.1109/LCN48667.2020.9314842
Jagnyashini Debadarshini, Sudipta Saha, O. Landsiedel, M. Chan
Intra-group spreading is one of the common and frequent needs in many decentralized communication protocols. Such spreading is useful for quick and local dissemination of information among different clusters in large-scale decentralized systems like the Internet-of-Things (IoT). Complex decentralized protocols can carefully exploit such localized dissemination as a base unit for their efficient implementation. However, due to the inherent broadcast nature of wireless communication, efficient and simultaneous execution of multiple intra-group disseminations is difficult. In this work, we propose a novel and simple way to completely hide a wireless transmission without changing any channel or frequency. Next, we use this for supporting simultaneous intra-group disseminations. Rigorous evaluation of the proposed strategy over testbeds show significant improvement of upto 60% in reliability with similar average latency and radio-on time in comparison to the baseline where no additional mechanism is adopted for separation of intra-group communications.
{"title":"Start of Frame Delimiters (SFDs) for Simultaneous Intra-Group One-to-All Dissemination","authors":"Jagnyashini Debadarshini, Sudipta Saha, O. Landsiedel, M. Chan","doi":"10.1109/LCN48667.2020.9314842","DOIUrl":"https://doi.org/10.1109/LCN48667.2020.9314842","url":null,"abstract":"Intra-group spreading is one of the common and frequent needs in many decentralized communication protocols. Such spreading is useful for quick and local dissemination of information among different clusters in large-scale decentralized systems like the Internet-of-Things (IoT). Complex decentralized protocols can carefully exploit such localized dissemination as a base unit for their efficient implementation. However, due to the inherent broadcast nature of wireless communication, efficient and simultaneous execution of multiple intra-group disseminations is difficult. In this work, we propose a novel and simple way to completely hide a wireless transmission without changing any channel or frequency. Next, we use this for supporting simultaneous intra-group disseminations. Rigorous evaluation of the proposed strategy over testbeds show significant improvement of upto 60% in reliability with similar average latency and radio-on time in comparison to the baseline where no additional mechanism is adopted for separation of intra-group communications.","PeriodicalId":245782,"journal":{"name":"2020 IEEE 45th Conference on Local Computer Networks (LCN)","volume":"1 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2020-11-16","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"129418406","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 : 2020-11-16DOI: 10.1109/LCN48667.2020.9314848
Jianmin Liu, Qi Wang, Chentao He, Yongjun Xu
The mobile robotic network consisting multiple robotic devices such as unmanned aerial vehicles (UAVs) is a high-speed mobile wireless network. Existing mobile ad hoc protocols cannot meet the demands of mobile robotic networks due to intermittently connected links and frequent topology changes. This paper proposes a deep reinforcement learning based adaptive and reliable routing protocol, ARdeep. We formulate routing decisions with a Markov Decision Process model to automatically characterize the network variations. To better infer network environment, the link status is considered when making routing decisions. Simulation results demonstrate that ARdeep outperforms the existing good performing QGeo and conventional GPSR.
{"title":"ARdeep: Adaptive and Reliable Routing Protocol for Mobile Robotic Networks with Deep Reinforcement Learning","authors":"Jianmin Liu, Qi Wang, Chentao He, Yongjun Xu","doi":"10.1109/LCN48667.2020.9314848","DOIUrl":"https://doi.org/10.1109/LCN48667.2020.9314848","url":null,"abstract":"The mobile robotic network consisting multiple robotic devices such as unmanned aerial vehicles (UAVs) is a high-speed mobile wireless network. Existing mobile ad hoc protocols cannot meet the demands of mobile robotic networks due to intermittently connected links and frequent topology changes. This paper proposes a deep reinforcement learning based adaptive and reliable routing protocol, ARdeep. We formulate routing decisions with a Markov Decision Process model to automatically characterize the network variations. To better infer network environment, the link status is considered when making routing decisions. Simulation results demonstrate that ARdeep outperforms the existing good performing QGeo and conventional GPSR.","PeriodicalId":245782,"journal":{"name":"2020 IEEE 45th Conference on Local Computer Networks (LCN)","volume":"10 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2020-11-16","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"127063935","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 : 2020-11-16DOI: 10.1109/LCN48667.2020.9314826
Fei Ge, L. Tan, Wei Zhang, Ming Liu, Xun Gao, Juan Luo
We describes four scheduling schemes on the predefined path in large-scale wireless networks supported by full-duplex radios. The end-to-end throughput on multi-hop path with these schemes is discussed, indicating the effect of scheduling schemes and the influence of full-duplex radios on multi-hop data transmission. We make a simulator and compare the throughput results in these schemes. Results show that the new scheduling methods may improve end-to-end throughput on multi-hop path in wireless networks moderately.
{"title":"Transmission Scheduling and End-to-end Throughput of Multi-hop Paths in Full-duplex Embedded Wireless Networks","authors":"Fei Ge, L. Tan, Wei Zhang, Ming Liu, Xun Gao, Juan Luo","doi":"10.1109/LCN48667.2020.9314826","DOIUrl":"https://doi.org/10.1109/LCN48667.2020.9314826","url":null,"abstract":"We describes four scheduling schemes on the predefined path in large-scale wireless networks supported by full-duplex radios. The end-to-end throughput on multi-hop path with these schemes is discussed, indicating the effect of scheduling schemes and the influence of full-duplex radios on multi-hop data transmission. We make a simulator and compare the throughput results in these schemes. Results show that the new scheduling methods may improve end-to-end throughput on multi-hop path in wireless networks moderately.","PeriodicalId":245782,"journal":{"name":"2020 IEEE 45th Conference on Local Computer Networks (LCN)","volume":"5 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2020-11-16","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"126643395","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 : 2020-11-16DOI: 10.1109/LCN48667.2020.9314812
Tomás Fukac, V. Kosar, J. Korenek, J. Matoušek
With an increasing speed of network links, it is also necessary to increase the throughput of network security systems. An intrusion detection system (IDS) is one of the key components in the protection of network infrastructure. Unfortunately, the IDS has to match a large set of regular expressions (REs) in network streams, which has a negative impact on its throughput. A fast pre-filtration of network traffic can allow to achieve a higher overall throughput. Therefore, we have designed a new algorithm, which is able to select short strings that represent an RE set utilized in the IDS. Compared to previous methods, strings are selected in less than a second for an RE and can reduce network traffic up to 3.3 times better. As all selected strings have the same length, they can be used in a hash-based pre-filter, which is able to process more 100 Gbps of network traffic.
{"title":"Increasing Throughput of Intrusion Detection Systems by Hash-Based Short String Pre-filter","authors":"Tomás Fukac, V. Kosar, J. Korenek, J. Matoušek","doi":"10.1109/LCN48667.2020.9314812","DOIUrl":"https://doi.org/10.1109/LCN48667.2020.9314812","url":null,"abstract":"With an increasing speed of network links, it is also necessary to increase the throughput of network security systems. An intrusion detection system (IDS) is one of the key components in the protection of network infrastructure. Unfortunately, the IDS has to match a large set of regular expressions (REs) in network streams, which has a negative impact on its throughput. A fast pre-filtration of network traffic can allow to achieve a higher overall throughput. Therefore, we have designed a new algorithm, which is able to select short strings that represent an RE set utilized in the IDS. Compared to previous methods, strings are selected in less than a second for an RE and can reduce network traffic up to 3.3 times better. As all selected strings have the same length, they can be used in a hash-based pre-filter, which is able to process more 100 Gbps of network traffic.","PeriodicalId":245782,"journal":{"name":"2020 IEEE 45th Conference on Local Computer Networks (LCN)","volume":"1 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2020-11-16","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"121379949","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 : 2020-11-16DOI: 10.1109/LCN48667.2020.9314838
Rreze Halili, F. Z. Yousaf, Nina Slamnik-Kriještorac, Girma M. Yilma, M. Liebsch, E. B. Silva, S. Hadiwardoyo, Rafael Berkvens, M. Weyn
5G has opened up possibilities of introducing new use cases and business models that could not be perceived before. In the context of public safety, 5G offers immense opportunities towards enhancing mission success and situation awareness during emergency management. This paper introduces Back-Situation Awareness (BSA) application enabling early warning/notification to vehicles of an approaching emergency vehicle indicating its presence and the time it will arrive. Such an application is expected to give drivers enough time to create a safety corridor for the emergency vehicle to pass through safely and unhindered. We provide details on the system and application design of the BSA application leveraging Multi-Access Edge Computing (MEC) systems that complement the 5G mobile communication system. An evaluation of the application is provided by using data measurements and indicating the accuracy of the computation and notification of the Estimated Time of Arrival (ETA) based on the ETSI C-ITS protocol messages.
{"title":"Leveraging MEC in a 5G System for Enhanced Back Situation Awareness","authors":"Rreze Halili, F. Z. Yousaf, Nina Slamnik-Kriještorac, Girma M. Yilma, M. Liebsch, E. B. Silva, S. Hadiwardoyo, Rafael Berkvens, M. Weyn","doi":"10.1109/LCN48667.2020.9314838","DOIUrl":"https://doi.org/10.1109/LCN48667.2020.9314838","url":null,"abstract":"5G has opened up possibilities of introducing new use cases and business models that could not be perceived before. In the context of public safety, 5G offers immense opportunities towards enhancing mission success and situation awareness during emergency management. This paper introduces Back-Situation Awareness (BSA) application enabling early warning/notification to vehicles of an approaching emergency vehicle indicating its presence and the time it will arrive. Such an application is expected to give drivers enough time to create a safety corridor for the emergency vehicle to pass through safely and unhindered. We provide details on the system and application design of the BSA application leveraging Multi-Access Edge Computing (MEC) systems that complement the 5G mobile communication system. An evaluation of the application is provided by using data measurements and indicating the accuracy of the computation and notification of the Estimated Time of Arrival (ETA) based on the ETSI C-ITS protocol messages.","PeriodicalId":245782,"journal":{"name":"2020 IEEE 45th Conference on Local Computer Networks (LCN)","volume":"81 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2020-11-16","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"114480609","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 : 2020-11-16DOI: 10.1109/LCN48667.2020.9314803
Khalil Guibene, Marwane Ayaida, L. Khoukhi, N. Messai
The implication of Cyber-Physical Systems (CPS) in critical infrastructures (e.g., smart grids, water distribution networks, etc.) has introduced new security issues and vulnerabilities to those systems. In this paper, we demonstrate that black-box system identification using Support Vector Regression (SVR) can be used efficiently to build a model of a given industrial system even when this system is protected with a watermark-based detector. First, we briefly describe the Tennessee Eastman Process used in this study. Then, we present the principal of detection scheme and the theory behind SVR. Finally, we design an efficient black-box SVR algorithm for the Tennessee Eastman Process. Extensive simulations prove the efficiency of our proposed algorithm.
{"title":"Black-box System Identification of CPS Protected by a Watermark-based Detector","authors":"Khalil Guibene, Marwane Ayaida, L. Khoukhi, N. Messai","doi":"10.1109/LCN48667.2020.9314803","DOIUrl":"https://doi.org/10.1109/LCN48667.2020.9314803","url":null,"abstract":"The implication of Cyber-Physical Systems (CPS) in critical infrastructures (e.g., smart grids, water distribution networks, etc.) has introduced new security issues and vulnerabilities to those systems. In this paper, we demonstrate that black-box system identification using Support Vector Regression (SVR) can be used efficiently to build a model of a given industrial system even when this system is protected with a watermark-based detector. First, we briefly describe the Tennessee Eastman Process used in this study. Then, we present the principal of detection scheme and the theory behind SVR. Finally, we design an efficient black-box SVR algorithm for the Tennessee Eastman Process. Extensive simulations prove the efficiency of our proposed algorithm.","PeriodicalId":245782,"journal":{"name":"2020 IEEE 45th Conference on Local Computer Networks (LCN)","volume":"1 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2020-11-16","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"116405740","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 : 2020-11-16DOI: 10.1109/LCN48667.2020.9314768
Yuanyuan Cao, Bin Dai, Yijun Mo, Yang Xu
With the rapid development of Internet applications, diversified Quality of Service (QoS) has been required in packet routing to meet the demand of various types of applications. This paper presents an Intelligent QoS-aware Routing (IQoR) framework with the assistance of Deep Reinforcement Learning (DRL), which supports multi-class QoS provisioning for packet forwarding. The simulation results show that IQoR outperforms the widely-used benchmark routing algorithms by significantly reducing the average delay and jitter of packets.
{"title":"IQoR: An Intelligent QoS-aware Routing Mechanism with Deep Reinforcement Learning","authors":"Yuanyuan Cao, Bin Dai, Yijun Mo, Yang Xu","doi":"10.1109/LCN48667.2020.9314768","DOIUrl":"https://doi.org/10.1109/LCN48667.2020.9314768","url":null,"abstract":"With the rapid development of Internet applications, diversified Quality of Service (QoS) has been required in packet routing to meet the demand of various types of applications. This paper presents an Intelligent QoS-aware Routing (IQoR) framework with the assistance of Deep Reinforcement Learning (DRL), which supports multi-class QoS provisioning for packet forwarding. The simulation results show that IQoR outperforms the widely-used benchmark routing algorithms by significantly reducing the average delay and jitter of packets.","PeriodicalId":245782,"journal":{"name":"2020 IEEE 45th Conference on Local Computer Networks (LCN)","volume":"1 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2020-11-16","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"125484005","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 : 2020-11-16DOI: 10.1109/LCN48667.2020.9314785
T. Dahanayaka, Guillaume Jourjon, Suranga Seneviratne
HTTPS encrypted traffic can leak information about underlying contents through various statistical properties of traffic flows like packet lengths and timing, opening doors to traffic fingerprinting attacks. Recently proposed traffic fingerprinting attacks leveraged Convolutional Neural Networks (CNNs) and recorded very high accuracies undermining the state-of-the-art mitigation techniques. In this paper, we methodically dissect such CNNs with the objectives of building further accurate and scalable traffic classifiers and understanding the inner workings of such CNNs to develop effective mitigation techniques. By conducting experiments with three datasets, we show that website fingerprinting CNNs focus majorly on the initial parts of traces instead of longer windows of continuous uploads or downloads. Next, we show that traffic fingerprinting CNNs exhibit transfer-learning capabilities allowing identification of new websites with fewer data. Finally, we show that traffic fingerprinting CNNs outperform RNNs because of their resilience to random shifts in data happening due to varying network conditions.
{"title":"Understanding Traffic Fingerprinting CNNs","authors":"T. Dahanayaka, Guillaume Jourjon, Suranga Seneviratne","doi":"10.1109/LCN48667.2020.9314785","DOIUrl":"https://doi.org/10.1109/LCN48667.2020.9314785","url":null,"abstract":"HTTPS encrypted traffic can leak information about underlying contents through various statistical properties of traffic flows like packet lengths and timing, opening doors to traffic fingerprinting attacks. Recently proposed traffic fingerprinting attacks leveraged Convolutional Neural Networks (CNNs) and recorded very high accuracies undermining the state-of-the-art mitigation techniques. In this paper, we methodically dissect such CNNs with the objectives of building further accurate and scalable traffic classifiers and understanding the inner workings of such CNNs to develop effective mitigation techniques. By conducting experiments with three datasets, we show that website fingerprinting CNNs focus majorly on the initial parts of traces instead of longer windows of continuous uploads or downloads. Next, we show that traffic fingerprinting CNNs exhibit transfer-learning capabilities allowing identification of new websites with fewer data. Finally, we show that traffic fingerprinting CNNs outperform RNNs because of their resilience to random shifts in data happening due to varying network conditions.","PeriodicalId":245782,"journal":{"name":"2020 IEEE 45th Conference on Local Computer Networks (LCN)","volume":"26 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2020-11-16","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"126063866","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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