2022 IEEE 19th Annual Consumer Communications & Networking Conference (CCNC)最新文献_第10页

Monitoring Electric Vehicles on The Go 监控行驶中的电动汽车

2022 IEEE 19th Annual Consumer Communications & Networking Conference (CCNC) Pub Date : 2022-01-08 DOI: 10.1109/CCNC49033.2022.9700713

Davide Aguiari, K. Chou, Rita Tse, Giovanni Pau

引用次数: 2

Towards the Optimal Pattern of Joint Beamforming, User Scheduling and Power Allocation in a multi-RAT Network 多rat网络中联合波束形成、用户调度和功率分配的最佳模式研究

2022 IEEE 19th Annual Consumer Communications & Networking Conference (CCNC) Pub Date : 2022-01-08 DOI: 10.1109/CCNC49033.2022.9700638

Jörg von Mankowski, Hansini Vijayaraghavan, Alberto Martínez Alba, L. Goratti, W. Kellerer

引用次数: 0

Location Independent Gesture Recognition Using Channel State Information 使用通道状态信息的位置独立手势识别

2022 IEEE 19th Annual Consumer Communications & Networking Conference (CCNC) Pub Date : 2022-01-08 DOI: 10.1109/CCNC49033.2022.9700590

Israel Elujide, Chunhai Feng, Aref Shiran, Jian Li, Yonghe Liu

{"title":"Location Independent Gesture Recognition Using Channel State Information","authors":"Israel Elujide, Chunhai Feng, Aref Shiran, Jian Li, Yonghe Liu","doi":"10.1109/CCNC49033.2022.9700590","DOIUrl":"https://doi.org/10.1109/CCNC49033.2022.9700590","url":null,"abstract":"Gesture recognition has been the subject of intensive research in recent years owing to its wide applications. Unlike traditional systems, which usually require wearable sensors, many recent works have achieved the desirable gesture recognition performance using wireless channel state information from commercially available WiFi devices. However, existing works generally require training new models for different locations due to the location-dependent nature of channel state information. This paper proposes a location-independent system that can recognize gestures performed in a new location without training a new model. Our approach uses disentanglement that extricates location and other extraneous information from those needed for gesture recognition. The implementation is based on an unsupervised invariance induction framework consisting of feature extraction, a multi-output latent space, gesture recognition, and decoder modules. The key idea in designing this system is to separate gesture-dependent features from location-dependent features. Specifically, the feature extraction module consisting of a long short-term memory network is employed to select representative features; it essentially serves as an encoder to generate the latent space. During the training process, the network learns to cluster features representation for the gesture recognition and decoder by minimizing the total loss of the gesture recognition and decoder modules. We test our system with a dataset collected from various subjects performing four different gestures in multiple locations in seven rooms with different layouts. The results show that our location-independent gesture recognition system can achieve 88.69% accuracy for new locations.","PeriodicalId":269305,"journal":{"name":"2022 IEEE 19th Annual Consumer Communications & Networking Conference (CCNC)","volume":"16 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2022-01-08","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"134300171","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}

引用次数: 1

Comparing the Predictability of Sensor Modalities to Detect Stress from Wearable Sensor Data 比较可穿戴传感器数据中检测应力的传感器模式的可预测性

2022 IEEE 19th Annual Consumer Communications & Networking Conference (CCNC) Pub Date : 2022-01-08 DOI: 10.1109/CCNC49033.2022.9700682

Ryan Holder, Ramesh Kumar Sah, M. Cleveland, Hassan Ghasemzadeh

{"title":"Comparing the Predictability of Sensor Modalities to Detect Stress from Wearable Sensor Data","authors":"Ryan Holder, Ramesh Kumar Sah, M. Cleveland, Hassan Ghasemzadeh","doi":"10.1109/CCNC49033.2022.9700682","DOIUrl":"https://doi.org/10.1109/CCNC49033.2022.9700682","url":null,"abstract":"Detecting stress from wearable sensor data enables those struggling with unhealthy stress coping mechanisms to better manage their stress. Previous studies have investigated how mechanisms for detecting stress from sensor data can be optimized, comparing alternative algorithms and approaches to find the best possible outcome. One strategy to make these mechanisms more accessible is to reduce the number of sensors that wearable devices must support. Reducing the number of sensors will enable wearable devices to be a smaller size, require less battery, and last longer, making use of these wearable devices more accessible. To progress towards this more convenient stress detection mechanism, we investigate how learning algorithms perform on singular modalities and compare the outcome with results from multiple modalities. We found that singular modalities performed comparably or better than combined modalities on two stress-detection datasets, suggesting that there is promise for detecting stress with fewer sensor requirements. From the four modalities we tested, acceleration, blood volume pulse, and electrodermal activity, we saw acceleration and electrodermal activity to stand out in a few cases, but all modalities showed potential. Our results are acquired from testing with random holdout and leave-one-subject-out validation, using several machine learning techniques. Our results can inspire work on optimizing stress detection with singular modalities to make the benefits of these detection mechanisms more convenient.","PeriodicalId":269305,"journal":{"name":"2022 IEEE 19th Annual Consumer Communications & Networking Conference (CCNC)","volume":"19 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2022-01-08","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"116650761","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}

引用次数: 4

Multi-factor Behavioral Authentication Using Correlations Enhanced by Neural Network-based Score Fusion 基于神经网络分数融合的多因素行为认证

2022 IEEE 19th Annual Consumer Communications & Networking Conference (CCNC) Pub Date : 2022-01-08 DOI: 10.1109/CCNC49033.2022.9700626

Akira Miyazawa, Tran Thao Phuong, R. Yamaguchi

{"title":"Multi-factor Behavioral Authentication Using Correlations Enhanced by Neural Network-based Score Fusion","authors":"Akira Miyazawa, Tran Thao Phuong, R. Yamaguchi","doi":"10.1109/CCNC49033.2022.9700626","DOIUrl":"https://doi.org/10.1109/CCNC49033.2022.9700626","url":null,"abstract":"In recent years, personal behavioral authentication has been proposed as a new authentication method to support traditional knowledge-based, possession-based, and biometrics-based authentication. Most of the previous behavioral authentication research relied on historical behavioral patterns or trained classification models, thus requiring a large amount of preliminary data. In addition, while many studies utilized multiple authentication factors, authentication scores of each element were calculated independently without using correlations between them. This paper proposes a new authentication approach that uses behavioral correlations between each factor. In order to demonstrate the effectiveness of our proposal, we constructed a correlation-based authentication method using the data collected from smartphones and wearable activity trackers, including GPS locations, Wi-Fi access points, and activity types inferred from the metabolic equivalent of task (MET). Since the proposed method matches the data from multiple sensors to verify the request rather than utilizing the pattern extracted from the historical data, it does not require a large amount of preliminary data. We also employed a neural network-based score fusion method that aggregates the three authentication scores to improve the final authentication accuracy. The experimental result showed that the proposed method could achieve a half total error rate (HTER, an arithmetic average of a false rejection rate and false acceptance rate) of merely 8.0% that is much lower than other classification methods using the same dataset.","PeriodicalId":269305,"journal":{"name":"2022 IEEE 19th Annual Consumer Communications & Networking Conference (CCNC)","volume":"12 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2022-01-08","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"121930863","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}

引用次数: 1

20-µs Accuracy Time-Synchronization Method using Bluetooth Low Energy for Internet-of-Things Sensors 基于低功耗蓝牙的物联网传感器20µs精度时间同步方法

2022 IEEE 19th Annual Consumer Communications & Networking Conference (CCNC) Pub Date : 2022-01-08 DOI: 10.1109/CCNC49033.2022.9700687

Masayasu Harada, S. Izumi, Ryosuke Kozeni, Yukiko Yoshikawa, T. Ishii, H. Kawaguchi, Shohei Uemura, Kaname Araki

引用次数: 2

Evaluating Efficiency and Security of Connected and Autonomous Vehicle Applications 评估联网和自动驾驶汽车应用的效率和安全性

2022 IEEE 19th Annual Consumer Communications & Networking Conference (CCNC) Pub Date : 2022-01-08 DOI: 10.1109/CCNC49033.2022.9700691

Curtis R. Taylor, J. Carter, S. Huff, Eric J. Nafziger, Jackeline Rios-Torres, B. Zhang, J. Turcotte

{"title":"Evaluating Efficiency and Security of Connected and Autonomous Vehicle Applications","authors":"Curtis R. Taylor, J. Carter, S. Huff, Eric J. Nafziger, Jackeline Rios-Torres, B. Zhang, J. Turcotte","doi":"10.1109/CCNC49033.2022.9700691","DOIUrl":"https://doi.org/10.1109/CCNC49033.2022.9700691","url":null,"abstract":"Evaluating efficiency and security of Connected and Autonomous Vehicles (CAVs) requires an environment that can support applications and measurements under real-world conditions. This work introduces our implementation and evaluation of a Connected and Autonomous Vehicle Research Environment (CAVRE). We implement and evaluate an existing CAV application called Cooperative Adaptive Cruise Control (CACC) using physical Vehicle-to-Vehicle (V2V) communications between a virtual agent and a real autonomous vehicle operating on a steerable dynamometer. CAVRE allows the follower to autonomously control longitudinal behavior on the dynamometer in order to maintain a steady following time gap from the leader. The effects of a wireless jamming attack on CACC and fuel efficiency is also evaluated. By executing attacks in a controlled environment, we learn how compromised communications can degrade CAV applications. We show that jamming V2V communications can impact CACC’s string stability and decrease fuel efficiency by more than 50%.","PeriodicalId":269305,"journal":{"name":"2022 IEEE 19th Annual Consumer Communications & Networking Conference (CCNC)","volume":"40 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2022-01-08","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"125113404","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}

引用次数: 1

A Host-based Investigation of IPv6 in Academia: The Cases of Japan and Vietnam 学术界基于主机的IPv6研究:以日本和越南为例

2022 IEEE 19th Annual Consumer Communications & Networking Conference (CCNC) Pub Date : 2022-01-08 DOI: 10.1109/CCNC49033.2022.9700604

Kien Nguyen, Phi-Le Nguyen, H. Sekiya

引用次数: 0

Towards an Optimal Feature Selection Method for AI-Based DDoS Detection System 基于ai的DDoS检测系统最优特征选择方法研究

2022 IEEE 19th Annual Consumer Communications & Networking Conference (CCNC) Pub Date : 2022-01-08 DOI: 10.1109/CCNC49033.2022.9700569

Sajal Saha, Annita Tahsin Priyoti, A. Sharma, Anwar Haque

{"title":"Towards an Optimal Feature Selection Method for AI-Based DDoS Detection System","authors":"Sajal Saha, Annita Tahsin Priyoti, A. Sharma, Anwar Haque","doi":"10.1109/CCNC49033.2022.9700569","DOIUrl":"https://doi.org/10.1109/CCNC49033.2022.9700569","url":null,"abstract":"Cyber-attacks are increasing rapidly, so developing effective intrusion detection and prevention tools for a secure and safer cyberspace is crucial. DDoS (Distributed Denial of Services) is one of the most well-known digital threats, endangering any cyber-physical system. DDoS prevents the host from serving the legitimate traffic by overflowing the host node with unwanted service requests. Nowadays, machine learning-based IDS (Intrusion Detection System) uses different Feature Selection (FS) methods to extract a feature subset from a large dataset to increase the model performance and decrease the training time. In this research work, we used the UNSW-NB15 dataset [1] to conduct a comprehensive analysis for evaluating the performance of different FS techniques in DDoS attack classification using both Machine Learning (ML) and Deep Learning (DL) models. Furthermore, an Ensemble Feature Selection (EN-FS) technique called Majority Voting (MV) has been implemented to combine the individual FS method’s output to extract an optimal feature set. Our ensemble feature selection approach significantly reduces the features from 42 to 15, which is 64% less than the original features. Lastly, an extensive experiment has been performed to estimate and compare the performance of individual, ensemble, and original feature set in both ML and DL-based DDoS detection systems. According to our analysis, the ensemble feature set-based classification model exhibits higher accuracy, lower False Positive Rate (FPR), and better execution time than the other individual feature set-based models.","PeriodicalId":269305,"journal":{"name":"2022 IEEE 19th Annual Consumer Communications & Networking Conference (CCNC)","volume":"98-D 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2022-01-08","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"124558926","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}

引用次数: 9

Trusted Decentralized Federated Learning 可信分散联邦学习

2022 IEEE 19th Annual Consumer Communications & Networking Conference (CCNC) Pub Date : 2022-01-08 DOI: 10.1109/CCNC49033.2022.9700624

Anousheh Gholami, Nariman Torkzaban, J. Baras

{"title":"Trusted Decentralized Federated Learning","authors":"Anousheh Gholami, Nariman Torkzaban, J. Baras","doi":"10.1109/CCNC49033.2022.9700624","DOIUrl":"https://doi.org/10.1109/CCNC49033.2022.9700624","url":null,"abstract":"Federated learning (FL) has received significant attention from both academia and industry, as an emerging paradigm for building machine learning models in a communication-efficient and privacy preserving manner. It enables potentially a massive number of resource constrained agents (e.g. mobile devices and IoT devices) to train a model by a repeated process of local training on agents and centralized model aggregation on a central server. To overcome the single-point-of-failure and scalability issues of the traditional FL frameworks, decentralized (server-less) FL has been proposed. In a decentralized FL setting, agents implement consensus techniques by exchanging local model updates. Despite bypassing the direct exchange of raw data between the collaborating agents, this scheme is still vulnerable to various security and privacy threats such as data poisoning attack.In this paper, we propose trust as a metric to measure the trustworthiness of the FL agents and thereby enhance the security of the FL training. We first elaborate on trust as a security metric by presenting a mathematical framework for trust computation and aggregation within a multi-agent system. We then discuss how this framework can be incorporated within a decentralized FL setup introducing the trusted decentralized FL algorithm. Finally, we validate our theoretical findings by means of numerical experiments.","PeriodicalId":269305,"journal":{"name":"2022 IEEE 19th Annual Consumer Communications & Networking Conference (CCNC)","volume":"73 5 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2022-01-08","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"123407111","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}

引用次数: 12