Proceedings of the 16th ACM Symposium on QoS and Security for Wireless and Mobile Networks最新文献

Session details: Session 6: Security 2 会话详细信息:会话6:安全

Proceedings of the 16th ACM Symposium on QoS and Security for Wireless and Mobile Networks Pub Date : 2020-11-16 DOI: 10.1145/3436362

A. Mostefaoui

引用次数: 0

Session details: Session 2: Security 1 会话详细信息:会话2:安全

Proceedings of the 16th ACM Symposium on QoS and Security for Wireless and Mobile Networks Pub Date : 2020-11-16 DOI: 10.1145/3436358

G. Gianini

引用次数: 0

Session details: Session 5:Mobile/Edge Computing 会议详情:会议5:移动/边缘计算

Proceedings of the 16th ACM Symposium on QoS and Security for Wireless and Mobile Networks Pub Date : 2020-11-16 DOI: 10.1145/3436361

Samir Chouali

引用次数: 0

Model-Oriented Methodology for Developing a Social Based Healthcare System 面向模型的方法开发基于社会的医疗保健系统

Proceedings of the 16th ACM Symposium on QoS and Security for Wireless and Mobile Networks Pub Date : 2020-11-16 DOI: 10.1145/3416013.3426452

Moustafa Fayad, A. Mostefaoui, Samir Chouali, S. Benbernou

引用次数: 2

Evaluation of SAC-OCDMA System Performance in Terms of BER and Q-Factor Using FBGs and M-Sequence Code 用fbg和m序列码评价SAC-OCDMA系统的误码率和q因子

Proceedings of the 16th ACM Symposium on QoS and Security for Wireless and Mobile Networks Pub Date : 2020-11-16 DOI: 10.1145/3416013.3426444

S. Boukricha, K. Ghoumid, E. Ar-reyouchi, Yusifli Elmar, R. Yahiaoui, O. Elmazria

{"title":"Evaluation of SAC-OCDMA System Performance in Terms of BER and Q-Factor Using FBGs and M-Sequence Code","authors":"S. Boukricha, K. Ghoumid, E. Ar-reyouchi, Yusifli Elmar, R. Yahiaoui, O. Elmazria","doi":"10.1145/3416013.3426444","DOIUrl":"https://doi.org/10.1145/3416013.3426444","url":null,"abstract":"In this paper, we investigate the performance of Spectral Amplitude Coding $-$ Optical Code Division Multiple Access (SAC-OCDMA) system using the m-sequence code as a signature sequence for the coding and the decoding processes. The first simulations have been carried out for a system of 8 users to investigate its performance for different values of the optical source power and the FBG's bandwidth. Then, the performance of a 10-user SAC-OCDMA system was demonstrated in terms of BER and Q-factor versus the number of active users for different transmission distances. The simulation results show that the proposed SAC-OCDMA system performs well at an optical power of -110 dBm and a FBG bandwith of 0.3 nm. In addition, for a transmission distance of 5 km, the number of possible active users is 9 users, while for 45 km, the number of possible active users is decreasing to 5 users. This shows that there is a non-proportional relationship between transmission distance and number of users, so that as the distance increases, the number of users decreases.","PeriodicalId":103892,"journal":{"name":"Proceedings of the 16th ACM Symposium on QoS and Security for Wireless and Mobile Networks","volume":"103 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":"124208660","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: 0

A Supervised Machine Learning Approach for DASH Video QoE Prediction in 5G Networks 5G网络中DASH视频QoE预测的监督式机器学习方法

Proceedings of the 16th ACM Symposium on QoS and Security for Wireless and Mobile Networks Pub Date : 2020-11-16 DOI: 10.1145/3416013.3426458

Simone Ferlin, Christian Esteve Rothenberg, R. Mustafa, Darijo Raca, Jason J. Quinlan

{"title":"A Supervised Machine Learning Approach for DASH Video QoE Prediction in 5G Networks","authors":"Simone Ferlin, Christian Esteve Rothenberg, R. Mustafa, Darijo Raca, Jason J. Quinlan","doi":"10.1145/3416013.3426458","DOIUrl":"https://doi.org/10.1145/3416013.3426458","url":null,"abstract":"Future fifth generation (5G) networks are envisioned to provide improved Quality-of-Experience (QoE) for applications by means of higher data rates, low and ultra-reliable latency and very high reliability. Proving increasing beneficial for mobile devices running multimedia applications. However, there exist two main co-related challenges in multimedia delivery in 5G. Namely, balancing operator provisioning and client expectations. To this end, we investigate how to build a QoE-aware network that guarantees at run-time that the end-to-end user experience meets the end users' expectations at the same that the network's Quality of Service (QoS) varies. The contribution of this paper is twofold: first, we consider a Dynamic Adaptive Streaming over HTTP (DASH) video application in a realistic emulation environment derived from real 5G traces in static and mobility scenarios to assess the QoE performance of three state-of-art Adaptive Bitrate Streaming (ABS) algorithm categories: Hybrid - Elastic and Arbiter+; buffer-based - BBA and Logistic; and rate-based - Exponential and Conventional. Second, we propose a Machine Learning (ML) classifier to predict user satisfaction which considers network metrics, such as RTT, throughput, and number of packets. Our proposed model does not rely on knowledge about the application or specific traffic information. We show that our ML classifiers achieve a QoE prediction accuracy of 87.63 % and 79 % for static and mobility scenarios, respectively.","PeriodicalId":103892,"journal":{"name":"Proceedings of the 16th ACM Symposium on QoS and Security for Wireless and Mobile Networks","volume":"138 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":"129827484","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: 10

A Performance Evaluation of Time-Series Mobility Prediction for Connected Vehicular Networks 车联网时间序列移动性预测的性能评价

Proceedings of the 16th ACM Symposium on QoS and Security for Wireless and Mobile Networks Pub Date : 2020-11-16 DOI: 10.1145/3416013.3426460

Noura Aljeri, A. Boukerche

{"title":"A Performance Evaluation of Time-Series Mobility Prediction for Connected Vehicular Networks","authors":"Noura Aljeri, A. Boukerche","doi":"10.1145/3416013.3426460","DOIUrl":"https://doi.org/10.1145/3416013.3426460","url":null,"abstract":"The role of connected vehicular networks has become vital for the future of smart and modern cities, as it can be envisioned as a stand alone connected network or a bridge between various networks, and end-users. Vehicular networks design provides safety, traffic control management, and cruise control services, among others. Many applications and protocols such as mobility management, service discovery, and routing were proposed over the past years to elevate the performance of vehicular networks communication and connectivity. In that context, understanding the vehicles' mobility characteristic, behavior, and pattern would assist those applications to become user-centric, adaptive, and proactive based on the dynamics of vehicles' projections. In this paper, we evaluate the performance of time-series forecasting techniques for vehicular mobility prediction. We first describe and extract vehicles mobility features into time-series sequences and discuss several machine learning techniques. Then, we compare the performance of recent time-series ML-based techniques, LSTM, and GRNN in terms of complexity and accuracy.","PeriodicalId":103892,"journal":{"name":"Proceedings of the 16th ACM Symposium on QoS and Security for Wireless and Mobile Networks","volume":"68 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":"130201485","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: 2

Deep Learning for Resource-Limited Devices 资源有限设备的深度学习

Proceedings of the 16th ACM Symposium on QoS and Security for Wireless and Mobile Networks Pub Date : 2020-11-16 DOI: 10.1145/3416013.3426445

M. Merzoug, A. Mostefaoui, Mohammed H. Kechout, Sebti Tamraoui

{"title":"Deep Learning for Resource-Limited Devices","authors":"M. Merzoug, A. Mostefaoui, Mohammed H. Kechout, Sebti Tamraoui","doi":"10.1145/3416013.3426445","DOIUrl":"https://doi.org/10.1145/3416013.3426445","url":null,"abstract":"In recent years, deep neural networks have revolutionized the development of intelligent systems and applications in many areas. Despite their numerous advantages and potentials, these intelligent models still suffer from several issues. Among them, the fact that they became very complex with millions of parameters. That is, requiring more resources and time, and being unsuitable for small restricted devices. To contribute in this direction, this paper presents (1) some state-of-the-art lightweight architectures that were specifically designed for small-sized devices, and (2) some recent solutions that have been proposed to optimize/compress classical deep neural networks to allow their deployment on embedded systems. In this paper, we also present our four-staged approach that aims to further enhance the performance of lightweight object-detection models. The conducted performance evaluations have demonstrated that the proposed four layers (basic model initialization, pruning, quantization, and adaptation) considerably reduce the redundancy of filters and model size with negligible performance degradation.","PeriodicalId":103892,"journal":{"name":"Proceedings of the 16th ACM Symposium on QoS and Security for Wireless and Mobile Networks","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":"133234510","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: 5

Novelty-based Intrusion Detection of Sensor Attacks on Unmanned Aerial Vehicles 基于新颖度的无人机传感器入侵检测

Proceedings of the 16th ACM Symposium on QoS and Security for Wireless and Mobile Networks Pub Date : 2020-11-16 DOI: 10.1145/3416013.3426446

Jason Whelan, Thanigajan Sangarapillai, Omar Minawi, Abdulaziz Almehmadi, K. El-Khatib

{"title":"Novelty-based Intrusion Detection of Sensor Attacks on Unmanned Aerial Vehicles","authors":"Jason Whelan, Thanigajan Sangarapillai, Omar Minawi, Abdulaziz Almehmadi, K. El-Khatib","doi":"10.1145/3416013.3426446","DOIUrl":"https://doi.org/10.1145/3416013.3426446","url":null,"abstract":"Unmanned Aerial Vehicles (UAVs) have proven to be a useful technology in numerous industries including industrial control systems surveillance, law enforcement, and military operations. Due to their heavy reliance on wireless protocols and hostile operating environments, UAVs face a large threat landscape. As attacks against UAVs increase, an intelligent intrusion detection system (IDS) is needed to aid the UAV in identifying attacks. The UAV domain presents unique challenges for intelligent IDS development, such as the variety of sensors, communication protocols, UAV platforms, control configurations, and dataset availability. In this paper, we propose a novelty-based approach to intrusion detection in UAVs by using one-class classifiers. One-class classifiers require only non-anomalous data to exist in the training set. This allows for the use of flight logs as training data, which are created by most UAVs during flight by default. Principal Component Analysis is applied to sensor logs for dimensionality reduction, and one-class classifier models are generated per sensor. A number of one-class classifiers are selected: One-Class Support Vector Machine, Autoencoder Neural Network, and Local Outlier Factor. The pre-processing, feature selection, training, and tuning of the selected algorithms is discussed. GPS spoofing is used throughout the paper as a common example of an external sensor-based attack. This approach shows to be effective across multiple UAV platforms with platform-specific F1 scores up to 99.56% and 99.73% for benign and malicious sensor readings respectively.","PeriodicalId":103892,"journal":{"name":"Proceedings of the 16th ACM Symposium on QoS and Security for Wireless and Mobile Networks","volume":"53 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":"128023379","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: 28

Session details: Session 1: Radio Communication 会议详情:第1部分:无线电通信

Proceedings of the 16th ACM Symposium on QoS and Security for Wireless and Mobile Networks Pub Date : 2020-11-16 DOI: 10.1145/3436357

A. Mostefaoui

引用次数: 0