2022 IEEE Latin-American Conference on Communications (LATINCOM)最新文献_第5页

Intelligent Configuration of PHY-Layer Parameters to Reduce Energy Consumption in LoRa 智能配置物理层参数，降低LoRa的能耗

2022 IEEE Latin-American Conference on Communications (LATINCOM) Pub Date : 2022-11-30 DOI: 10.1109/LATINCOM56090.2022.10000495

Mário Nascimento Carvalho Filho, M. Campista

引用次数: 0

A Stacked Ensemble Classifier for an Intrusion Detection System in the Edge of IoT and IIoT Networks 物联网和工业物联网边缘入侵检测系统的堆叠集成分类器

2022 IEEE Latin-American Conference on Communications (LATINCOM) Pub Date : 2022-11-30 DOI: 10.1109/LATINCOM56090.2022.10000559

Giovanni Aparecido Da Silva Oliveira, P. Lima, Fabio Kon, R. Terada, D. Batista, Roberto Hirata, Mosab Hamdan

{"title":"A Stacked Ensemble Classifier for an Intrusion Detection System in the Edge of IoT and IIoT Networks","authors":"Giovanni Aparecido Da Silva Oliveira, P. Lima, Fabio Kon, R. Terada, D. Batista, Roberto Hirata, Mosab Hamdan","doi":"10.1109/LATINCOM56090.2022.10000559","DOIUrl":"https://doi.org/10.1109/LATINCOM56090.2022.10000559","url":null,"abstract":"Over the last three decades, cyberattacks have become a threat to national security. These attacks can compromise Internet of Things (IoT) and Industrial Internet of Things (IIoT) networks and affect society. In this paper, we explore Artificial Intelligence (AI) techniques with Machine and Deep Learning models to improve the performance of an anomaly-based Intrusion Detection System (IDS). We use the ensemble classifier method to find the best combination between multiple models of prediction algorithms and to stack the output of these individual models to obtain the final prediction of a new and unique model with better precision. Although, there are many ensemble approaches, finding a suitable ensemble configuration for a given dataset is still challenging. We designed an Artificial Neural Network (ANN) with the Adam optimizer to update all model weights based on training data and achieve the best performance. The result shows that it is possible to use a stacked ensemble classifier to achieve good evaluation metrics. For instance, the average accuracy achieved by one of the proposed models was 99.7%. This result was better than the results obtained by any other individual classifier. All the developed code is publicly available to ensure reproducibility.","PeriodicalId":221354,"journal":{"name":"2022 IEEE Latin-American Conference on Communications (LATINCOM)","volume":"30 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2022-11-30","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"114310938","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

Uplink Interference Management in Cellular-Connected UAV Networks Using Multi-Armed Bandit and NOMA 基于多臂强盗和NOMA的蜂窝连接无人机网络上行干扰管理

2022 IEEE Latin-American Conference on Communications (LATINCOM) Pub Date : 2022-11-30 DOI: 10.1109/LATINCOM56090.2022.10000584

Fatemeh Banaeizadeh, M. Barbeau, Joaquín García, Venkata Srinivas Kothapalli, E. Kranakis

引用次数: 0

Split-Demand and Multipath Routing in Space-Division Multiplexing Optical Networks 空分复用光网络中的分需和多径路由

2022 IEEE Latin-American Conference on Communications (LATINCOM) Pub Date : 2022-11-30 DOI: 10.1109/LATINCOM56090.2022.10000522

S. Trindade, N. Fonseca

引用次数: 0

A Proactive Algorithm for the Mitigation of Fragmentation Losses in Elastic Links 一种缓解弹性链路碎片损失的主动算法

2022 IEEE Latin-American Conference on Communications (LATINCOM) Pub Date : 2022-11-30 DOI: 10.1109/LATINCOM56090.2022.10000530

H. Waldman, R. C. Bortoletto, Vinicius F. de Souza, R. C. Almeida

引用次数: 0

Improving Multi-Band Elastic Optical Networks Performance using Behavior Induction on Deep Reinforcement Learning 基于深度强化学习的行为诱导改进多波段弹性光网络性能

2022 IEEE Latin-American Conference on Communications (LATINCOM) Pub Date : 2022-11-30 DOI: 10.1109/LATINCOM56090.2022.10000531

Marcelo Gonzalez, Felipe Condon, P. Morales, N. Jara

{"title":"Improving Multi-Band Elastic Optical Networks Performance using Behavior Induction on Deep Reinforcement Learning","authors":"Marcelo Gonzalez, Felipe Condon, P. Morales, N. Jara","doi":"10.1109/LATINCOM56090.2022.10000531","DOIUrl":"https://doi.org/10.1109/LATINCOM56090.2022.10000531","url":null,"abstract":"Deep Reinforcement Learning (DRL) has proven a considerable potential for enabling non-trivial solutions to resource allocation problems in optical networks. However, applying plain DRL does not ensure better performance than currently known best heuristics solutions. DRL demands a parameter tuning process to improve its performance. One tuning possibility is the reward function design. The reward function allows feedback to the agents on whether the actions sent to the environment were successful or not. A transparent reward function returns whether the action succeeds or not, but an elaborate reward function may allow inducing the desired behaviour to improve DRL performance. Our work designs reward functions in multi-band elastic optical networks (MB-EON) to improve the overall network blocking probability. A test environment was set up to analyze the performance of four reward functions for inducing a lower blocking probability. The proposed reward functions use band usage, link compactness, spectrum availability and link fragmentation as feedback information to the agents. Analysis was carried out using the DQN agent in the NSFNet network topology. Results show that reward function design improves the blocking probability. The best-performing one uses the band availability criteria, decreasing the blocking probability, as an average, by 22% compared to the baseline reward function, with a peak of 63,67% of improvement for a 1000 Erlang traffic load scenario.","PeriodicalId":221354,"journal":{"name":"2022 IEEE Latin-American Conference on Communications (LATINCOM)","volume":"2673 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2022-11-30","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"132786524","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

Path-Loss Prediction of Millimeter-wave using Machine Learning Techniques 基于机器学习技术的毫米波路径损耗预测

2022 IEEE Latin-American Conference on Communications (LATINCOM) Pub Date : 2022-11-30 DOI: 10.1109/LATINCOM56090.2022.10000523

Y. Nuñez, LISANDRO LOVISOLO, L. Mello, Carlos Orihuela

引用次数: 0

PyWiCh: Python Wireless Channel Simulator Python无线信道模拟器

2022 IEEE Latin-American Conference on Communications (LATINCOM) Pub Date : 2022-11-30 DOI: 10.1109/LATINCOM56090.2022.10000470

P. Belzarena

引用次数: 0

LATINCOM 2022 Message from the General Chairs 拉丁美洲会议2022年主席致辞

2022 IEEE Latin-American Conference on Communications (LATINCOM) Pub Date : 2022-11-30 DOI: 10.1109/latincom56090.2022.10000516

引用次数: 0

Design and Analysis of Neural-Network-based, Single-User Codes for Multiuser Channels 基于神经网络的多用户信道单用户代码设计与分析

2022 IEEE Latin-American Conference on Communications (LATINCOM) Pub Date : 2022-11-30 DOI: 10.1109/LATINCOM56090.2022.10000520

N. C. Matson, D. Rajan, J. Camp

{"title":"Design and Analysis of Neural-Network-based, Single-User Codes for Multiuser Channels","authors":"N. C. Matson, D. Rajan, J. Camp","doi":"10.1109/LATINCOM56090.2022.10000520","DOIUrl":"https://doi.org/10.1109/LATINCOM56090.2022.10000520","url":null,"abstract":"Inspired by its success in other fields, there have been many recent developments in the use of machine learning and neural networks to enable multiuser communication and to design efficient channel codes along with practical decoders. However, there has been little attempt to combine the results of these efforts. In this paper, for the first time, we present a neural network autoencoder architecture to jointly address both problems. The resulting codes designed by our simple and easy-to-train neural network can have arbitrary rates, are comparable to existing state-of-the-art neural network designed codes, and are directly applicable in a multiuser context. We analyze these single-user codes and characterize the design parameters which affect their performance. We then show that these same single-user codes can be used to operate close the maximum sum rate of a K-user Gaussian multiple access channel (MAC) under various SNR scenarios, without the need for retraining or learning a joint code. This improved performance is achieved by introducing a new iterative successive interference cancellation method (SIC) that outperforms traditional onion-peeling.","PeriodicalId":221354,"journal":{"name":"2022 IEEE Latin-American Conference on Communications (LATINCOM)","volume":"13 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2022-11-30","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"115177851","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