Detection of the Primary User's Behavior for the Intervention of the Secondary User Using Machine Learning

被引：3

作者：

Zambrano Soto, Deisy Dayana ^{[1
]}

Salcedo Parra, Octavio Jose ^{[1
,2
]}

Lopez Sarmiento, Danilo Alfonso ^{[1
]}

机构：

[1] Univ Distrital Francisco Jose de Caldas, Internet Res Grp, Fac Engn, Bogota, Colombia

[2] Univ Nacl Colombia, Fac Engn, Dept Syst & Ind Engn, Bogota, Colombia

来源：

FUTURE DATA AND SECURITY ENGINEERING, FDSE 2018 | 2018年 / 11251卷

关键词：

Cognitive Radio; KNN; LR; Machine learning; Primary users (PU); SVM;

D O I：

10.1007/978-3-030-03192-3_15

中图分类号：

TP301 [理论、方法];

学科分类号：

081202 ;

摘要：

The predictive analysis for the spectral decision with automatic Learning is a task that is currently challenging. Some automatic Learning techniques are shown in order to predict the presence or absence of a primary user (PU) in Cognitive Radio. Four machine learning methods are examined including the K-nearest neighbors (KNN), the support vector machines (SVM), logistic regression (LR) and decision tree (DT) classifiers. These predictive models are built based on data and their performance is compared with the purpose of selecting the best classifier that can predict spectral occupancy.

引用

页码：200 / 213

页数：14

共 50 条

[21] BGP Dataset-Based Malicious User Activity Detection Using Machine Learning
Park, Hansol
Kim, Kookjin
Shin, Dongil
Shin, Dongkyoo
[J]. INFORMATION, 2023, 14 (09)
[22] Machine Learning-based Primary User Emulation Attack Detection In Cognitive Radio Networks using Pattern Described Link-Signature (PDLS)
Albehadili, Abdulsahib
Ali, Atif
Jahan, Farha
Javaid, Ahmad Y.
Oluoch, Jared
Devabhaktuni, Vijay
[J]. 2019 WIRELESS TELECOMMUNICATIONS SYMPOSIUM (WTS), 2019,
[23] User Behavior Analysis with Machine Learning Techniques in Cloud Computing Architectures
Callara, Matias
Wira, Patrice
[J]. PROCEEDINGS OF THE 2018 INTERNATIONAL CONFERENCE ON APPLIED SMART SYSTEMS (ICASS), 2018,
[24] Machine Learning and Event-Based User and Entity Behavior Analysis
Onal, Vedat
Arslan, Halil
Gormez, Yasin
[J]. 32ND IEEE SIGNAL PROCESSING AND COMMUNICATIONS APPLICATIONS CONFERENCE, SIU 2024, 2024,
[25] Machine Learning-based Electric Vehicle User Behavior Prediction
Lilhore, Aakash
Prasad, Kavita Kiran
Agarwal, Vivek
[J]. 2023 IEEE IAS GLOBAL CONFERENCE ON RENEWABLE ENERGY AND HYDROGEN TECHNOLOGIES, GLOBCONHT, 2023,
[26] Exploiting User Behavior to Predict Parking Availability through Machine Learning
Bassetti, Enrico
Berti, Andrea
Bisante, Alba
Magnante, Andrea
Panizzi, Emanuele
[J]. SMART CITIES, 2022, 5 (04): : 1243 - 1266
[27] Prediction of User's Purchase Intention Based on Machine Learning
Liu Bing
Shi Yuliang
[J]. 2016 3RD INTERNATIONAL CONFERENCE ON SOFT COMPUTING & MACHINE INTELLIGENCE (ISCMI 2016), 2016, : 99 - 103
[28] Machine Learning for Intelligent Mobile User Interfaces using TensorFlow
Mayer, Sven
Le, Huy Viet
Henze, Niels
[J]. PROCEEDINGS OF THE 19TH INTERNATIONAL CONFERENCE ON HUMAN-COMPUTER INTERACTION WITH MOBILE DEVICES AND SERVICES (MOBILEHCI '17), 2017,
[29] User Classification in Electronic Devices Using Machine Learning Methods
Liu, Xinglu
Wang, Wan
Chan, Wai Kin Victor
Kuan, Chiung Ying
Lee, Junyoung
[J]. 2019 IEEE INTERNATIONAL CONFERENCE ON INDUSTRIAL ENGINEERING AND ENGINEERING MANAGEMENT (IEEM), 2019, : 1553 - 1556
[30] User Experience Design Using Machine Learning: A Systematic Review
Abbas, Abdallah M. H.
Ghauth, Khairil Imran
Ting, Choo-Yee
[J]. IEEE ACCESS, 2022, 10 : 51501 - 51514

← 1 2 3 4 5 →