Artículos de revistas sobre el tema "Machine learning, big data, anomaly detection, network monitoring"
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Oprea, Simona-Vasilica, Adela Bâra, Florina Camelia Puican y Ioan Cosmin Radu. "Anomaly Detection with Machine Learning Algorithms and Big Data in Electricity Consumption". Sustainability 13, n.º 19 (2 de octubre de 2021): 10963. http://dx.doi.org/10.3390/su131910963.
Texto completoAlnafessah, Ahmad y Giuliano Casale. "Artificial neural networks based techniques for anomaly detection in Apache Spark". Cluster Computing 23, n.º 2 (23 de octubre de 2019): 1345–60. http://dx.doi.org/10.1007/s10586-019-02998-y.
Texto completoBorghesi, Andrea, Andrea Bartolini, Michele Lombardi, Michela Milano y Luca Benini. "Anomaly Detection Using Autoencoders in High Performance Computing Systems". Proceedings of the AAAI Conference on Artificial Intelligence 33 (17 de julio de 2019): 9428–33. http://dx.doi.org/10.1609/aaai.v33i01.33019428.
Texto completoAlbattah, Albatul y Murad A. Rassam. "A Correlation-Based Anomaly Detection Model for Wireless Body Area Networks Using Convolutional Long Short-Term Memory Neural Network". Sensors 22, n.º 5 (2 de marzo de 2022): 1951. http://dx.doi.org/10.3390/s22051951.
Texto completoChen, Naiyue, Yi Jin, Yinglong Li y Luxin Cai. "Trust-based federated learning for network anomaly detection". Web Intelligence 19, n.º 4 (20 de enero de 2022): 317–27. http://dx.doi.org/10.3233/web-210475.
Texto completoDo, ChoXuan, Nguyen Quang Dam y Nguyen Tung Lam. "Optimization of network traffic anomaly detection using machine learning". International Journal of Electrical and Computer Engineering (IJECE) 11, n.º 3 (1 de junio de 2021): 2360. http://dx.doi.org/10.11591/ijece.v11i3.pp2360-2370.
Texto completoVajda, Daniel, Adrian Pekar y Karoly Farkas. "Towards Machine Learning-based Anomaly Detection on Time-Series Data". Infocommunications journal 13, n.º 1 (2021): 35–44. http://dx.doi.org/10.36244/icj.2021.1.5.
Texto completoNovoa-Paradela, David, Óscar Fontenla-Romero y Bertha Guijarro-Berdiñas. "Adaptive Real-Time Method for Anomaly Detection Using Machine Learning". Proceedings 54, n.º 1 (22 de agosto de 2020): 38. http://dx.doi.org/10.3390/proceedings2020054038.
Texto completoChimphlee, Siriporn y Witcha Chimphlee. "Machine learning to improve the performance of anomaly-based network intrusion detection in big data". Indonesian Journal of Electrical Engineering and Computer Science 30, n.º 2 (1 de mayo de 2023): 1106. http://dx.doi.org/10.11591/ijeecs.v30.i2.pp1106-1119.
Texto completoKáš, M. y F. F. Wamba. "Anomaly detection-based condition monitoring". Insight - Non-Destructive Testing and Condition Monitoring 64, n.º 8 (1 de agosto de 2022): 453–58. http://dx.doi.org/10.1784/insi.2022.64.8.453.
Texto completoPreuveneers, Davy, Vera Rimmer, Ilias Tsingenopoulos, Jan Spooren, Wouter Joosen y Elisabeth Ilie-Zudor. "Chained Anomaly Detection Models for Federated Learning: An Intrusion Detection Case Study". Applied Sciences 8, n.º 12 (18 de diciembre de 2018): 2663. http://dx.doi.org/10.3390/app8122663.
Texto completoAhn, Hyojung, Han-Lim Choi, Minguk Kang y SungTae Moon. "Learning-Based Anomaly Detection and Monitoring for Swarm Drone Flights". Applied Sciences 9, n.º 24 (13 de diciembre de 2019): 5477. http://dx.doi.org/10.3390/app9245477.
Texto completoAlkahtani, Hasan, Theyazn H. H. Aldhyani y Mohammed Al-Yaari. "Adaptive Anomaly Detection Framework Model Objects in Cyberspace". Applied Bionics and Biomechanics 2020 (9 de diciembre de 2020): 1–14. http://dx.doi.org/10.1155/2020/6660489.
Texto completoTang, Xiaoyu, Sijia Xu y Hui Ye. "Labeling Expert: A New Multi-Network Anomaly Detection Architecture Based on LNN-RLSTM". Applied Sciences 13, n.º 1 (31 de diciembre de 2022): 581. http://dx.doi.org/10.3390/app13010581.
Texto completoThoidis, Iordanis, Marios Giouvanakis y George Papanikolaou. "Semi-Supervised Machine Condition Monitoring by Learning Deep Discriminative Audio Features". Electronics 10, n.º 20 (11 de octubre de 2021): 2471. http://dx.doi.org/10.3390/electronics10202471.
Texto completoRamesh, Jayroop, Sakib Shahriar, A. R. Al-Ali, Ahmed Osman y Mostafa F. Shaaban. "Machine Learning Approach for Smart Distribution Transformers Load Monitoring and Management System". Energies 15, n.º 21 (27 de octubre de 2022): 7981. http://dx.doi.org/10.3390/en15217981.
Texto completoPoorvadevi, Dr R., Bodala Yaswanth Nikhil y Darisi Venkata Sravan Kumar. "An Intelligent Data-Driven Model to Secure Intra- Vehicle Communications based on Machine Learning". International Journal for Research in Applied Science and Engineering Technology 10, n.º 3 (31 de marzo de 2022): 1329–34. http://dx.doi.org/10.22214/ijraset.2022.40863.
Texto completoLaskar, Md Tahmid Rahman, Jimmy Xiangji Huang, Vladan Smetana, Chris Stewart, Kees Pouw, Aijun An, Stephen Chan y Lei Liu. "Extending Isolation Forest for Anomaly Detection in Big Data via K-Means". ACM Transactions on Cyber-Physical Systems 5, n.º 4 (31 de octubre de 2021): 1–26. http://dx.doi.org/10.1145/3460976.
Texto completoHuang, Yu Liu, Junge y Jihao Wang. "Environmental Safety Monitoring System Based on Microservice Architecture and Machine Learning". South Florida Journal of Development 2, n.º 2 (4 de junio de 2021): 2894–902. http://dx.doi.org/10.46932/sfjdv2n2-133.
Texto completoBasora, Luis, Paloma Bry, Xavier Olive y Floris Freeman. "Aircraft Fleet Health Monitoring with Anomaly Detection Techniques". Aerospace 8, n.º 4 (7 de abril de 2021): 103. http://dx.doi.org/10.3390/aerospace8040103.
Texto completoSireesha, P., Kongara Narmada, Kadurkapu Chandana, Govindu Badri y Kalakonda Shirisha. "Detection of Diabetes Using 5G Network". International Journal for Research in Applied Science and Engineering Technology 10, n.º 11 (30 de noviembre de 2022): 1656–60. http://dx.doi.org/10.22214/ijraset.2022.47622.
Texto completoMokhtari, Sohrab, Alireza Abbaspour, Kang K. Yen y Arman Sargolzaei. "A Machine Learning Approach for Anomaly Detection in Industrial Control Systems Based on Measurement Data". Electronics 10, n.º 4 (8 de febrero de 2021): 407. http://dx.doi.org/10.3390/electronics10040407.
Texto completoYe, Jiaxing, Yuichi Kurashima, Takeshi Kobayashi, Hiroshi Tsuda, Teruyoshi Takahara y Wataru Sakurai. "An Efficient In-Situ Debris Flow Monitoring System over a Wireless Accelerometer Network". Remote Sensing 11, n.º 13 (26 de junio de 2019): 1512. http://dx.doi.org/10.3390/rs11131512.
Texto completoEl-Khchine, Radouane, Amine Amar, Zine Elabidine Guennoun, Charaf Bensouda y Youness Laaroussi. "Machine Learning for Supply Chain’s Big Data: State of the art and application to Social Networks’ data". MATEC Web of Conferences 200 (2018): 00015. http://dx.doi.org/10.1051/matecconf/201820000015.
Texto completoKaraçay, Leyli, Erkay Savaş y Halit Alptekin. "Intrusion Detection Over Encrypted Network Data". Computer Journal 63, n.º 4 (17 de noviembre de 2019): 604–19. http://dx.doi.org/10.1093/comjnl/bxz111.
Texto completoDiro, Abebe, Naveen Chilamkurti, Van-Doan Nguyen y Will Heyne. "A Comprehensive Study of Anomaly Detection Schemes in IoT Networks Using Machine Learning Algorithms". Sensors 21, n.º 24 (13 de diciembre de 2021): 8320. http://dx.doi.org/10.3390/s21248320.
Texto completoIbrahim, Juma y Slavko Gajin. "Entropy-based network traffic anomaly classification method resilient to deception". Computer Science and Information Systems, n.º 00 (2021): 45. http://dx.doi.org/10.2298/csis201229045i.
Texto completoLatif, Zohaib, Qasim Umer, Choonhwa Lee, Kashif Sharif, Fan Li y Sujit Biswas. "A Machine Learning-Based Anomaly Prediction Service for Software-Defined Networks". Sensors 22, n.º 21 (2 de noviembre de 2022): 8434. http://dx.doi.org/10.3390/s22218434.
Texto completoChristyawan, Tomi Yahya, Ahmad Afif Supianto y Wayan Firdaus Mahmudy. "Anomaly-based intrusion detector system using restricted growing self organizing map". Indonesian Journal of Electrical Engineering and Computer Science 13, n.º 3 (1 de marzo de 2019): 919. http://dx.doi.org/10.11591/ijeecs.v13.i3.pp919-926.
Texto completoLi, Zhi, Fei Fei y Guanglie Zhang. "Edge-to-Cloud IIoT for Condition Monitoring in Manufacturing Systems with Ubiquitous Smart Sensors". Sensors 22, n.º 15 (7 de agosto de 2022): 5901. http://dx.doi.org/10.3390/s22155901.
Texto completoThaseen, Ikram Sumaiya, Vanitha Mohanraj, Sakthivel Ramachandran, Kishore Sanapala y Sang-Soo Yeo. "A Hadoop Based Framework Integrating Machine Learning Classifiers for Anomaly Detection in the Internet of Things". Electronics 10, n.º 16 (13 de agosto de 2021): 1955. http://dx.doi.org/10.3390/electronics10161955.
Texto completoApostol, Elena-Simona, Ciprian-Octavian Truică, Florin Pop y Christian Esposito. "Change Point Enhanced Anomaly Detection for IoT Time Series Data". Water 13, n.º 12 (10 de junio de 2021): 1633. http://dx.doi.org/10.3390/w13121633.
Texto completoNaveed, Muhammad, Fahim Arif, Syed Muhammad Usman, Aamir Anwar, Myriam Hadjouni, Hela Elmannai, Saddam Hussain, Syed Sajid Ullah y Fazlullah Umar. "A Deep Learning-Based Framework for Feature Extraction and Classification of Intrusion Detection in Networks". Wireless Communications and Mobile Computing 2022 (8 de agosto de 2022): 1–11. http://dx.doi.org/10.1155/2022/2215852.
Texto completoMunir, Mohsin, Shoaib Ahmed Siddiqui, Muhammad Ali Chattha, Andreas Dengel y Sheraz Ahmed. "FuseAD: Unsupervised Anomaly Detection in Streaming Sensors Data by Fusing Statistical and Deep Learning Models". Sensors 19, n.º 11 (29 de mayo de 2019): 2451. http://dx.doi.org/10.3390/s19112451.
Texto completoManzano Sanchez, Ricardo Alejandro, Marzia Zaman, Nishith Goel, Kshirasagar Naik y Rohit Joshi. "Towards Developing a Robust Intrusion Detection Model Using Hadoop–Spark and Data Augmentation for IoT Networks". Sensors 22, n.º 20 (12 de octubre de 2022): 7726. http://dx.doi.org/10.3390/s22207726.
Texto completoElia, Domenico, Gioacchino Vino, Giacinto Donvito y Marica Antonacci. "Developing a monitoring system for Cloud-based distributed data-centers". EPJ Web of Conferences 214 (2019): 08012. http://dx.doi.org/10.1051/epjconf/201921408012.
Texto completoImran, Faisal Jamil y Dohyeun Kim. "An Ensemble of a Prediction and Learning Mechanism for Improving Accuracy of Anomaly Detection in Network Intrusion Environments". Sustainability 13, n.º 18 (8 de septiembre de 2021): 10057. http://dx.doi.org/10.3390/su131810057.
Texto completoMitiche, Imene, Tony McGrail, Philip Boreham, Alan Nesbitt y Gordon Morison. "Data-Driven Anomaly Detection in High-Voltage Transformer Bushings with LSTM Auto-Encoder". Sensors 21, n.º 21 (8 de noviembre de 2021): 7426. http://dx.doi.org/10.3390/s21217426.
Texto completoRashid, A. N. M. Bazlur, Mohiuddin Ahmed y Al-Sakib Khan Pathan. "Infrequent Pattern Detection for Reliable Network Traffic Analysis Using Robust Evolutionary Computation". Sensors 21, n.º 9 (25 de abril de 2021): 3005. http://dx.doi.org/10.3390/s21093005.
Texto completoMinea, Marius, Cătălin Marian Dumitrescu y Viviana Laetitia Minea. "Intelligent Network Applications Monitoring and Diagnosis Employing Software Sensing and Machine Learning Solutions". Sensors 21, n.º 15 (25 de julio de 2021): 5036. http://dx.doi.org/10.3390/s21155036.
Texto completoEketnova, Yu M. "Comparative Analysis of Machine learning Methods to Identify signs of suspicious Transactions of Credit Institutions and Their Clients". Finance: Theory and Practice 25, n.º 5 (28 de octubre de 2021): 186–99. http://dx.doi.org/10.26794/2587-5671-2020-25-5-186-199.
Texto completoAlzahrani, Abdulsalam O. y Mohammed J. F. Alenazi. "Designing a Network Intrusion Detection System Based on Machine Learning for Software Defined Networks". Future Internet 13, n.º 5 (28 de abril de 2021): 111. http://dx.doi.org/10.3390/fi13050111.
Texto completoLu, Jiazhong, Weina Niu, Xiaolei Liu, Teng Hu y Xiaosong Zhang. "A Lockable Abnormal Electromagnetic Signal Joint Detection Algorithm". International Journal of Pattern Recognition and Artificial Intelligence 33, n.º 13 (15 de diciembre de 2019): 1958009. http://dx.doi.org/10.1142/s0218001419580096.
Texto completoMeng, Lei. "Internet of Things Information Network Security Situational Awareness Based on Machine Learning Algorithms". Mobile Information Systems 2022 (21 de julio de 2022): 1–7. http://dx.doi.org/10.1155/2022/4146042.
Texto completoLi, Han, Xinyu Wang, Zhongguo Yang, Sikandar Ali, Ning Tong y Samad Baseer. "Correlation-Based Anomaly Detection Method for Multi-sensor System". Computational Intelligence and Neuroscience 2022 (31 de mayo de 2022): 1–13. http://dx.doi.org/10.1155/2022/4756480.
Texto completoWong, Simon, John-Kun-Woon Yeung, Yui-Yip Lau y Joseph So. "Technical Sustainability of Cloud-Based Blockchain Integrated with Machine Learning for Supply Chain Management". Sustainability 13, n.º 15 (23 de julio de 2021): 8270. http://dx.doi.org/10.3390/su13158270.
Texto completoShoukat, Aimen, Muhammad Abul Hassan, Muhammad Rizwan, Muhammad Imad, Farhatullah, Syed Haider Ali y Sana Ullah. "Design a framework for IoT- Identification, Authentication and Anomaly detection using Deep Learning: A Review". EAI Endorsed Transactions on Smart Cities 7, n.º 1 (17 de enero de 2023): e1. http://dx.doi.org/10.4108/eetsc.v7i1.2067.
Texto completoMiller, Andrew, Jan Petrich y Shashi Phoha. "Advanced Image Analysis for Learning Underlying Partial Differential Equations for Anomaly Identification". Journal of Imaging Science and Technology 64, n.º 2 (1 de marzo de 2020): 20510–1. http://dx.doi.org/10.2352/j.imagingsci.technol.2020.64.2.020510.
Texto completoKhan, Muhammad Ashfaq y Juntae Kim. "Toward Developing Efficient Conv-AE-Based Intrusion Detection System Using Heterogeneous Dataset". Electronics 9, n.º 11 (26 de octubre de 2020): 1771. http://dx.doi.org/10.3390/electronics9111771.
Texto completoAnwar, Raja Waseem, Kashif Naseer Qureshi, Wamda Nagmeldin, Abdelzahir Abdelmaboud, Kayhan Zrar Ghafoor, Ibrahim Tariq Javed y Noel Crespi. "Data Analytics, Self-Organization, and Security Provisioning for Smart Monitoring Systems". Sensors 22, n.º 19 (22 de septiembre de 2022): 7201. http://dx.doi.org/10.3390/s22197201.
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