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