Artículos de revistas sobre el tema "Networks anomalies detection"
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Mažeika, Dalius y Saulius Jasonis. "NETWORK TRAFFIC ANOMALIES DETECTING USING MAXIMUM ENTROPY METHOD / KOMPIUTERIŲ TINKLO SRAUTO ANOMALIJŲ ATPAŽINIMAS MAKSIMALIOS ENTROPIJOS METODU". Mokslas – Lietuvos ateitis 6, n.º 2 (24 de abril de 2014): 162–67. http://dx.doi.org/10.3846/mla.2014.22.
Texto completoRačys, Donatas y Dalius Mažeika. "NETWORK TRAFFIC ANOMALIES IDENTIFICATION BASED ON CLASSIFICATION METHODS / TINKLO SRAUTO ANOMALIJŲ IDENTIFIKAVIMAS, TAIKANT KLASIFIKAVIMO METODUS". Mokslas – Lietuvos ateitis 7, n.º 3 (13 de julio de 2015): 340–44. http://dx.doi.org/10.3846/mla.2015.796.
Texto completoRejito, Juli, Deris Stiawan, Ahmed Alshaflut y Rahmat Budiarto. "Machine learning-based anomaly detection for smart home networks under adversarial attack". Computer Science and Information Technologies 5, n.º 2 (1 de julio de 2024): 122–29. http://dx.doi.org/10.11591/csit.v5i2.p122-129.
Texto completoRejito, Juli, Deris Stiawan, Ahmed Alshaflut y Rahmat Budiarto. "Machine learning-based anomaly detection for smart home networks under adversarial attack". Computer Science and Information Technologies 5, n.º 2 (1 de julio de 2024): 122–29. http://dx.doi.org/10.11591/csit.v5i2.pp122-129.
Texto completoLiao, Xiao Ju, Yi Wang y Hai Lu. "Rule Anomalies Detection in Firewalls". Key Engineering Materials 474-476 (abril de 2011): 822–27. http://dx.doi.org/10.4028/www.scientific.net/kem.474-476.822.
Texto completoGutiérrez-Gómez, Leonardo, Alexandre Bovet y Jean-Charles Delvenne. "Multi-Scale Anomaly Detection on Attributed Networks". Proceedings of the AAAI Conference on Artificial Intelligence 34, n.º 01 (3 de abril de 2020): 678–85. http://dx.doi.org/10.1609/aaai.v34i01.5409.
Texto completoRana, Samir. "Anomaly Detection in Network Traffic using Machine Learning and Deep Learning Techniques". Turkish Journal of Computer and Mathematics Education (TURCOMAT) 10, n.º 2 (10 de septiembre de 2019): 1063–67. http://dx.doi.org/10.17762/turcomat.v10i2.13626.
Texto completoJiang, Ding De, Cheng Yao, Zheng Zheng Xu, Peng Zhang, Zhen Yuan y Wen Da Qin. "An Continuous Wavelet Transform-Based Detection Approach to Traffic Anomalies". Applied Mechanics and Materials 130-134 (octubre de 2011): 2098–102. http://dx.doi.org/10.4028/www.scientific.net/amm.130-134.2098.
Texto completoA, Nandini. "Anomaly Detection Using CNN with I3D Feature Extraction". INTERANTIONAL JOURNAL OF SCIENTIFIC RESEARCH IN ENGINEERING AND MANAGEMENT 08, n.º 03 (18 de marzo de 2024): 1–5. http://dx.doi.org/10.55041/ijsrem29371.
Texto completoBadr, Malek, Shaha Al-Otaibi, Nazik Alturki y Tanvir Abir. "Deep Learning-Based Networks for Detecting Anomalies in Chest X-Rays". BioMed Research International 2022 (23 de julio de 2022): 1–10. http://dx.doi.org/10.1155/2022/7833516.
Texto completoSozol, Md Shariar, Golam Mostafa Saki y Md Mostafizur Rahman. "Anomaly Detection in Cybersecurity with Graph-Based Approaches". INTERANTIONAL JOURNAL OF SCIENTIFIC RESEARCH IN ENGINEERING AND MANAGEMENT 08, n.º 008 (13 de agosto de 2024): 1–5. http://dx.doi.org/10.55041/ijsrem37061.
Texto completoDehbozorgi, Leila, Reza Akbari-Hasanjani y Reza Sabbaghi-Nadooshan. "Chaotic seismic signal modeling based on noise and earthquake anomaly detection". Facta universitatis - series: Electronics and Energetics 35, n.º 4 (2022): 603–17. http://dx.doi.org/10.2298/fuee2204603d.
Texto completoKotenko, Igor, Igor Saenko, Oleg Lauta y Alexander Kriebel. "Anomaly and Cyber Attack Detection Technique Based on the Integration of Fractal Analysis and Machine Learning Methods". Informatics and Automation 21, n.º 6 (24 de noviembre de 2022): 1328–58. http://dx.doi.org/10.15622/ia.21.6.9.
Texto completoPEROV, ROMAN A., OLEG S. LAUTA, ALEXANDER M. KRIBEL y YURI V. FEDULOV. "A METHOD FOR DETECTING ANOMALIES IN NETWORK TRAFFIC". H&ES Research 14, n.º 3 (2022): 25–31. http://dx.doi.org/10.36724/2409-5419-2022-14-3-25-31.
Texto completoBarrionuevo, Mercedes, Mariela Lopresti, Natalia Miranda y Fabiana Piccoli. "Secure Computer Network: Strategies and Challengers in Big Data Era". Journal of Computer Science and Technology 18, n.º 03 (12 de diciembre de 2018): e28. http://dx.doi.org/10.24215/16666038.18.e28.
Texto completoYallamanda Rajesh Babu, Et al. "Subgraph Anomaly Detection in Social Networks using Clustering-Based Deep Autoencoders". International Journal on Recent and Innovation Trends in Computing and Communication 11, n.º 9 (5 de noviembre de 2023): 1646–55. http://dx.doi.org/10.17762/ijritcc.v11i9.9150.
Texto completoRizwan, Ramsha, Farrukh Aslam Khan, Haider Abbas y Sajjad Hussain Chauhdary. "Anomaly Detection in Wireless Sensor Networks Using Immune-Based Bioinspired Mechanism". International Journal of Distributed Sensor Networks 2015 (2015): 1–10. http://dx.doi.org/10.1155/2015/684952.
Texto completoBurgueño, Jesús, Isabel de-la-Bandera, Jessica Mendoza, David Palacios, Cesar Morillas y Raquel Barco. "Online Anomaly Detection System for Mobile Networks". Sensors 20, n.º 24 (17 de diciembre de 2020): 7232. http://dx.doi.org/10.3390/s20247232.
Texto completoMa, Shu Hua, Jin Kuan Wang, Zhi Gang Liu y Hou Yan Jiang. "Density-Based Distributed Elliptical Anomaly Detection in Wireless Sensor Networks". Applied Mechanics and Materials 249-250 (diciembre de 2012): 226–30. http://dx.doi.org/10.4028/www.scientific.net/amm.249-250.226.
Texto completoLegashev, Leonid, Irina Bolodurina, Lubov Zabrodina, Yuri Ushakov, Alexander Shukhman, Denis Parfenov, Yong Zhou y Yan Xu. "Message Authentication and Network Anomalies Detection in Vehicular Ad Hoc Networks". Security and Communication Networks 2022 (24 de febrero de 2022): 1–18. http://dx.doi.org/10.1155/2022/9440886.
Texto completoMillán-Roures, Laura, Irene Epifanio y Vicente Martínez. "Detection of Anomalies in Water Networks by Functional Data Analysis". Mathematical Problems in Engineering 2018 (21 de junio de 2018): 1–13. http://dx.doi.org/10.1155/2018/5129735.
Texto completoBattini Sujatha, Et al. "An Efficient Fuzzy Based Multi Level Clustering Model Using Artificial Bee Colony For Intrusion Detection". International Journal on Recent and Innovation Trends in Computing and Communication 11, n.º 11 (30 de noviembre de 2023): 264–73. http://dx.doi.org/10.17762/ijritcc.v11i11.9390.
Texto completoAlfardus, Asma y Danda B. Rawat. "Machine Learning-Based Anomaly Detection for Securing In-Vehicle Networks". Electronics 13, n.º 10 (16 de mayo de 2024): 1962. http://dx.doi.org/10.3390/electronics13101962.
Texto completoŽarković, Mileta y Goran Dobrić. "Artificial Intelligence for Energy Theft Detection in Distribution Networks". Energies 17, n.º 7 (26 de marzo de 2024): 1580. http://dx.doi.org/10.3390/en17071580.
Texto completoRovatsos, Georgios, George V. Moustakides y Venugopal V. Veeravalli. "Quickest Detection of Moving Anomalies in Sensor Networks". IEEE Journal on Selected Areas in Information Theory 2, n.º 2 (junio de 2021): 762–73. http://dx.doi.org/10.1109/jsait.2021.3076043.
Texto completoTian, Hui, Jingtian Liu y Meimei Ding. "Promising techniques for anomaly detection on network traffic". Computer Science and Information Systems 14, n.º 3 (2017): 597–609. http://dx.doi.org/10.2298/csis170201018h.
Texto completoGarcía González, Gastón, Pedro Casas, Alicia Fernández y Gabriel Gómez. "On the Usage of Generative Models for Network Anomaly Detection in Multivariate Time-Series". ACM SIGMETRICS Performance Evaluation Review 48, n.º 4 (17 de mayo de 2021): 49–52. http://dx.doi.org/10.1145/3466826.3466843.
Texto completoYan Lei. "Smart Network Forensics with Generative Adversarial Networks Leveraging Blockchain for Anomaly Detection and Immutable Audit Trails". Power System Technology 48, n.º 1 (28 de mayo de 2024): 1625–42. http://dx.doi.org/10.52783/pst.432.
Texto completoKuang, Ye, Dandan Li, Xiaohong Huang y Mo Zhou. "On the Modeling of RTT Time Series for Network Anomaly Detection". Security and Communication Networks 2022 (6 de mayo de 2022): 1–13. http://dx.doi.org/10.1155/2022/5499080.
Texto completoHajirahimova, Makrufa y Leyla Yusifova. "Experimental Study of Machine Learning Methods in Anomaly Detection". Problems of Information Technology 13, n.º 1 (24 de enero de 2022): 9–19. http://dx.doi.org/10.25045/jpit.v13.i1.02.
Texto completoZehra, Sehar, Ummay Faseeha, Hassan Jamil Syed, Fahad Samad, Ashraf Osman Ibrahim, Anas W. Abulfaraj y Wamda Nagmeldin. "Machine Learning-Based Anomaly Detection in NFV: A Comprehensive Survey". Sensors 23, n.º 11 (5 de junio de 2023): 5340. http://dx.doi.org/10.3390/s23115340.
Texto completoRadivilova, Tamara, Lyudmyla Kirichenko, Maksym Tawalbeh y Andrii Ilkov. "DETECTION OF ANOMALIES IN THE TELECOMMUNICATIONS TRAFFIC BY STATISTICAL METHODS". Cybersecurity: Education, Science, Technique 11, n.º 3 (2021): 183–94. http://dx.doi.org/10.28925/2663-4023.2021.11.183194.
Texto completoSousa, Inês Sousa, António Casimiro y José Cecílio. "Artificial Neural Networks for Real-Time Data Quality Assurance". ACM SIGAda Ada Letters 42, n.º 1 (15 de diciembre de 2022): 86–89. http://dx.doi.org/10.1145/3577949.3577966.
Texto completoKomadina, Adrian, Ivan Kovačević, Bruno Štengl y Stjepan Groš. "Comparative Analysis of Anomaly Detection Approaches in Firewall Logs: Integrating Light-Weight Synthesis of Security Logs and Artificially Generated Attack Detection". Sensors 24, n.º 8 (20 de abril de 2024): 2636. http://dx.doi.org/10.3390/s24082636.
Texto completoRajaboevich, Gulomov Sherzod y Ganiev Abdukhalil Abdujalilovich. "Methods and models of protecting computer networks from un-wanted network traffic". International Journal of Engineering & Technology 7, n.º 4 (24 de septiembre de 2018): 2541. http://dx.doi.org/10.14419/ijet.v7i4.14744.
Texto completoDymora, Paweł y Mirosław Mazurek. "Anomaly Detection in IoT Communication Network Based on Spectral Analysis and Hurst Exponent". Applied Sciences 9, n.º 24 (6 de diciembre de 2019): 5319. http://dx.doi.org/10.3390/app9245319.
Texto completoMandrikova, O. V. "Intelligent methods for natural data analysis: application to space weather". Computer Optics 48, n.º 1 (febrero de 2024): 139–48. http://dx.doi.org/10.18287/2412-6179-co-1367.
Texto completoHabeeb, Mohammed Sayeeduddin y Tummala Ranga Babu. "MS-CFFS: Multistage Coarse and Fine Feature Selecton for Advanced Anomaly Detection in IoT Security Networks". International Journal of Electrical and Electronics Research 12, n.º 3 (25 de julio de 2024): 780–90. http://dx.doi.org/10.37391/ijeer.120308.
Texto completoLópez-Vizcaíno, Manuel, Carlos Dafonte, Francisco Nóvoa, Daniel Garabato y M. Álvarez. "Network Data Unsupervised Clustering to Anomaly Detection". Proceedings 2, n.º 18 (17 de septiembre de 2018): 1173. http://dx.doi.org/10.3390/proceedings2181173.
Texto completoMeneganti, M., F. S. Saviello y R. Tagliaferri. "Fuzzy neural networks for classification and detection of anomalies". IEEE Transactions on Neural Networks 9, n.º 5 (1998): 848–61. http://dx.doi.org/10.1109/72.712157.
Texto completoP, Bharathisindhu y Dr S.SelvaBrunda. "Probability Model for Intrusion Detection System in Mobile Adhoc Network". International Journal of Engineering & Technology 7, n.º 2.20 (18 de abril de 2018): 302. http://dx.doi.org/10.14419/ijet.v7i2.20.16722.
Texto completo.., Pallavi y Sarika Chaudhary. "Maximizing Anomaly Detection Performance in Next-Generation Networks". Journal of Cybersecurity and Information Management 12, n.º 2 (2023): 36–51. http://dx.doi.org/10.54216/jcim.120203.
Texto completoSun, Yumeng. "Unsupervised Wireless Network Model-Assisted Abnormal Warning Information in Government Management". Journal of Sensors 2021 (26 de octubre de 2021): 1–12. http://dx.doi.org/10.1155/2021/1614055.
Texto completoClausen, Henry, Gudmund Grov y David Aspinall. "CBAM: A Contextual Model for Network Anomaly Detection". Computers 10, n.º 6 (11 de junio de 2021): 79. http://dx.doi.org/10.3390/computers10060079.
Texto completoYu, Xiang, Hui Lu, Xianfei Yang, Ying Chen, Haifeng Song, Jianhua Li y Wei Shi. "An adaptive method based on contextual anomaly detection in Internet of Things through wireless sensor networks". International Journal of Distributed Sensor Networks 16, n.º 5 (mayo de 2020): 155014772092047. http://dx.doi.org/10.1177/1550147720920478.
Texto completoMeleshko, Alexey, Anton Shulepov, Vasily Desnitsky y Evgenia Novikova. "Integrated approach to revelation of anomalies in wireless sensor networks for water control cases". Computer Tools in Education, n.º 1 (28 de marzo de 2021): 58–67. http://dx.doi.org/10.32603/2071-2340-2021-1-59-68.
Texto completoKhilar, Rashmita, K. Mariyappan, Mary Subaja Christo, J. Amutharaj, T. Anitha, T. Rajendran y Areda Batu. "Artificial Intelligence-Based Security Protocols to Resist Attacks in Internet of Things". Wireless Communications and Mobile Computing 2022 (5 de abril de 2022): 1–10. http://dx.doi.org/10.1155/2022/1440538.
Texto completoDymora, Paweł y Mirosław Mazurek. "An Innovative Approach to Anomaly Detection in Communication Networks Using Multifractal Analysis". Applied Sciences 10, n.º 9 (8 de mayo de 2020): 3277. http://dx.doi.org/10.3390/app10093277.
Texto completoPatel, Darsh, Kathiravan Srinivasan, Chuan-Yu Chang, Takshi Gupta y Aman Kataria. "Network Anomaly Detection inside Consumer Networks—A Hybrid Approach". Electronics 9, n.º 6 (1 de junio de 2020): 923. http://dx.doi.org/10.3390/electronics9060923.
Texto completoImtiaz, Syed Ibrahim, Liaqat Ali Khan, Ahmad S. Almadhor, Sidra Abbas, Shtwai Alsubai, Michal Gregus y Zunera Jalil. "Efficient Approach for Anomaly Detection in Internet of Things Traffic Using Deep Learning". Wireless Communications and Mobile Computing 2022 (10 de septiembre de 2022): 1–15. http://dx.doi.org/10.1155/2022/8266347.
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