Literatura académica sobre el tema "Machine learning, big data, anomaly detection, network monitoring"
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Artículos de revistas sobre el tema "Machine learning, big data, anomaly detection, network monitoring"
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 completoTesis sobre el tema "Machine learning, big data, anomaly detection, network monitoring"
Syal, Astha. "Automatic Network Traffic Anomaly Detection and Analysis using SupervisedMachine Learning Techniques". Youngstown State University / OhioLINK, 2019. http://rave.ohiolink.edu/etdc/view?acc_num=ysu1578259840945109.
Texto completoJehangiri, Ali Imran. "Distributed Anomaly Detection and Prevention for Virtual Platforms". Doctoral thesis, 2015. http://hdl.handle.net/11858/00-1735-0000-0022-605F-2.
Texto completo(10723926), Adefolarin Alaba Bolaji. "Community Detection of Anomaly in Large-Scale Network Dissertation - Adefolarin Bolaji .pdf". Thesis, 2021.
Buscar texto completoThe detection of anomalies in real-world networks is applicable in different domains; the application includes, but is not limited to, credit card fraud detection, malware identification and classification, cancer detection from diagnostic reports, abnormal traffic detection, identification of fake media posts, and the like. Many ongoing and current researches are providing tools for analyzing labeled and unlabeled data; however, the challenges of finding anomalies and patterns in large-scale datasets still exist because of rapid changes in the threat landscape.
In this study, I implemented a novel and robust solution that combines data science and cybersecurity to solve complex network security problems. I used Long Short-Term Memory (LSTM) model, Louvain algorithm, and PageRank algorithm to identify and group anomalies in large-scale real-world networks. The network has billions of packets. The developed model used different visualization techniques to provide further insight into how the anomalies in the network are related.
Mean absolute error (MAE) and root mean square error (RMSE) was used to validate the anomaly detection models, the results obtained for both are 5.1813e-04 and 1e-03 respectively. The low loss from the training phase confirmed the low RMSE at loss: 5.1812e-04, mean absolute error: 5.1813e-04, validation loss: 3.9858e-04, validation mean absolute error: 3.9858e-04. The result from the community detection shows an overall modularity value of 0.914 which is proof of the existence of very strong communities among the anomalies. The largest sub-community of the anomalies connects 10.42% of the total nodes of the anomalies.
The broader aim and impact of this study was to provide sophisticated, AI-assisted countermeasures to cyber-threats in large-scale networks. To close the existing gaps created by the shortage of skilled and experienced cybersecurity specialists and analysts in the cybersecurity field, solutions based on out-of-the-box thinking are inevitable; this research was aimed at yielding one of such solutions. It was built to detect specific and collaborating threat actors in large networks and to help speed up how the activities of anomalies in any given large-scale network can be curtailed in time.
Capítulos de libros sobre el tema "Machine learning, big data, anomaly detection, network monitoring"
Kumar, Shailender, Namrata Jha y Nikhil Sachdeva. "A Deep Learning Approach for Anomaly-Based Network Intrusion Detection Systems: A Survey and an Objective Comparison". En Machine Learning and Big Data Analytics (Proceedings of International Conference on Machine Learning and Big Data Analytics (ICMLBDA) 2021), 227–35. Cham: Springer International Publishing, 2021. http://dx.doi.org/10.1007/978-3-030-82469-3_20.
Texto completoNarayan, Valliammal y Shanmugapriya D. "Big Data Analytics With Machine Learning and Deep Learning Methods for Detection of Anomalies in Network Traffic". En Research Anthology on Big Data Analytics, Architectures, and Applications, 678–707. IGI Global, 2022. http://dx.doi.org/10.4018/978-1-6684-3662-2.ch032.
Texto completoNarayan, Valliammal y Shanmugapriya D. "Big Data Analytics With Machine Learning and Deep Learning Methods for Detection of Anomalies in Network Traffic". En Handbook of Research on Machine and Deep Learning Applications for Cyber Security, 317–46. IGI Global, 2020. http://dx.doi.org/10.4018/978-1-5225-9611-0.ch015.
Texto completoVidal, Jorge Maestre, Marco Antonio Sotelo Monge y Sergio Mauricio Martínez Monterrubio. "Anomaly-Based Intrusion Detection". En Handbook of Research on Machine and Deep Learning Applications for Cyber Security, 195–218. IGI Global, 2020. http://dx.doi.org/10.4018/978-1-5225-9611-0.ch010.
Texto completoZhao, Peng, Yuan Ren y Xi Chen. "Big Data Helps for Non-Pharmacological Disease Control Measures of COVID-19". En Encyclopedia of Data Science and Machine Learning, 143–55. IGI Global, 2022. http://dx.doi.org/10.4018/978-1-7998-9220-5.ch009.
Texto completoActas de conferencias sobre el tema "Machine learning, big data, anomaly detection, network monitoring"
Nivlet, Philippe, Knut Steinar Bjorkevoll, Mandar Tabib, Jan Ole Skogestad, Bjornar Lund, Roar Nybo y Adil Rasheed. "Towards Real-Time Bad Hole Cleaning Problem Detection Through Adaptive Deep Learning Models". En Middle East Oil, Gas and Geosciences Show. SPE, 2023. http://dx.doi.org/10.2118/213643-ms.
Texto completoLiu, Zhipeng, Niraj Thapa, Addison Shaver, Kaushik Roy, Xiaohong Yuan y Sajad Khorsandroo. "Anomaly Detection on IoT Network Intrusion Using Machine Learning". En 2020 International Conference on Artificial Intelligence, Big Data, Computing and Data Communication Systems (icABCD). IEEE, 2020. http://dx.doi.org/10.1109/icabcd49160.2020.9183842.
Texto completoWang, Biao, Guoqing Han, Xin Lu, Shuai Tan, Zhiyong Zhu y Huizhu Xiang. "Remote Monitoring of Well Production Performance Based on Machine Learning". En SPE Western Regional Meeting. SPE, 2022. http://dx.doi.org/10.2118/209255-ms.
Texto completoCadei, Luca, Gianmarco Rossi, Lorenzo Lancia, Danilo Loffreno, Andrea Corneo, Diletta Milana, Marco Montini et al. "Hazardous Events Prevention and Management Through an Integrated Machine Learning and Big Data Analytics Framework". En SPE Conference at Oman Petroleum & Energy Show. SPE, 2022. http://dx.doi.org/10.2118/200110-ms.
Texto completoPeng, Dandan, Chenyu Liu, Wim Desmet y Konstantinos Gryllias. "Condition Monitoring of Wind Turbines Based on Anomaly Detection Using Deep Support Vector Data Description". En ASME Turbo Expo 2022: Turbomachinery Technical Conference and Exposition. American Society of Mechanical Engineers, 2022. http://dx.doi.org/10.1115/gt2022-82624.
Texto completoAhmed, Muhammad Shahzad, Mahdi Abdula Al Bloushi y Asad Ali. "Case Study: Application of Wireless Condition Based Monitoring by Applying Machine Learning Models". En ADIPEC. SPE, 2022. http://dx.doi.org/10.2118/211258-ms.
Texto completoAndreoni Lopez, Martin E., Otto Carlos Muniz Bandeira Duarte y Guy Pujolle. "A Monitoring and Threat Detection System Using Stream Processing as a Virtual Function for Big Data". En XXXVII Simpósio Brasileiro de Redes de Computadores e Sistemas Distribuídos. Sociedade Brasileira de Computação - SBC, 2019. http://dx.doi.org/10.5753/sbrc_estendido.2019.7789.
Texto completoHarputlu Aksu, Şeniz y Erman Çakıt. "Classifying mental workload using EEG data: A machine learning approach". En 13th International Conference on Applied Human Factors and Ergonomics (AHFE 2022). AHFE International, 2022. http://dx.doi.org/10.54941/ahfe1001820.
Texto completoBuiu, Catalin y Vladrares Danaila. "DATA SCIENCE AND MACHINE LEARNING TECHNIQUES FOR CASE-BASED LEARNING IN MEDICAL BIOENGINEERING EDUCATION". En eLSE 2020. University Publishing House, 2020. http://dx.doi.org/10.12753/2066-026x-20-194.
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