Littérature scientifique sur le sujet « Machine learning, big data, anomaly detection, network monitoring »
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Articles de revues sur le sujet "Machine learning, big data, anomaly detection, network monitoring"
Oprea, Simona-Vasilica, Adela Bâra, Florina Camelia Puican et Ioan Cosmin Radu. « Anomaly Detection with Machine Learning Algorithms and Big Data in Electricity Consumption ». Sustainability 13, no 19 (2 octobre 2021) : 10963. http://dx.doi.org/10.3390/su131910963.
Texte intégralAlnafessah, Ahmad, et Giuliano Casale. « Artificial neural networks based techniques for anomaly detection in Apache Spark ». Cluster Computing 23, no 2 (23 octobre 2019) : 1345–60. http://dx.doi.org/10.1007/s10586-019-02998-y.
Texte intégralBorghesi, Andrea, Andrea Bartolini, Michele Lombardi, Michela Milano et Luca Benini. « Anomaly Detection Using Autoencoders in High Performance Computing Systems ». Proceedings of the AAAI Conference on Artificial Intelligence 33 (17 juillet 2019) : 9428–33. http://dx.doi.org/10.1609/aaai.v33i01.33019428.
Texte intégralAlbattah, Albatul, et 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 (2 mars 2022) : 1951. http://dx.doi.org/10.3390/s22051951.
Texte intégralChen, Naiyue, Yi Jin, Yinglong Li et Luxin Cai. « Trust-based federated learning for network anomaly detection ». Web Intelligence 19, no 4 (20 janvier 2022) : 317–27. http://dx.doi.org/10.3233/web-210475.
Texte intégralDo, ChoXuan, Nguyen Quang Dam et Nguyen Tung Lam. « Optimization of network traffic anomaly detection using machine learning ». International Journal of Electrical and Computer Engineering (IJECE) 11, no 3 (1 juin 2021) : 2360. http://dx.doi.org/10.11591/ijece.v11i3.pp2360-2370.
Texte intégralVajda, Daniel, Adrian Pekar et 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.
Texte intégralNovoa-Paradela, David, Óscar Fontenla-Romero et Bertha Guijarro-Berdiñas. « Adaptive Real-Time Method for Anomaly Detection Using Machine Learning ». Proceedings 54, no 1 (22 août 2020) : 38. http://dx.doi.org/10.3390/proceedings2020054038.
Texte intégralChimphlee, Siriporn, et 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 (1 mai 2023) : 1106. http://dx.doi.org/10.11591/ijeecs.v30.i2.pp1106-1119.
Texte intégralKáš, M., et F. F. Wamba. « Anomaly detection-based condition monitoring ». Insight - Non-Destructive Testing and Condition Monitoring 64, no 8 (1 août 2022) : 453–58. http://dx.doi.org/10.1784/insi.2022.64.8.453.
Texte intégralThèses sur le sujet "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.
Texte intégralJehangiri, Ali Imran. « Distributed Anomaly Detection and Prevention for Virtual Platforms ». Doctoral thesis, 2015. http://hdl.handle.net/11858/00-1735-0000-0022-605F-2.
Texte intégral(10723926), Adefolarin Alaba Bolaji. « Community Detection of Anomaly in Large-Scale Network Dissertation - Adefolarin Bolaji .pdf ». Thesis, 2021.
Trouver le texte intégralThe 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.
Chapitres de livres sur le sujet "Machine learning, big data, anomaly detection, network monitoring"
Kumar, Shailender, Namrata Jha et Nikhil Sachdeva. « A Deep Learning Approach for Anomaly-Based Network Intrusion Detection Systems : A Survey and an Objective Comparison ». Dans 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.
Texte intégralNarayan, Valliammal, et Shanmugapriya D. « Big Data Analytics With Machine Learning and Deep Learning Methods for Detection of Anomalies in Network Traffic ». Dans 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.
Texte intégralNarayan, Valliammal, et Shanmugapriya D. « Big Data Analytics With Machine Learning and Deep Learning Methods for Detection of Anomalies in Network Traffic ». Dans 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.
Texte intégralVidal, Jorge Maestre, Marco Antonio Sotelo Monge et Sergio Mauricio Martínez Monterrubio. « Anomaly-Based Intrusion Detection ». Dans 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.
Texte intégralZhao, Peng, Yuan Ren et Xi Chen. « Big Data Helps for Non-Pharmacological Disease Control Measures of COVID-19 ». Dans Encyclopedia of Data Science and Machine Learning, 143–55. IGI Global, 2022. http://dx.doi.org/10.4018/978-1-7998-9220-5.ch009.
Texte intégralActes de conférences sur le sujet "Machine learning, big data, anomaly detection, network monitoring"
Nivlet, Philippe, Knut Steinar Bjorkevoll, Mandar Tabib, Jan Ole Skogestad, Bjornar Lund, Roar Nybo et Adil Rasheed. « Towards Real-Time Bad Hole Cleaning Problem Detection Through Adaptive Deep Learning Models ». Dans Middle East Oil, Gas and Geosciences Show. SPE, 2023. http://dx.doi.org/10.2118/213643-ms.
Texte intégralLiu, Zhipeng, Niraj Thapa, Addison Shaver, Kaushik Roy, Xiaohong Yuan et Sajad Khorsandroo. « Anomaly Detection on IoT Network Intrusion Using Machine Learning ». Dans 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.
Texte intégralWang, Biao, Guoqing Han, Xin Lu, Shuai Tan, Zhiyong Zhu et Huizhu Xiang. « Remote Monitoring of Well Production Performance Based on Machine Learning ». Dans SPE Western Regional Meeting. SPE, 2022. http://dx.doi.org/10.2118/209255-ms.
Texte intégralCadei, 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 ». Dans SPE Conference at Oman Petroleum & Energy Show. SPE, 2022. http://dx.doi.org/10.2118/200110-ms.
Texte intégralPeng, Dandan, Chenyu Liu, Wim Desmet et Konstantinos Gryllias. « Condition Monitoring of Wind Turbines Based on Anomaly Detection Using Deep Support Vector Data Description ». Dans ASME Turbo Expo 2022 : Turbomachinery Technical Conference and Exposition. American Society of Mechanical Engineers, 2022. http://dx.doi.org/10.1115/gt2022-82624.
Texte intégralAhmed, Muhammad Shahzad, Mahdi Abdula Al Bloushi et Asad Ali. « Case Study : Application of Wireless Condition Based Monitoring by Applying Machine Learning Models ». Dans ADIPEC. SPE, 2022. http://dx.doi.org/10.2118/211258-ms.
Texte intégralAndreoni Lopez, Martin E., Otto Carlos Muniz Bandeira Duarte et Guy Pujolle. « A Monitoring and Threat Detection System Using Stream Processing as a Virtual Function for Big Data ». Dans 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.
Texte intégralHarputlu Aksu, Şeniz, et Erman Çakıt. « Classifying mental workload using EEG data : A machine learning approach ». Dans 13th International Conference on Applied Human Factors and Ergonomics (AHFE 2022). AHFE International, 2022. http://dx.doi.org/10.54941/ahfe1001820.
Texte intégralBuiu, Catalin, et Vladrares Danaila. « DATA SCIENCE AND MACHINE LEARNING TECHNIQUES FOR CASE-BASED LEARNING IN MEDICAL BIOENGINEERING EDUCATION ». Dans eLSE 2020. University Publishing House, 2020. http://dx.doi.org/10.12753/2066-026x-20-194.
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