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Feature identification and processing strategies of machine learning techniques in big data traffic analysis

  
24 set 2025
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Liu, J., Liu, F., & Ansari, N. (2014). Monitoring and analyzing big traffic data of a large-scale cellular network with Hadoop. IEEE network, 28(4), 32-39. LiuJ. LiuF. & Ansari N. (2014). Monitoring and analyzing big traffic data of a large-scale cellular network with Hadoop. IEEE network, 28(4), 32-39.Search in Google Scholar

Parwez, M. S., Rawat, D. B., & Garuba, M. (2017). Big data analytics for user-activity analysis and user-anomaly detection in mobile wireless network. IEEE Transactions on Industrial Informatics, 13(4), 2058-2065. ParwezM. S. RawatD. B. & Garuba M. (2017). Big data analytics for user-activity analysis and user-anomaly detection in mobile wireless network. IEEE Transactions on Industrial Informatics, 13(4), 2058-2065.Search in Google Scholar

Bachechi, C., Po, L., & Rollo, F. (2022). Big data analytics and visualization in traffic monitoring. Big Data Research, 27, 100292. BachechiC. PoL. & Rollo F. (2022). Big data analytics and visualization in traffic monitoring. Big Data Research, 27, 100292.Search in Google Scholar

Meshram, A., & Haas, C. (2017). Anomaly detection in industrial networks using machine learning: a roadmap. In Machine learning for cyber physical systems: selected papers from the international conference ML4CPS 2016 (pp. 65-72). Springer Berlin Heidelberg. MeshramA. & Haas C. (2017). Anomaly detection in industrial networks using machine learning: a roadmap. In Machine learning for cyber physical systems: selected papers from the international conference ML4CPS 2016 (pp. 65-72). Springer Berlin Heidelberg.Search in Google Scholar

Zachos, G., Essop, I., Mantas, G., Porfyrakis, K., Ribeiro, J. C., & Rodriguez, J. (2021). An anomaly-based intrusion detection system for internet of medical things networks. Electronics, 10(21), 2562. ZachosG. EssopI. MantasG. PorfyrakisK. RibeiroJ. C. & Rodriguez J. (2021). An anomaly-based intrusion detection system for internet of medical things networks. Electronics, 10(21), 2562.Search in Google Scholar

Vela, A. P., Ruiz, M., & Velasco, L. (2017). Distributing data analytics for efficient multiple traffic anomalies detection. Computer Communications, 107, 1-12. VelaA. P. RuizM. & Velasco L. (2017). Distributing data analytics for efficient multiple traffic anomalies detection. Computer Communications, 107, 1-12Search in Google Scholar

Arjunan, T. (2024). Real-Time Detection of Network Traffic Anomalies in Big Data Environments Using Deep Learning Models. International Journal for Research in Applied Science and Engineering Technology, 12(9), 10-22214. Arjunan T. (2024). Real-Time Detection of Network Traffic Anomalies in Big Data Environments Using Deep Learning Models. International Journal for Research in Applied Science and Engineering Technology, 12(9), 10-22214.Search in Google Scholar

Ahmed, M. (2019). Intelligent big data summarization for rare anomaly detection. Ieee Access, 7, 68669-68677. Ahmed M. (2019). Intelligent big data summarization for rare anomaly detection. Ieee Access, 7, 68669-68677Search in Google Scholar

Mansour, R. F., Abdel-Khalek, S., Hilali-Jaghdam, I., Nebhen, J., Cho, W., & Joshi, G. P. (2023). An intelligent outlier detection with machine learning empowered big data analytics for mobile edge computing. Cluster Computing, 1-13. MansourR. F. Abdel-KhalekS. Hilali-JaghdamI. NebhenJ. ChoW. & Joshi G. P. (2023). An intelligent outlier detection with machine learning empowered big data analytics for mobile edge computing. Cluster Computing, 1-13.Search in Google Scholar

Wang, L., & Jones, R. (2021). Big data analytics in cyber security: network traffic and attacks. Journal of Computer Information Systems, 61(5), 410-417. WangL. & Jones R. (2021). Big data analytics in cyber security: network traffic and attacks. Journal of Computer Information Systems, 61(5), 410-417.Search in Google Scholar

Sultan, K., Ali, H., & Zhang, Z. (2018). Call detail records driven anomaly detection and traffic prediction in mobile cellular networks. IEEE Access, 6, 41728-41737. SultanK. AliH. & Zhang Z. (2018). Call detail records driven anomaly detection and traffic prediction in mobile cellular networks. IEEE Access, 6, 41728-41737Search in Google Scholar

Dromard, J., Roudiere, G., & Owezarski, P. (2015, August). Unsupervised network anomaly detection in real-time on big data. In East European conference on advances in databases and information systems (pp. 197-206). Cham: Springer International Publishing. DromardJ. RoudiereG. & OwezarskiP. (2015, August). Unsupervised network anomaly detection in real-time on big data. In East European conference on advances in databases and information systems (pp. 197-206). Cham: Springer International Publishing.Search in Google Scholar

Raiyn, J. (2022). Detection of road traffic anomalies based on computational data science. Discover Internet of things, 2(1), 6. Raiyn J. (2022). Detection of road traffic anomalies based on computational data science. Discover Internet of things, 2(1), 6.Search in Google Scholar

D’Alconzo, A., Drago, I., Morichetta, A., Mellia, M., & Casas, P. (2019). A survey on big data for network traffic monitoring and analysis. IEEE Transactions on Network and Service Management, 16(3), 800-813. D’AlconzoA. DragoI. MorichettaA. MelliaM. & Casas P. (2019). A survey on big data for network traffic monitoring and analysis. IEEE Transactions on Network and Service Management, 16(3), 800-813.Search in Google Scholar

Chen, L., Gao, S., & Cao, X. (2020). Research on real-time outlier detection over big data streams. International Journal of Computers and Applications, 42(1), 93-101. ChenL. GaoS. & Cao X. (2020). Research on real-time outlier detection over big data streams. International Journal of Computers and Applications, 42(1), 93-101.Search in Google Scholar

Narayan, V., & Shanmugapriya, D. (2022). Big data analytics with machine learning and deep learning methods for detection of anomalies in network traffic. In Research Anthology on Big Data Analytics, Architectures, and Applications (pp. 678-707). IGI Global. NarayanV. & Shanmugapriya D. (2022). Big data analytics with machine learning and deep learning methods for detection of anomalies in network traffic. In Research Anthology on Big Data Analytics, Architectures, and Applications (pp. 678-707). IGI Global.Search in Google Scholar

Guezzaz, A., Asimi, Y., Azrour, M., & Asimi, A. (2021). Mathematical validation of proposed machine learning classifier for heterogeneous traffic and anomaly detection. Big Data Mining and Analytics, 4(1), 18-24. GuezzazA. AsimiY. AzrourM. & Asimi A. (2021). Mathematical validation of proposed machine learning classifier for heterogeneous traffic and anomaly detection. Big Data Mining and Analytics, 4(1), 18-24.Search in Google Scholar

Nannan Chong & Fan Yang. (2025). A monocular medical endoscopic images depth estimation method based on a confidence-guided dual-branch siamese network. Biomedical Signal Processing and Control107123-107123. NannanChong & FanYang. (2025). A monocular medical endoscopic images depth estimation method based on a confidence-guided dual-branch siamese network. Biomedical Signal Processing and Control107123-107123.Search in Google Scholar

Andres Lizano Villalobos,Benjamin Namikas & Xun Tang. (2024). Siamese neural network improves the performance of a convolutional neural network in colloidal self-assembly state classification. The Journal of chemical physics(20). AndresLizanoVillalobosBenjamin Namikas & XunTang. (2024). Siamese neural network improves the performance of a convolutional neural network in colloidal self-assembly state classification. The Journal of chemical physics(20).Search in Google Scholar

Liu Peng,Zhou Zeyu,Li Ning,Zhang Bowei,Han Minglei & Jiao Huiying. (2021). Contribution Assessment Approach for Command and Control System Based on Force-Sparsed Stacked-Auto Encoding Neural Networks. Journal of Physics: Conference Series(1). LiuPengZhouZeyuLiNingZhangBoweiHanMinglei & JiaoHuiying. (2021). Contribution Assessment Approach for Command and Control System Based on Force-Sparsed Stacked-Auto Encoding Neural Networks. Journal of Physics: Conference Series(1).Search in Google Scholar

Yiwen Chen,Guoguang Wen,Ahmed Rahmani,Zhaoxia Peng,Jun Jiang & Tingwen Huang. (2025). Resilient stepped transmission and control for nonlinear systems against DoS attacks. Automatica111990-111990. YiwenChenGuoguangWenAhmedRahmaniZhaoxiaPengJunJiang & TingwenHuang. (2025). Resilient stepped transmission and control for nonlinear systems against DoS attacks. Automatica111990-111990.Search in Google Scholar

Lingua:
Inglese
Frequenza di pubblicazione:
1 volte all'anno
Argomenti della rivista:
Scienze biologiche, Scienze della vita, altro, Matematica, Matematica applicata, Matematica generale, Fisica, Fisica, altro