This work is licensed under the Creative Commons Attribution 4.0 International License.
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. & AnsariN. (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. & GarubaM. (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. & RolloF. (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. & HaasC. (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. & RodriguezJ. (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. & VelascoL. (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.ArjunanT. (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.AhmedM. (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. & JoshiG. 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. & JonesR. (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. & ZhangZ. (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.RaiynJ. (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. & CasasP. (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. & CaoX. (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. & ShanmugapriyaD. (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. & AsimiA. (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