DeepStream: Autoencoder-based stream temporal clustering and anomaly detection

Computers & Security - Tập 106 - Trang 102276 - 2021
Shimon Harush1, Yair Meidan1, Asaf Shabtai1
1Department of Software and Information Systems Engineering, Ben-Gurion University of the Negev, Israel

Tài liệu tham khảo

Kdd, 1999. cup 1999 data. http://kdd.ics.uci.edu/databases/kddcup99/kddcup99.html. Accessed: 2020-06-01. Aggarwal, 2007, 31 Aggarwal, 2018, A survey of stream clustering algorithms, 231 Aggarwal, 2005, On high dimensional projected clustering of data streams, Data Min. Knowl.dge Discov., 10, 251, 10.1007/s10618-005-0645-7 Aggarwal, 2003, A framework for clustering evolving data streams, 29, 81 Aljawarneh, 2020, Garuda: Gaussian dissimilarity measure for feature representation and anomaly detection in internet of things, J. Supercomput., 76, 4376, 10.1007/s11227-018-2397-3 Anguita, 2013, A public domain dataset for human activity recognition using smartphones., 3 Aytekin, 2018, Clustering and unsupervised anomaly detection with l 2 normalized deep auto-encoder representations, 1 Baker, 1998, Distributional clustering of words for text classification, 96 Bekkerman, 2001, On feature distributional clustering for text categorization, 146 Cao, 2006, Density-based clustering over an evolving data stream with noise, 328 Chen, G., 2015. Deep learning with nonparametric clustering. arXiv preprint arXiv:1501.03084 Chen, 2007, Density-based clustering for real-time stream data, 133 Chien, 2005, Semantic similarity between search engine queries using temporal correlation, 2 De Maesschalck, 2000, The mahalanobis distance, Chemom. Intell. Lab. Syst., 50, 1, 10.1016/S0169-7439(99)00047-7 Ding, 2015, Research on data stream clustering algorithms, Artif. Intell. Rev., 43, 593, 10.1007/s10462-013-9398-7 Gama, 2007 Glorot, 2011, Deep sparse rectifier neural networks, 315 Gower, 1985, Properties of euclidean and non-euclidean distance matrices, Linear Algebra Appl., 67, 81, 10.1016/0024-3795(85)90187-9 Hahsler, 2016, Clustering data streams based on shared density between micro-clusters, IEEE Trans. Knowl. Data Eng., 28, 1449, 10.1109/TKDE.2016.2522412 Hahsler, 2017, Introduction to stream: an extensible framework for data stream clustering research with r, J. Stat. Softw., 76, 1, 10.18637/jss.v076.i14 Henzinger, 1998, Computing on data streams., Extern. Memory Algorithms, 50, 107, 10.1090/dimacs/050/05 Hinton, 2006, Reducing the dimensionality of data with neural networks, science, 313, 504, 10.1126/science.1127647 Huang, 2014, Deep embedding network for clustering, 1532 Hubert, 1985, Comparing partitions, J. Classif., 2, 193, 10.1007/BF01908075 Klambauer, 2017, Self-normalizing neural networks, 971 Kontaki, 2008, Continuous trend-based clustering in data streams, 251 Kushwaha, 2017, Anomaly based intrusion detection using filter based feature selection on KDD-CUP 99, 839 Maas, 2013, Rectifier nonlinearities improve neural network acoustic models, 30, 3 Meidan, Y., Bohadana, M., Shabtai, A., Ochoa, M., Tippenhauer, N. O., Guarnizo, J. D., Elovici, Y., 2017. Detection of unauthorized IoT devices using machine learning techniques. arXiv preprint arXiv:1709.04647 Mirsky, 2017, Anomaly detection for smartphone data streams, Pervasive Mob. Comput., 35, 83, 10.1016/j.pmcj.2016.07.006 Mirsky, 2015, pcStream: a stream clustering algorithm for dynamically detecting and managing temporal contexts, 119 Nguyen, 2015, A survey on data stream clustering and classification, Knowl. Inf. Syst., 45, 535, 10.1007/s10115-014-0808-1 Pearson, 1901, LIII. On lines and planes of closest fit to systems of points in space, Lond. Edinb. Dublin Philos. Mag. J. Sci., 2, 559, 10.1080/14786440109462720 Puschmann, 2016, Adaptive clustering for dynamic IoT data streams, IEEE Internet Things J., 4, 64, 10.1109/JIOT.2016.2618909 Reiss, 2012, Introducing a new benchmarked dataset for activity monitoring, 108 Sharafaldin, 2018, Toward generating a new intrusion detection dataset and intrusion traffic characterization., 108 Silva, 2013, Data stream clustering: a survey, ACM Comput. Surv. (CSUR), 46, 13, 10.1145/2522968.2522981 Srivastava, 2014, Dropout: a simple way to prevent neural networks from overfitting, J. Mach. Learn. Res., 15, 1929 Su, 2015, A relationship between the average precision and the area under the ROC curve, 349 Tian, 2014, Learning deep representations for graph clustering, 1293 Vincent, 2008, Extracting and composing robust features with denoising autoencoders, 1096 Vincent, 2010, Stacked denoising autoencoders: learning useful representations in a deep network with a local denoising criterion, J. Mach. Learn. Res., 11, 3371 Weber, 2017 Wold, 1977, Simca: a method for analyzing chemical data in terms of similarity and analogy, Chemometrics, 52, 243 Xie, 2016, Unsupervised deep embedding for clustering analysis, 478 Yang, 2016, Joint unsupervised learning of deep representations and image clusters, 5147 Zeng, 2004, Learning to cluster web search results, 210