Ensemble Neuroevolution-Based Approach for Multivariate Time Series Anomaly Detection
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Dominik Zurek | Kamil Pietak | Kamil Faber | Marcin Pietron | Kamil Faber | Kamil Piętak | D. Zurek | Marcin Pietron
[1] Charles C. Kemp,et al. A Multimodal Anomaly Detector for Robot-Assisted Feeding Using an LSTM-Based Variational Autoencoder , 2017, IEEE Robotics and Automation Letters.
[2] Maria A. Zuluaga,et al. USAD: UnSupervised Anomaly Detection on Multivariate Time Series , 2020, KDD.
[3] Clara Pizzuti,et al. Fast Outlier Detection in High Dimensional Spaces , 2002, PKDD.
[4] Hans-Peter Kriegel,et al. LOF: identifying density-based local outliers , 2000, SIGMOD 2000.
[5] Edgar Galván López,et al. Neuroevolution in Deep Neural Networks: Current Trends and Future Challenges , 2020, IEEE Transactions on Artificial Intelligence.
[6] J. Ma,et al. Time-series novelty detection using one-class support vector machines , 2003, Proceedings of the International Joint Conference on Neural Networks, 2003..
[7] Lei Shi,et al. MAD-GAN: Multivariate Anomaly Detection for Time Series Data with Generative Adversarial Networks , 2019, ICANN.
[8] Elliot Meyerson,et al. Evolving Deep Neural Networks , 2017, Artificial Intelligence in the Age of Neural Networks and Brain Computing.
[9] Hans-Peter Kriegel,et al. LOF: identifying density-based local outliers , 2000, SIGMOD '00.
[10] Bryan Hooi,et al. Graph Neural Network-Based Anomaly Detection in Multivariate Time Series , 2021, AAAI.
[11] Risto Miikkulainen,et al. Evolving Neural Networks through Augmenting Topologies , 2002, Evolutionary Computation.
[12] Ailong Ma,et al. SceneNet: Remote sensing scene classification deep learning network using multi-objective neural evolution architecture search , 2021 .
[13] Aditya P. Mathur,et al. WADI: a water distribution testbed for research in the design of secure cyber physical systems , 2017, CySWATER@CPSWeek.
[14] Bo Zong,et al. Deep Autoencoding Gaussian Mixture Model for Unsupervised Anomaly Detection , 2018, ICLR.
[15] Zhi-Hua Zhou,et al. Isolation Forest , 2008, 2008 Eighth IEEE International Conference on Data Mining.
[16] Nils Ole Tippenhauer,et al. SWaT: a water treatment testbed for research and training on ICS security , 2016, 2016 International Workshop on Cyber-physical Systems for Smart Water Networks (CySWater).
[17] Andrew L. Maas. Rectifier Nonlinearities Improve Neural Network Acoustic Models , 2013 .
[18] Xiaofei Xing,et al. A Malware Detection Approach Using Malware Images and Autoencoders , 2020, 2020 IEEE 17th International Conference on Mobile Ad Hoc and Sensor Systems (MASS).
[19] Yuval Elovici,et al. Kitsune: An Ensemble of Autoencoders for Online Network Intrusion Detection , 2018, NDSS.
[20] Kris Villez,et al. Anomaly Detection using Deep Autoencoders for in-situ Wastewater Systems Monitoring Data , 2020, ArXiv.
[21] USAD , 2020, Proceedings of the 26th ACM SIGKDD International Conference on Knowledge Discovery & Data Mining.
[22] Yue Dong,et al. Threaded ensembles of autoencoders for stream learning , 2018, Comput. Intell..
[23] Mengjie Zhang,et al. Evolving Deep Convolutional Neural Networks for Image Classification , 2017, IEEE Transactions on Evolutionary Computation.
[24] Rolf Isermann,et al. Model-based fault-detection and diagnosis - status and applications , 2004, Annu. Rev. Control..