292 lines
14 KiB
BibTeX
Executable File
292 lines
14 KiB
BibTeX
Executable File
@article{anomaly_detection_survey,
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author = {Chandola, Varun and Banerjee, Arindam and Kumar, Vipin},
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title = {Anomaly detection: A survey},
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year = {2009},
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issue_date = {July 2009},
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publisher = {Association for Computing Machinery},
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address = {New York, NY, USA},
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volume = {41},
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number = {3},
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issn = {0360-0300},
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url = {https://doi.org/10.1145/1541880.1541882},
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doi = {10.1145/1541880.1541882},
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abstract = {Anomaly detection is an important problem that has been researched
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within diverse research areas and application domains. Many anomaly
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detection techniques have been specifically developed for certain
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application domains, while others are more generic. This survey
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tries to provide a structured and comprehensive overview of the
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research on anomaly detection. We have grouped existing techniques
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into different categories based on the underlying approach adopted
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by each technique. For each category we have identified key
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assumptions, which are used by the techniques to differentiate
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between normal and anomalous behavior. When applying a given
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technique to a particular domain, these assumptions can be used as
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guidelines to assess the effectiveness of the technique in that
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domain. For each category, we provide a basic anomaly detection
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technique, and then show how the different existing techniques in
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that category are variants of the basic technique. This template
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provides an easier and more succinct understanding of the
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techniques belonging to each category. Further, for each category,
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we identify the advantages and disadvantages of the techniques in
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that category. We also provide a discussion on the computational
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complexity of the techniques since it is an important issue in real
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application domains. We hope that this survey will provide a better
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understanding of the different directions in which research has
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been done on this topic, and how techniques developed in one area
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can be applied in domains for which they were not intended to begin
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with.},
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journal = {ACM Comput. Surv.},
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month = jul,
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articleno = {15},
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numpages = {58},
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keywords = {outlier detection, Anomaly detection},
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},
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@dataset{alexander_kyuroson_2023_7913307,
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author = {Alexander Kyuroson and Niklas Dahlquist and Nikolaos Stathoulopoulos
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and Vignesh Kottayam Viswanathan and Anton Koval and George
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Nikolakopoulos},
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title = {Multimodal Dataset from Harsh Sub-Terranean Environment with Aerosol
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Particles for Frontier Exploration },
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month = may,
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year = 2023,
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publisher = {Zenodo},
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version = {v1},
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doi = {10.5281/zenodo.7913307},
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url = {https://doi.org/10.5281/zenodo.7913307},
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},
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@article{deepsad,
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author = {Lukas Ruff and Robert A. Vandermeulen and Nico G{\"{o}}rnitz and
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Alexander Binder and Emmanuel M{\"{u}}ller and Klaus{-}Robert M{\"{u}
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}ller and Marius Kloft},
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title = {Deep Semi-Supervised Anomaly Detection},
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journal = {CoRR},
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volume = {abs/1906.02694},
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year = {2019},
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url = {http://arxiv.org/abs/1906.02694},
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eprinttype = {arXiv},
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eprint = {1906.02694},
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timestamp = {Thu, 13 Jun 2019 13:36:00 +0200},
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biburl = {https://dblp.org/rec/journals/corr/abs-1906-02694.bib},
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bibsource = {dblp computer science bibliography, https://dblp.org},
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},
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@inproceedings{subter,
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title = {Multimodal Dataset from Harsh Sub-Terranean Environment with Aerosol
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Particles for Frontier Exploration},
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url = {http://dx.doi.org/10.1109/MED59994.2023.10185906},
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DOI = {10.1109/med59994.2023.10185906},
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booktitle = {2023 31st Mediterranean Conference on Control and Automation
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(MED)},
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publisher = {IEEE},
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author = {Kyuroson, Alexander and Dahlquist, Niklas and Stathoulopoulos,
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Nikolaos and Viswanathan, Vignesh Kottayam and Koval, Anton and
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Nikolakopoulos, George},
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year = {2023},
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month = jun,
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pages = {716–721},
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}
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,
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@inproceedings{deepsvdd,
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title = {Deep One-Class Classification},
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author = {Ruff, Lukas and Vandermeulen, Robert and Goernitz, Nico and Deecke,
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Lucas and Siddiqui, Shoaib Ahmed and Binder, Alexander and M{\"u}ller
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, Emmanuel and Kloft, Marius},
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booktitle = {Proceedings of the 35th International Conference on Machine
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Learning},
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pages = {4393--4402},
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year = {2018},
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editor = {Dy, Jennifer and Krause, Andreas},
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volume = {80},
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series = {Proceedings of Machine Learning Research},
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month = {10--15 Jul},
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publisher = {PMLR},
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pdf = {http://proceedings.mlr.press/v80/ruff18a/ruff18a.pdf},
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url = {https://proceedings.mlr.press/v80/ruff18a.html},
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abstract = {Despite the great advances made by deep learning in many machine
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learning problems, there is a relative dearth of deep learning
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approaches for anomaly detection. Those approaches which do exist
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involve networks trained to perform a task other than anomaly
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detection, namely generative models or compression, which are in
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turn adapted for use in anomaly detection; they are not trained on
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an anomaly detection based objective. In this paper we introduce a
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new anomaly detection method—Deep Support Vector Data Description—,
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which is trained on an anomaly detection based objective. The
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adaptation to the deep regime necessitates that our neural network
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and training procedure satisfy certain properties, which we
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demonstrate theoretically. We show the effectiveness of our method
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on MNIST and CIFAR-10 image benchmark datasets as well as on the
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detection of adversarial examples of GTSRB stop signs.},
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},
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@inproceedings{pmlr-v80-ruff18a,
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title = {Deep One-Class Classification},
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author = {Ruff, Lukas and Vandermeulen, Robert and Goernitz, Nico and Deecke,
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Lucas and Siddiqui, Shoaib Ahmed and Binder, Alexander and M{\"u}ller
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, Emmanuel and Kloft, Marius},
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booktitle = {Proceedings of the 35th International Conference on Machine
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Learning},
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pages = {4393--4402},
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year = {2018},
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editor = {Dy, Jennifer and Krause, Andreas},
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volume = {80},
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series = {Proceedings of Machine Learning Research},
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month = {10--15 Jul},
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publisher = {PMLR},
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pdf = {http://proceedings.mlr.press/v80/ruff18a/ruff18a.pdf},
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url = {https://proceedings.mlr.press/v80/ruff18a.html},
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abstract = {Despite the great advances made by deep learning in many machine
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learning problems, there is a relative dearth of deep learning
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approaches for anomaly detection. Those approaches which do exist
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involve networks trained to perform a task other than anomaly
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detection, namely generative models or compression, which are in
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turn adapted for use in anomaly detection; they are not trained on
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an anomaly detection based objective. In this paper we introduce a
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new anomaly detection method—Deep Support Vector Data Description—,
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which is trained on an anomaly detection based objective. The
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adaptation to the deep regime necessitates that our neural network
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and training procedure satisfy certain properties, which we
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demonstrate theoretically. We show the effectiveness of our method
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on MNIST and CIFAR-10 image benchmark datasets as well as on the
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detection of adversarial examples of GTSRB stop signs.},
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},
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@inproceedings{anomaly_detection_medical,
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author = {{Wei}, Qi and {Ren}, Yinhao and {Hou}, Rui and {Shi}, Bibo and {Lo},
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Joseph Y. and {Carin}, Lawrence},
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title = "{Anomaly detection for medical images based on a one-class
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classification}",
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booktitle = {Medical Imaging 2018: Computer-Aided Diagnosis},
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year = 2018,
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editor = {{Petrick}, Nicholas and {Mori}, Kensaku},
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series = {Society of Photo-Optical Instrumentation Engineers (SPIE) Conference
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Series},
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volume = {10575},
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month = feb,
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eid = {105751M},
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pages = {105751M},
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doi = {10.1117/12.2293408},
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adsurl = {https://ui.adsabs.harvard.edu/abs/2018SPIE10575E..1MW},
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adsnote = {Provided by the SAO/NASA Astrophysics Data System},
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},
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@article{anomaly_detection_defi,
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author = {Ul Hassan, Muneeb and Rehmani, Mubashir Husain and Chen, Jinjun},
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journal = {IEEE Communications Surveys & Tutorials},
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title = {Anomaly Detection in Blockchain Networks: A Comprehensive Survey},
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year = {2023},
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volume = {25},
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number = {1},
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pages = {289-318},
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keywords = {Blockchains;Anomaly detection;Security;Smart
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contracts;Privacy;Bitcoin;Tutorials;Blockchain;anomaly
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detection;fraud detection},
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doi = {10.1109/COMST.2022.3205643},
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}
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,
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@article{anomaly_detection_manufacturing,
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AUTHOR = {Oh, Dong Yul and Yun, Il Dong},
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TITLE = {Residual Error Based Anomaly Detection Using Auto-Encoder in SMD
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Machine Sound},
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JOURNAL = {Sensors},
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VOLUME = {18},
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YEAR = {2018},
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NUMBER = {5},
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ARTICLE-NUMBER = {1308},
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URL = {https://www.mdpi.com/1424-8220/18/5/1308},
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PubMedID = {29695084},
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ISSN = {1424-8220},
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ABSTRACT = {Detecting an anomaly or an abnormal situation from given noise is
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highly useful in an environment where constantly verifying and
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monitoring a machine is required. As deep learning algorithms are
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further developed, current studies have focused on this problem.
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However, there are too many variables to define anomalies, and the
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human annotation for a large collection of abnormal data labeled at
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the class-level is very labor-intensive. In this paper, we propose
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to detect abnormal operation sounds or outliers in a very complex
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machine along with reducing the data-driven annotation cost. The
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architecture of the proposed model is based on an auto-encoder, and
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it uses the residual error, which stands for its reconstruction
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quality, to identify the anomaly. We assess our model using
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Surface-Mounted Device (SMD) machine sound, which is very complex,
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as experimental data, and state-of-the-art performance is
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successfully achieved for anomaly detection.},
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DOI = {10.3390/s18051308},
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},
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@article{anomaly_detection_history,
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author = {F.Y. Edgeworth and},
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title = {XLI. On discordant observations },
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journal = {The London, Edinburgh, and Dublin Philosophical Magazine and
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Journal of Science},
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volume = {23},
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number = {143},
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pages = {364--375},
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year = {1887},
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publisher = {Taylor \& Francis},
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doi = {10.1080/14786448708628471},
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URL = { https://doi.org/10.1080/14786448708628471 },
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eprint = { https://doi.org/10.1080/14786448708628471 },
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},
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@inproceedings{degradation_quantification_rain,
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author = {Zhang, Chen and Huang, Zefan and Ang, Marcelo H. and Rus, Daniela},
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booktitle = {2021 IEEE/RSJ International Conference on Intelligent Robots and
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Systems (IROS)},
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title = {LiDAR Degradation Quantification for Autonomous Driving in Rain},
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year = {2021},
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volume = {},
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number = {},
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pages = {3458-3464},
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keywords = {Degradation;Location awareness;Laser radar;Rain;Codes;System
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performance;Current measurement},
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doi = {10.1109/IROS51168.2021.9636694},
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},
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@article{deep_learning_overview,
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title = {Deep learning in neural networks: An overview},
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journal = {Neural Networks},
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volume = {61},
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pages = {85-117},
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year = {2015},
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issn = {0893-6080},
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doi = {https://doi.org/10.1016/j.neunet.2014.09.003},
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url = {https://www.sciencedirect.com/science/article/pii/S0893608014002135},
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author = {Jürgen Schmidhuber},
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keywords = {Deep learning, Supervised learning, Unsupervised learning,
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Reinforcement learning, Evolutionary computation},
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abstract = {In recent years, deep artificial neural networks (including
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recurrent ones) have won numerous contests in pattern recognition
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and machine learning. This historical survey compactly summarizes
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relevant work, much of it from the previous millennium. Shallow and
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Deep Learners are distinguished by the depth of their credit
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assignment paths, which are chains of possibly learnable, causal
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links between actions and effects. I review deep supervised
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learning (also recapitulating the history of backpropagation),
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unsupervised learning, reinforcement learning & evolutionary
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computation, and indirect search for short programs encoding deep
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and large networks.},
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},
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@article{autoencoder_survey,
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title = {A comprehensive survey on design and application of autoencoder in
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deep learning},
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journal = {Applied Soft Computing},
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volume = {138},
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pages = {110176},
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year = {2023},
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issn = {1568-4946},
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doi = {https://doi.org/10.1016/j.asoc.2023.110176},
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url = {https://www.sciencedirect.com/science/article/pii/S1568494623001941},
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author = {Pengzhi Li and Yan Pei and Jianqiang Li},
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keywords = {Deep learning, Autoencoder, Unsupervised learning, Feature
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extraction, Autoencoder application},
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abstract = {Autoencoder is an unsupervised learning model, which can
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automatically learn data features from a large number of samples
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and can act as a dimensionality reduction method. With the
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development of deep learning technology, autoencoder has attracted
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the attention of many scholars. Researchers have proposed several
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improved versions of autoencoder based on different application
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fields. First, this paper explains the principle of a conventional
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autoencoder and investigates the primary development process of an
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autoencoder. Second, We proposed a taxonomy of autoencoders
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according to their structures and principles. The related
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autoencoder models are comprehensively analyzed and discussed. This
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paper introduces the application progress of autoencoders in
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different fields, such as image classification and natural language
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processing, etc. Finally, the shortcomings of the current
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autoencoder algorithm are summarized, and prospected for its future
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development directions are addressed.},
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}
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