Abstract
Anomaly detection is a major problem that has been well studied in various fields of research and fields of application. In this paper, we present several methods that can be built on existing deep learning solutions for unsupervised anomaly detection, so that outliers can be separated from normal data in an efficient manner. We focus on approaches that use generative adversarial networks (GAN) and autoencoders for anomaly detection. By using these deep anomaly detection techniques, we can overcome the problem that we need to have a large-scale anomaly data in the learning phase of a detection system. So, we compared various methods of machine based and deep learning anomaly detection with its application in various fields. This article used seven available datasets. We report the results on anomaly detection datasets, using performance metrics, and discuss their performance on finding clustered and low density anomalies.
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References
Adler, J., Lunz, S.: Banach wasserstein gan. In: Advances in Neural Information Processing Systems, pp. 6754–6763 (2018)
Akçay, S., Abarghouei, A.A., Breckon, T.P.: Ganomaly: semi-supervised anomaly detection via adversarial training. In: ACCV (2018)
Akçay, S., Atapour-Abarghouei, A., Breckon, T.P.: Skip-ganomaly: Skip connected and adversarially trained encoder-decoder anomaly detection (2019). arXiv preprint arXiv:1901.08954
Antipov, G., Baccouche, M., Dugelay, J.L.: Face aging with conditional generative adversarial networks. In: 2017 IEEE International Conference on Image Processing (ICIP), pp. 2089–2093. IEEE (2017)
Breunig, M.M., Kriegel, H.P., Ng, R.T., Sander, J.: Lof: identifying density-based local outliers. In: ACM Sigmod Record, vol. 29, pp. 93–104. ACM (2000)
Chen, Y., Zhang, J., Yeo, C.K.: Network anomaly detection using federated deep autoencoding gaussian mixture model. In: International Conference on Machine Learning for Networking, pp. 1–14. Springer (2019)
Donahue, J., Krähenbühl, P., Darrell, T.: Adversarial feature learning (2016). arXiv preprint arXiv:1605.09782
Dong, H., Liang, X., Gong, K., Lai, H., Zhu, J., Yin, J.: Soft-gated warping-gan for pose-guided person image synthesis. In: Advances in Neural Information Processing Systems, pp. 474–484 (2018)
Ester, M., Kriegel, H.P., Sander, J., Xu, X., et al.: A density-based algorithm for discovering clusters in large spatial databases with noise. Kdd. 96, 226–231 (1996)
Ge, Y., Li, Z., Zhao, H., Yin, G., Yi, S., Wang, X., et al.: Fd-gan: pose-guided feature distilling gan for robust person re-identification. In: Advances in Neural Information Processing Systems, pp. 1222–1233 (2018)
Goldstein, M., Dengel, A.: Histogram-based outlier score (hbos): a fast unsupervised anomaly detection algorithm. In: Poster and Demo Track of the 35th German Conference on Artificial Intelligence, pp. 59–63 (2012)
Goodfellow, I., Pouget-Abadie, J., Mirza, M., Xu, B., Warde-Farley, D., Ozair, S., Courville, A., Bengio, Y.: Generative adversarial nets. In: Advances in Neural Information Processing Systems, pp. 2672–2680 (2014)
Hawkins, D.M.: Identification of Outliers, vol. 11. Springer (1980)
Lazarevic, A., Kumar, V.: Feature bagging for outlier detection. In: Proceedings of the Eleventh ACM SIGKDD International Conference on Knowledge Discovery in Data Mining, pp. 157–166. ACM (2005)
Lim, S.K., Loo, Y., Tran, N.T., Cheung, N.M., Roig, G., Elovici, Y.: Doping: Generative data augmentation for unsupervised anomaly detection with gan. In: 2018 IEEE International Conference on Data Mining (ICDM), pp. 1122–1127. IEEE (2018)
Liu, F.T., Ting, K.M., Zhou, Z.H.: Isolation-based anomaly detection. ACM Trans. Knowl. Discov. Data (TKDD) 6(1), 3 (2012)
Liu, S., Sun, Y., Zhu, D., Bao, R., Wang, W., Shu, X., Yan, S.: Face aging with contextual generative adversarial nets. In: Proceedings of the 25th ACM International Conference on Multimedia, pp. 82–90. ACM (2017)
Liu, Y., Li, Z., Zhou, C., Jiang, Y., Sun, J., Wang, M., He, X.: Generative adversarial active learning for unsupervised outlier detection. IEEE Trans. Knowl, Data Eng (2019)
Matsumoto, M., Saito, N., Ogawa, T., Haseyama, M.: Chronic gastritis detection from gastric x-ray images via deep autoencoding gaussian mixture models. In: 2019 IEEE 1st Global Conference on Life Sciences and Technologies (LifeTech), pp. 231–232. IEEE (2019)
Menon, A.K., Williamson, R.C.: A loss framework for calibrated anomaly detection. In: Proceedings of the 32nd International Conference on Neural Information Processing Systems, pp. 1494–1504. Curran Associates Inc. (2018)
Mika, S., Schölkopf, B., Smola, A.J., Müller, K.R., Scholz, M., Rätsch, G.: Kernel pca and de-noising in feature spaces. In: Advances in Neural Information Processing Systems, pp. 536–542 (1999)
Ramaswamy, S., Rastogi, R., Shim, K.: Efficient algorithms for mining outliers from large data sets. In: ACM Sigmod Record, vol. 29, pp. 427–438. ACM (2000)
Schlegl, T., Seeböck, P., Waldstein, S.M., Schmidt-Erfurth, U., Langs, G.: Unsupervised anomaly detection with generative adversarial networks to guide marker discovery. In: International Conference on Information Processing in Medical Imaging, pp. 146–157. Springer (2017)
Schölkopf, B., Platt, J.C., Shawe-Taylor, J., Smola, A.J., Williamson, R.C.: Estimating the support of a high-dimensional distribution. Neural Comput. 13(7), 1443–1471 (2001)
Schölkopf, B., Smola, A., Müller, K.R.: Kernel principal component analysis. In: International Conference on Artificial Neural Networks, pp. 583–588. Springer (1997)
Syarif, I., Prugel-Bennett, A., Wills, G.: Unsupervised clustering approach for network anomaly detection. In: International Conference on Networked Digital Technologies, pp. 135–145. Springer (2012)
Zong, B., Song, Q., Min, M.R., Cheng, W., Lumezanu, C., Cho, D., Chen, H.: Deep autoencoding gaussian mixture model for unsupervised anomaly detection. In: International Conference on Learning Representations (2018)
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Ounasser, N., Rhanoui, M., Mikram, M., Asri, B.E. (2022). Generative and Autoencoder Models for Large-Scale Mutivariate Unsupervised Anomaly Detection. In: Ben Ahmed, M., Teodorescu, HN.L., Mazri, T., Subashini, P., Boudhir, A.A. (eds) Networking, Intelligent Systems and Security. Smart Innovation, Systems and Technologies, vol 237. Springer, Singapore. https://doi.org/10.1007/978-981-16-3637-0_4
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