Anomaly Detection for imbalanced datasets with Deep Generative Models

Many important data analysis applications present with severely imbalanced datasets with respect to the target variable. A typical example is medical image analysis, where positive samples are scarce, while performance is commonly estimated against the correct detection of these positive examples. We approach this challenge by formulating the problem as anomaly detection with generative models. We train a generative model without supervision on the `negative' (common) datapoints and use this model to estimate the likelihood of unseen data. A successful model allows us to detect the `positive' case as low likelihood datapoints. In this position paper, we present the use of state-of-the-art deep generative models (GAN and VAE) for the estimation of a likelihood of the data. Our results show that on the one hand both GANs and VAEs are able to separate the `positive' and `negative' samples in the MNIST case. On the other hand, for the NLST case, neither GANs nor VAEs were able to capture the complexity of the data and discriminate anomalies at the level that this task requires. These results show that even though there are a number of successes presented in the literature for using generative models in similar applications, there remain further challenges for broad successful implementation.Comment: 15 pages, 13 figures, accepted by Benelearn 2018 conferenc

Anomaly detection for imbalanced datasets with deep generative models

Many important data analysis applications present with severely imbalanced datasets with respect to the target variable. A typical example is medical image analysis, where positive samples are scarce, while performance is commonly estimated against the correct detection of these positive examples. We approach this challenge by formulating the problem as anomaly detection with generative models. We train a generative model without supervision on the ‘negative’ (common) datapoints and use this model to estimate the likelihood of unseen data. A successful model allows us to detect the ‘positive’ case as low likelihooddatapoints.In this position paper, we present the use of state-of-the-art deep generative models (GAN and VAE) for the estimation of a likelihood of the data. Our results show that on the one hand both GANs and VAEs are able to separate the ‘positive’ and ‘negative’ samples in the MNIST case. On the other hand, for the NLST case, neither GANs nor VAEs were able to capture the complexity of the data and discriminate anomalies at the level that this task requires. These results show that even though there are a number of successes presented in the literature for using generative models in similar applications, there remain further challenges for broad successful implementation

Anomaly detection for imbalanced datasets with deep generative models

Many important data analysis applications present with severely imbalanced datasets with respect to the target variable. A typical example is medical image analysis, where positive samples are scarce, while performance is commonly estimated against the correct detection of these positive examples. We approach this challenge by formulating the problem as anomaly detection with generative models. We train a generative model without supervision on the ‘negative’ (common) datapoints and use this model to estimate the likelihood of unseen data. A successful model allows us to detect the ‘positive’ case as low likelihood datapoints. In this position paper, we present the use of state-of-the-art deep generative models (GAN and VAE) for the estimation of a likelihood of the data. Our results show that on the one hand both GANs and VAEs are able to separate the ‘positive’ and ‘negative’ samples in the MNIST case. On the other hand, for the NLST case, neither GANs nor VAEs were able to capture the complexity of the data and discriminate anomalies at the level that this task requires. These results show that even though there are a number of successes presented in the literature for using generative models in similar applications, there remain further challenges for broad successful implementation

Anomaly detection for imbalanced datasets with deep generative models

Many important data analysis applications present with severely imbalanced datasets with respect to the target variable. A typical example is medical image analysis, where positive samples are scarce, while performance is commonly estimated against the correct detection of these positive examples. We approach this challenge by formulating the problem as anomaly detection with generative models. We train a generative model without supervision on the ‘negative’ (common) datapoints and use this model to estimate the likelihood of unseen data. A successful model allows us to detect the ‘positive’ case as low likelihood\u3cbr/\u3edatapoints.\u3cbr/\u3eIn this position paper, we present the use of state-of-the-art deep generative models (GAN and VAE) for the estimation of a likelihood of the data. Our results show that on the one hand both GANs and VAEs are able to separate the ‘positive’ and ‘negative’ samples in the MNIST case. On the other hand, for the NLST case, neither GANs nor VAEs were able to capture the complexity of the data and discriminate anomalies at the level that this task requires. These results show that even though there are a number of successes presented in the literature for using generative models in similar applications, there remain further challenges for broad successful implementation

Spatiotemporal anomaly detection: streaming architecture and algorithms

Includes bibliographical references.2020 Summer.Anomaly detection is the science of identifying one or more rare or unexplainable samples or events in a dataset or data stream. The field of anomaly detection has been extensively studied by mathematicians, statisticians, economists, engineers, and computer scientists. One open research question remains the design of distributed cloud-based architectures and algorithms that can accurately identify anomalies in previously unseen, unlabeled streaming, multivariate spatiotemporal data. With streaming data, time is of the essence, and insights are perishable. Real-world streaming spatiotemporal data originate from many sources, including mobile phones, supervisory control and data acquisition enabled (SCADA) devices, the internet-of-things (IoT), distributed sensor networks, and social media. Baseline experiments are performed on four (4) non-streaming, static anomaly detection multivariate datasets using unsupervised offline traditional machine learning (TML), and unsupervised neural network techniques. Multiple architectures, including autoencoders, generative adversarial networks, convolutional networks, and recurrent networks, are adapted for experimentation. Extensive experimentation demonstrates that neural networks produce superior detection accuracy over TML techniques. These same neural network architectures can be extended to process unlabeled spatiotemporal streaming using online learning. Space and time relationships are further exploited to provide additional insights and increased anomaly detection accuracy. A novel domain-independent architecture and set of algorithms called the Spatiotemporal Anomaly Detection Environment (STADE) is formulated. STADE is based on federated learning architecture. STADE streaming algorithms are based on a geographically unique, persistently executing neural networks using online stochastic gradient descent (SGD). STADE is designed to be pluggable, meaning that alternative algorithms may be substituted or combined to form an ensemble. STADE incorporates a Stream Anomaly Detector (SAD) and a Federated Anomaly Detector (FAD). The SAD executes at multiple locations on streaming data, while the FAD executes at a single server and identifies global patterns and relationships among the site anomalies. Each STADE site streams anomaly scores to the centralized FAD server for further spatiotemporal dependency analysis and logging. The FAD is based on recent advances in DNN-based federated learning. A STADE testbed is implemented to facilitate globally distributed experimentation using low-cost, commercial cloud infrastructure provided by Microsoft™. STADE testbed sites are situated in the cloud within each continent: Africa, Asia, Australia, Europe, North America, and South America. Communication occurs over the commercial internet. Three STADE case studies are investigated. The first case study processes commercial air traffic flows, the second case study processes global earthquake measurements, and the third case study processes social media (i.e., Twitter™) feeds. These case studies confirm that STADE is a viable architecture for the near real-time identification of anomalies in streaming data originating from (possibly) computationally disadvantaged, geographically dispersed sites. Moreover, the addition of the FAD provides enhanced anomaly detection capability. Since STADE is domain-independent, these findings can be easily extended to additional application domains and use cases