Unsupervised anomaly detection for underwater gliders using generative adversarial networks

An effective anomaly detection system is critical for marine autonomous systems operating in complex and dynamic marine environments to reduce operational costs and achieve concurrent large-scale fleet deployments. However, developing an automated fault detection system remains challenging for several reasons including limited data transmission via satellite services. Currently, most anomaly detection for marine autonomous systems, such as underwater gliders, rely on intensive analysis by pilots. This study proposes an unsupervised anomaly detection system using bidirectional generative adversarial networks guided by assistive hints for marine autonomous systems with time series data collected by multiple sensors. In this study, the anomaly detection system for a fleet of underwater gliders is trained on two healthy deployment datasets and tested on other nine deployment datasets collected by a selection of vehicles operating in a range of locations and environmental conditions. The system is successfully applied to detect anomalies in the nine test deployments, which include several different types of anomalies as well as healthy behaviour. Also, a sensitivity study of the data decimation settings suggests the proposed system is robust for Near Real-Time anomaly detection for underwater gliders

Federated Variational Learning for Anomaly Detection in Multivariate Time Series

Anomaly detection has been a challenging task given high-dimensional multivariate time series data generated by networked sensors and actuators in Cyber-Physical Systems (CPS). Besides the highly nonlinear, complex, and dynamic nature of such time series, the lack of labeled data impedes data exploitation in a supervised manner and thus prevents an accurate detection of abnormal phenomenons. On the other hand, the collected data at the edge of the network is often privacy sensitive and large in quantity, which may hinder the centralized training at the main server. To tackle these issues, we propose an unsupervised time series anomaly detection framework in a federated fashion to continuously monitor the behaviors of interconnected devices within a network and alert for abnormal incidents so that countermeasures can be taken before undesired consequences occur. To be specific, we leave the training data distributed at the edge to learn a shared Variational Autoencoder (VAE) based on Convolutional Gated Recurrent Unit (ConvGRU) model, which jointly captures feature and temporal dependencies in the multivariate time series data for representation learning and downstream anomaly detection tasks. Experiments on three real-world networked sensor datasets illustrate the advantage of our approach over other state-of-the-art models. We also conduct extensive experiments to demonstrate the effectiveness of our detection framework under non-federated and federated settings in terms of overall performance and detection latency

Correct and Control Complex IoT Systems: Evaluation of a Classification for System Anomalies

In practice there are deficiencies in precise interteam communications about system anomalies to perform troubleshooting and postmortem analysis along different teams operating complex IoT systems. We evaluate the quality in use of an adaptation of IEEE Std. 1044-2009 with the objective to differentiate the handling of fault detection and fault reaction from handling of defect and its options for defect correction. We extended the scope of IEEE Std. 1044-2009 from anomalies related to software only to anomalies related to complex IoT systems. To evaluate the quality in use of our classification a study was conducted at Robert Bosch GmbH. We applied our adaptation to a postmortem analysis of an IoT solution and evaluated the quality in use by conducting interviews with three stakeholders. Our adaptation was effectively applied and interteam communications as well as iterative and inductive learning for product improvement were enhanced. Further training and practice are required.Comment: Submitted to QRS 2020 (IEEE Conference on Software Quality, Reliability and Security

Learning Deep Representations of Appearance and Motion for Anomalous Event Detection

We present a novel unsupervised deep learning framework for anomalous event detection in complex video scenes. While most existing works merely use hand-crafted appearance and motion features, we propose Appearance and Motion DeepNet (AMDN) which utilizes deep neural networks to automatically learn feature representations. To exploit the complementary information of both appearance and motion patterns, we introduce a novel double fusion framework, combining both the benefits of traditional early fusion and late fusion strategies. Specifically, stacked denoising autoencoders are proposed to separately learn both appearance and motion features as well as a joint representation (early fusion). Based on the learned representations, multiple one-class SVM models are used to predict the anomaly scores of each input, which are then integrated with a late fusion strategy for final anomaly detection. We evaluate the proposed method on two publicly available video surveillance datasets, showing competitive performance with respect to state of the art approaches.Comment: Oral paper in BMVC 201