Collaborating Authors

Towards Experienced Anomaly Detector Through Reinforcement Learning

AAAI Conferences

This abstract proposes a time series anomaly detector which 1) makes no assumption about the underlying mechanism of anomaly patterns, 2) refrains from the cumbersome work of threshold setting for good anomaly detection performance under specific scenarios, and 3) keeps evolving with the growth of anomaly detection experience. Essentially, the anomaly detector is powered by the Recurrent Neural Network (RNN) and adopts the Reinforcement Learning (RL) method to achieve the self-learning process. Our initial experiments demonstrate promising results of using the detector in network time series anomaly detection problems.

How to build robust anomaly detectors with machine learning


Modern software applications are often comprised of distributed microservices. Consider typical Software as a Service (SaaS) applications, which are accessed through web interfaces and run on the cloud. In part due to their physically distributed nature, managing and monitoring performance in these complex systems is becoming increasingly difficult. When issues such as performance degradations arise, it can be challenging to identify and debug the root causes. At Ericsson's Global AI Accelerator, we're exploring data-science based monitoring solutions that can learn to identify and categorize anomalous system behavior, and thereby improve incident resolution times.

Statistical Anomaly Detection for Train Fleets

AAAI Conferences

We have developed a method for statistical anomaly detection which has been deployed in a tool for condition monitoring of train fleets. The tool is currently used by several railway operators over the world to inspect and visualize the occurrence of event messages generated on the trains. The anomaly detection component helps the operators to quickly find significant deviations from normal behavior and to detect early indications for possible problems. The savings in maintenance costs comes mainly from avoiding costly breakdowns, and have been estimated to several million Euros per year for the tool. In the long run, it is expected that maintenance costs can be reduced with between 5 and 10 % by using the tool.

Anomaly Detection in Multivariate Non-stationary Time Series for Automatic DBMS Diagnosis Machine Learning

Anomaly detection in database management systems (DBMSs) is difficult because of increasing number of statistics (stat) and event metrics in big data system. In this paper, I propose an automatic DBMS diagnosis system that detects anomaly periods with abnormal DB stat metrics and finds causal events in the periods. Reconstruction error from deep autoencoder and statistical process control approach are applied to detect time period with anomalies. Related events are found using time series similarity measures between events and abnormal stat metrics. After training deep autoencoder with DBMS metric data, efficacy of anomaly detection is investigated from other DBMSs containing anomalies. Experiment results show effectiveness of proposed model, especially, batch temporal normalization layer. Proposed model is used for publishing automatic DBMS diagnosis reports in order to determine DBMS configuration and SQL tuning.

Anomaly Detection with Inexact Labels Machine Learning

Tomoharu Iwata 1 Machiko Toyoda 2 Shotaro Tora 2 Naonori Ueda 1 1 NTT Communication Science Laboratories 2 NTT Software Innovation Center Abstract We propose a supervised anomaly detection method for data with inexact anomaly labels, where each label, which is assigned to a set of instances, indicates that at least one instance in the set is anomalous. Although many anomaly detection methods have been proposed, they cannot handle inexact anomaly labels. To measure the performance with inexact anomaly labels, we define the inexact AUC, which is our extension of the area under the ROC curve (AUC) for inexact labels. The proposed method trains an anomaly score function so that the smooth approximation of the inexact AUC increases while anomaly scores for non-anomalous instances become low. The proposed method performs well even when only a small number of inexact labels are available by incorporating an unsupervised anomaly detection mechanism with inexact AUC maximization. Using various datasets, we experimentally demonstrate that our proposed method improves the anomaly detection performance with inexact anomaly labels, and outperforms existing unsupervised and supervised anomaly detection and multiple instance learning methods. 1 Introduction Anomaly detection is an important machine learning task, which is a task to find the anomalous instances in a dataset. Many unsupervised anomaly detection methods have been proposed (Breunig et al., 2000; Sch olkopf et al., 2001; Liu et al., 2008; Sakurada and Yairi, 2014).