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Anomaly Detection

66 papers with code · Methodology

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BINet: Multi-perspective Business Process Anomaly Classification

8 Feb 2019tnolle/binet

Finally, we demonstrate that a simple set of rules can be used to utilize the output of BINet for anomaly classification. We compare BINet to eight other state-of-the-art anomaly detection algorithms and evaluate their performance on an elaborate data corpus of 29 synthetic and 15 real-life event logs.

ANOMALY DETECTION

08 Feb 2019

Active Anomaly Detection via Ensembles: Insights, Algorithms, and Interpretability

23 Jan 2019freedombenLiu/ad_examples

In this paper, we study the problem of active learning to automatically tune ensemble of anomaly detectors to maximize the number of true anomalies discovered. Second, we present several algorithms for active learning with tree-based AD ensembles.

ACTIVE LEARNING ANOMALY DETECTION

23 Jan 2019

MAD-GAN: Multivariate Anomaly Detection for Time Series Data with Generative Adversarial Networks

15 Jan 2019LiDan456/MAD-GANs

The prevalence of networked sensors and actuators in many real-world systems such as smart buildings, factories, power plants, and data centers generate substantial amounts of multivariate time series data for these systems. The rich sensor data can be continuously monitored for intrusion events through anomaly detection.

ANOMALY DETECTION TIME SERIES

15 Jan 2019

Autoencoders and Generative Adversarial Networks for Anomaly Detection for Sequences

8 Jan 2019stephanieger/sequence-anomaly-detection

We introduce synthetic oversampling in anomaly detection for multi-feature sequence datasets based on autoencoders and generative adversarial networks. The first approach considers the use of an autoencoder in conjunction with standard oversampling methods to generate synthetic data that captures the sequential nature of the data.

ANOMALY DETECTION OUTLIER DETECTION

08 Jan 2019

PyOD: A Python Toolbox for Scalable Outlier Detection

6 Jan 2019KarthikKothareddy/pyod

PyOD is an open-source Python toolbox for performing scalable outlier detection on multivariate data. Uniquely, it provides access to a wide range of outlier detection algorithms, including established outlier ensembles and more recent neural network-based approaches, under a single, well-documented API designed for use by both practitioners and researchers.

ANOMALY DETECTION OUTLIER ENSEMBLES

06 Jan 2019

An Evaluation of Methods for Real-Time Anomaly Detection using Force Measurements from the Turning Process

20 Dec 2018Yuanzhi-H/Real-Time-Detection-Methods

We examined the use of three conventional anomaly detection methods and assess their potential for on-line tool wear monitoring. Through efficient data processing and transformation of the algorithm proposed here, in a real-time environment, these methods were tested for fast evaluation of cutting tools on CNC machines.

ANOMALY DETECTION

20 Dec 2018

Video Trajectory Classification and Anomaly Detection Using Hybrid CNN-VAE

18 Dec 2018lisaong/hss

If such trajectories are used to understand the behavior (normal or anomalous) of moving objects, they need to be represented correctly. Finally, a hybrid CNN-VAE architecture has been used for trajectory classification and anomaly detection.

ANOMALY DETECTION TIME SERIES

18 Dec 2018

Mapper Comparison with Wasserstein Metrics

15 Dec 2018mikemccabe210/mapper_comparison

The challenge of describing model drift is an open question in unsupervised learning. It can be difficult to evaluate at what point an unsupervised model has deviated beyond what would be expected from a different sample from the same population.

ANOMALY DETECTION TOPOLOGICAL DATA ANALYSIS

15 Dec 2018

Deep Anomaly Detection with Outlier Exposure

ICLR 2019 hendrycks/outlier-exposure

The use of larger and more complex inputs in deep learning magnifies the difficulty of distinguishing between anomalous and in-distribution examples. We also analyze the flexibility and robustness of Outlier Exposure, and identify characteristics of the auxiliary dataset that improve performance.

ANOMALY DETECTION

11 Dec 2018

Adversarially Learned Anomaly Detection

6 Dec 2018houssamzenati/Adversarially-Learned-Anomaly-Detection

Anomaly detection is a significant and hence well-studied problem. However, developing effective anomaly detection methods for complex and high-dimensional data remains a challenge.

ANOMALY DETECTION

06 Dec 2018