The challenge of efficiently identifying anomalies in data sequences is an important statistical problem that now arises in many applications. Whilst there has been substantial work aimed at making statistical analyses robust to outliers, or point anomalies, there has been much less work on detecting anomalous segments…
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48 results for “collective anomalies”
In recent years, there has been a growing interest in identifying anomalous structure within multivariate data streams. We consider the problem of detecting collective anomalies, corresponding to intervals where one or more of the data streams behaves anomalously. We first develop a test for a single collective anomaly…
Enhanced neural networks detect thin boundaries between different types of anomalies.
problem Detecting thin boundaries between different types of anomalies in outlier detection.
method Defined new types of anomalies, enhanced Multi-Layer Perceptron Neural Network with Genetic Algorithm.
result Reduced error in anomaly detection compared to baseline methods.
An Encoder-Decoder Based Approach for Anomaly Detection with Application in Additive Manufacturingcs.LG
We present a novel unsupervised deep learning approach that utilizes the encoder-decoder architecture for detecting anomalies in sequential sensor data collected during industrial manufacturing. Our approach is designed not only to detect whether there exists an anomaly at a given time step, but also to predict what wi…
DiFF-RF detects point-wise and collective anomalies using random partitioning trees.
problem Detecting anomalies in data, especially collective anomalies.
method Random partitioning binary trees with distance-based leaves and semi-supervised learning.
result DiFF-RF significantly outperforms isolation forest and one-class SVM.
One-class Collective Anomaly Detection based on Long Short-Term Memory Recurrent Neural Networkscs.LG
Intrusion detection for computer network systems has been becoming one of the most critical tasks for network administrators today. It has an important role for organizations, governments and our society due to the valuable resources hosted on computer networks. Traditional misuse detection strategies are unable to det…
T2IVAE detects anomalies in time series data with high accuracy.
problem Detecting anomalies in noisy, complex time series data.
method Unsupervised model based on NVAE and GANs, transforming 1D to 2D images.
result T2IVAE outperforms other models on various datasets.
Paper introduces MJ distances for better anomaly detection in time series.
problem Anomaly detection in large collections of time series.
method Introduces semi-metric MJ distances for measuring structural breaks.
result MJ distances outperform existing metrics in detecting similarity and anomalies.
Survey categorizes time series anomaly detection methods.
problem Need for anomaly detection in time series data.
method Process-centric taxonomy of anomaly detection methods.
result Meta-analysis of time series anomaly detection trends.
We propose an algorithm for detecting patterns exhibited by anomalous clusters in high dimensional discrete data. Unlike most anomaly detection (AD) methods, which detect individual anomalies, our proposed method detects groups (clusters) of anomalies; i.e. sets of points which collectively exhibit abnormal patterns. I…
Informative Path Planning for Extreme Anomaly Detection in Environment Exploration and Monitoringstat.ML
Novel path planning improves UAV detection of extreme anomalies.
problem Improper criteria for UAV anomaly detection.
method Mathematical criteria guiding UAV towards strong anomalies.
result Proposed approach outperforms traditional methods in various applications.
Dynamics, behaviours, and anomaly persistence in cryptocurrencies and equities surrounding COVID-19q-fin.ST
Study shows similarities and differences in crypto and equity dynamics during pandemic.
problem Comparing cryptocurrency and equity market dynamics during the pandemic.
method New methodologies applied to study cryptocurrency and equity market dynamics, including recently introduced methods for trajectory and anomaly analysis.
result Cryptocurrencies exhibit stronger collective dynamics and correlation, while equities show greater persistence in anomalies over time.
Anomaly detection for high-dimensional data using large deviations principle.
problem Challenges in anomaly detection for high-dimensional data.
method Large Deviations Anomaly Detection (LAD) algorithm.
result Outperforms state-of-the-art methods on high-dimensional data sets.
Paper proposes a new dataset for group anomaly detection in physics.
problem Group anomaly detection in physics.
method Development of a synthetic benchmark dataset and comparison of anomaly detection techniques.
result Demonstrated performance of anomaly detection techniques on the new dataset.
Anomaly detection is the process of finding data points that deviate from a baseline. In a real-life setting, anomalies are usually unknown or extremely rare. Moreover, the detection must be accomplished in a timely manner or the risk of corrupting the system might grow exponentially. In this work, we propose a two lev…
This review examines techniques for detecting anomalies in time series data.
problem Detecting anomalies in time series data.
method Taxonomy based on main aspects of outlier detection techniques.
result Structured and comprehensive state-of-the-art on outlier detection techniques in time series.
Paper proposes semi-supervised learning for bearing anomaly detection.
problem Challenges in obtaining accurate labels for bearing fault diagnosis.
method Uses deep variational autoencoders for semi-supervised learning.
result Improves anomaly detection accuracy by 3% to 30% using semi-supervised learning.
On Accurate and Reliable Anomaly Detection for Gas Turbine Combustors: A Deep Learning Approachcs.LG
Monitoring gas turbine combustors health, in particular, early detecting abnormal behaviors and incipient faults, is critical in ensuring gas turbines operating efficiently and in preventing costly unplanned maintenance. One popular means of detecting combustor abnormalities is through continuously monitoring exhaust g…
RePAD detects anomalies in streaming time series data in real-time.
problem Real-time anomaly detection for time series data without human intervention.
method RePAD uses LSTM to predict anomalies based on short-term historic data.
result RePAD detects anomalies proactively and provides early warnings.
mmFall detects falls using mmWave radar and a hybrid VRAE, achieving high accuracy.
problem Detecting falls accurately and privately using mmWave radar.
method Uses mmWave radar for body point cloud and centroid, combined with a hybrid VRAE for anomaly detection.
result Achieves 98% fall detection with 2 false alarms out of 50 falls.
A new method detects anomalies in multivariate streams without unit dependence.
problem Detect anomalies in multivariate streams without unit dependence.
method Proposes SigMahaKNN combining variance norm and path signature.
result SigMahaKNN detects anomalies better than existing methods.
Anomalies and outliers are common in real-world data, and they can arise from many sources, such as sensor faults. Accordingly, anomaly detection is important both for analyzing the anomalies themselves and for cleaning the data for further analysis of its ambient structure. Nonetheless, a precise definition of anomali…
Aircraft engine manufacturers collect large amount of engine related data during flights. These data are used to detect anomalies in the engines in order to help companies optimize their maintenance costs. This article introduces and studies a generic methodology that allows one to build automatic early signs of anomal…
Framework detects anomalies in real-time PMU data.
problem Anomaly detection in power grid operations.
method Statistical learning and dynamical model.
result Effective anomaly detection and classification.
Detecting early signs of failures (anomalies) in complex systems is one of the main goal of preventive maintenance. It allows in particular to avoid actual failures by (re)scheduling maintenance operations in a way that optimizes maintenance costs. Aircraft engine health monitoring is one representative example of a fi…
Deep learning autoencoder detects bee colony anomalies.
problem Early detection of bee swarms and other unusual behaviors.
method Deep Recurrent Autoencoder model trained on sensor data.
result Autoencoder detects anomalies independent of their origin.
This article provides a thorough meta-analysis of the anomaly detection problem. To accomplish this we first identify approaches to benchmarking anomaly detection algorithms across the literature and produce a large corpus of anomaly detection benchmarks that vary in their construction across several dimensions we deem…
Anomaly detection is a fundamental problem in data mining field with many real-world applications. A vast majority of existing anomaly detection methods predominately focused on data collected from a single source. In real-world applications, instances often have multiple types of features, such as images (ID photos, f…
This study benchmarks anomaly detection methods in a functional setup.
problem Efficient anomaly detection in high-frequency sensor data.
method Functional analysis approach for multivariate data.
result Comparison of anomaly detection methods on real datasets.
This review unifies deep and shallow anomaly detection methods.
problem Improving anomaly detection on complex datasets.
method Systematic comparison of deep and shallow approaches.
result Identifies common principles and assumptions in anomaly detection.
This paper proposes a communication-efficient deep anomaly detection framework for industrial IoT.
problem Accurately detecting anomalies in time-series data from edge devices in industrial IoT.
method A federated learning-based approach with an Attention Mechanism-based Convolutional Neural Network-Long Short Term Memory (AMCNN-LSTM) model and gradient compression.
result The proposed framework accurately and timely detects anomalies with reduced communication overhead.
MAD-GAN: Multivariate Anomaly Detection for Time Series Data with Generative Adversarial Networkscs.LG
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 detecti…
Insider trading is one of the numerous white collar crimes that can contribute to the instability of the economy. Traditionally, the detection of illegal insider trades has been a human-driven process. In this paper, we collect the insider tradings made available by the US Securities and Exchange Commissions (SEC) thro…
Anomaly detection model for large networks identifies attacks with reduced false positives.
problem Detecting anomalies in large, sparse, directed networks.
method Dynamic logistic model with latent factors, variational Bayesian estimation, case-control approximation.
result Model reduces false positives by identifying red team attack with half the detection rate.
TSML tackles anomaly detection and pattern discovery in industrial time series data.
problem Extracting and exploiting information from large industrial data to reduce downtimes and manufacturing errors.
method TSML uses a pipeline of lightweight filters to process industrial time series data in parallel.
result TSML effectively detects anomalies and discovers patterns in industrial time series data.
RTVAE uses β-divergence to detect anomalies in tabular data robustly.
problem Outliers in tabular data affect VAE training and anomaly detection.
method Robust VAE with β-divergence for mixed categorical and continuous features.
result Demonstrates effectiveness on network traffic anomaly detection.
RAD detects anomalies in unreliable data streams with up to 98% accuracy.
problem Anomaly detection in unreliable data sources.
method Two-layer on-line learning framework with data cleansing and oracle learning.
result RAD improves anomaly detection accuracy by up to 28%.
Develops a framework for continual learning in anomaly detection.
problem Deterioration of monitoring performance due to new defect categories.
method Pseudo replay-based class incremental learning with oversampling.
result Enhanced monitoring performance and flexibility in model architecture.
Novel method detects faults in helicopter transmissions using healthy data only.
problem Detecting faults in rare events in helicopter transmissions.
method Probabilistic anomaly detection using Bayesian measures.
result Method achieves competitive detection performance in real-world and benchmark applications.
Logsy detects anomalies in logs using a novel classification-based approach.
problem Anomaly detection in unstructured logs is challenging due to limited model generalization.
method Logsy learns log representations by distinguishing normal and anomaly logs using a classification-based approach with an attention-based encoder and hyperspherical loss function.
result Logsy improves anomaly detection performance by 0.25 in F1 score compared to previous methods.
Framework transfers complementary operating conditions to train anomaly detectors.
problem Training anomaly detectors on changing operating conditions requires comprehensive data, which is hard to obtain.
method Proposes unsupervised transfer learning to align and combine data from different units.
result Demonstrates improved anomaly detection in changing operating conditions.
DeepTrust uses NLP to quickly identify and verify financial anomalies on Twitter.
problem Unreliable information in financial markets leading to unexpected price changes.
method Machine learning for anomaly detection, NLP for information retrieval and reliability assessment.
result DeepTrust outperforms baseline classifiers in identifying financial anomalies.
Paper uses ensemble learning for IoT cybersecurity anomaly detection.
problem Anomaly detection in IoT data is challenging due to heterogeneous device types.
method Bayesian hyperparameter optimisation for ensemble learning.
result Ensemble learning with Bayesian optimisation improves anomaly detection accuracy.
GANs improve anomaly detection in power plants, achieving nearly perfect classification.
problem Anomaly detection in power generation plants to identify irregularities.
method Used Generative Adversarial Networks (GANs) for anomaly detection in power generation plants.
result GANs achieved an accuracy rate of 98.99% in anomaly detection, significantly improved by data augmentation.
Develops probabilistic forecasting for Sea Level Anomalies using Conformal Prediction on functional time series.
problem Forecasting and uncertainty quantification for Sea Level Anomalies.
method Functional data analysis, Conformal Prediction, Functional Autoregressive Processes.
result Proposed method provides accurate probabilistic predictions and uncertainty quantification for Sea Level Anomalies.
Paper proposes anomaly detection using Eigentraces and one-class classification.
problem Detect anomalies in system call trace data for Linux OS.
method One-class classification with Eigentraces feature extraction, Radial Basis Function neural network, and Random Forest.
result High performance in detecting anomalies and normal activities.
Applied Bayesian Structural Health Monitoring: inclinometer data anomaly detection and forecastingcs.LG
Bayesian methods detect and forecast inclinometer anomalies in UK rail data.
problem Detecting and predicting dangerous movements in earthwork slopes.
method Bayesian UQ techniques applied to latent Markov process and non-linear Bayesian filter.
result Anomaly detection and forecasting demonstrated on large real-world data.
Given a time series of graphs G(t) = (V, E(t)), t = 1, 2, ..., where the fixed vertex set V represents "actors" and an edge between vertex u and vertex v at time t (uv \in E(t)) represents the existence of a communications event between actors u and v during the tth time period, we wish to detect anomalies and/or chang…