IDK improves anomaly detection for points and groups without explicit learning.
problem Anomaly detection for points and groups using kernel methods.
method Isolation Distributional Kernel (IDK) addresses data independence and intractable dimensionality issues.
result IDK outperforms existing methods for both point and group anomaly detection.
We propose the Autoencoding Binary Classifiers (ABC), a novel supervised anomaly detector based on the Autoencoder (AE). There are two main approaches in anomaly detection: supervised and unsupervised. The supervised approach accurately detects the known anomalies included in training data, but it cannot detect the unk…
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…
A new anomaly detection method using partial identification.
problem Detecting anomalies in large datasets.
method Partial Identification framework and PIDScore geometric anomaly measure.
result PIDForest outperforms other methods in anomaly detection.
CAD detects anomalies and selects prototypes using polyhedron curvature.
problem Anomaly detection and prototype selection in data.
method Curvature Anomaly Detection (CAD) and Kernel CAD approach using polyhedron curvature.
result The proposed methods are effective for anomaly detection and prototype selection.
Proposes BA method for unbiased time series anomaly detection evaluation.
problem Anomalies in time series data are rare, making F1-score unreliable.
method Introduces Balanced Point Adjustment (BA) to address F1-score bias.
result BA provides fairer evaluation of time series anomaly detectors.
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.
AEGR method improves anomaly detection in autoencoders without needing anomaly-free training data.
problem Challenges in anomaly detection, especially high dimensionality and noise in training sets.
method Gradient-reversal method for autoencoders, using reconstruction error and Local Outlier Factor.
result The proposed AEGR model outperforms other methods in detecting network anomalies.
A new model classifies surface anomalies in 3D point cloud data.
problem Accurate classification of surface anomalies in manufacturing processes.
method Deep subspace learning approach for 3D point cloud data.
result The method effectively identifies new types of anomalies.
We propose a novel non-parametric adaptive anomaly detection algorithm for high dimensional data based on rank-SVM. Data points are first ranked based on scores derived from nearest neighbor graphs on n-point nominal data. We then train a rank-SVM using this ranked data. A test-point is declared as an anomaly at alpha-…
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…
Enhances deep learning models for anomaly detection in time series data.
problem Anomalies in time series data corrupt performance of models.
method Monte Carlo EM for inferring anomaly indicators during training.
result Improves model performance on nominal data and anomalous points.
Novel method improves load estimation in power grids using anomaly and change point detection.
problem Improving load estimation in power grid systems.
method Combining unsupervised anomaly and change point detection methods for automatic filtering.
result Automatic load estimation is accurate with 90% estimates within a 10% error margin.
DTOR explains anomalies with rule-based explanations.
problem Need to explain anomalies in data effectively.
method Applies Decision Tree Regressor to estimate anomaly scores and generate rule-based explanations.
result DTOR produces robust and consistent rule-based explanations.
ReRe detects anomalies in real-time for time series data.
problem Real-time anomaly detection for time series data requires human intervention or domain knowledge and high computation complexity.
method ReRe uses two lightweight LSTM models to predict and determine anomalies based on historical data and adaptive thresholds.
result ReRe detects anomalies in real-time without requiring human intervention or domain knowledge.
PIF detects anomalies in structured patterns using preference embedding.
problem Detecting anomalies with respect to structured patterns.
method PIF combines adaptive isolation methods with preference embedding to compute anomaly scores using a tree-based method, PI-Forest.
result PIF outperforms state-of-the-art techniques in anomaly detection.
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.
Metric anomalies arising from a distribution of point defects (intrinsic interstitials, vacancies, point stacking faults), thermal deformation, biological growth, etc. are well known sources of material inhomogeneity and internal stress. By emphasizing the geometric nature of such anomalies we seek their representation…
The random cluster model is used to define an upper bound on a distance measure as a function of the number of data points to be classified and the expected value of the number of classes to form in a hybrid K-means and regression classification methodology, with the intent of detecting anomalies. Conditions are given …
We present five methods to the problem of network anomaly detection. These methods cover most of the common techniques in the anomaly detection field, including Statistical Hypothesis Tests (SHT), Support Vector Machines (SVM) and clustering analysis. We evaluate all methods in a simulated network that consists of nomi…
Graph-based methods for anomaly detection and semi-supervised learning.
problem Detecting unusual clinical actions and anomalies in hospital data.
method Label propagation, harmonic solution, regularization, graph connectivity analysis.
result Effective anomaly detection and semi-supervised learning methods for healthcare data.
We propose a non-parametric anomaly detection algorithm for high dimensional data. We first rank scores derived from nearest neighbor graphs on n-point nominal training data. We then train limited complexity models to imitate these scores based on the max-margin learning-to-rank framework. A test-point is declared as…
This paper tackles the 'Clever Hans' effect in anomaly detection models.
problem The 'Clever Hans' effect undermines the generalization capability of anomaly detection models.
method An explainable AI procedure to highlight relevant features used by anomaly detection models.
result The Clever Hans effect is widespread in anomaly detection and occurs in many forms.
OneFlow detects anomalies by finding a minimal volume region, outperforming other methods.
problem Anomaly detection in data with complex outlier structures.
method Flow-based one-class classifier that uses a minimal volume region to define outliers.
result OneFlow outperforms other methods in real-world anomaly detection tasks.
Hashing detects anomalies in structured data efficiently.
problem Identifying non-conforming samples on low-dimensional manifolds.
method Locality Sensitive Hashing in Preference Space.
result State-of-the-art performance at lower computational cost.
TAnoGan detects anomalies in time series data using GANs.
problem Anomaly detection in time series data.
method Generative Adversarial Networks (GAN) for unsupervised anomaly detection.
result TAnoGan outperforms traditional and neural network models in anomaly detection.
A novel one-class classifier fusion method for robust anomaly detection.
problem Fundamental challenges in ensemble-based anomaly detection.
method Locally adaptive learning with dynamic ℓp-norm constraints and interior-point optimization.
result Significantly improved computational efficiency and superior performance across diverse anomaly types.
ACE explains security anomaly detection models through feature contributions.
problem Understanding which features contribute to security anomalies.
method Regression framework to locally approximate anomaly scores.
result Identifies correct contributing features in synthetic data and real data.
A new framework detects anomalies in structured data.
problem Detecting anomalies in samples not conforming to low-dimensional manifolds.
method Preference Isolation Forest (PIF) framework combining adaptive isolation methods and preference embedding.
result Anomalies identified as isolated points in a high-dimensional preference space.
Paper introduces an unsupervised tensor-based anomaly detection method for spatiotemporal data.
problem Challenges in detecting anomalies in spatiotemporal data, especially in urban traffic monitoring and medical imaging.
method Formulates anomaly detection as a regularized robust low-rank + sparse tensor decomposition, incorporating spatiotemporal smoothness and local dependencies.
result Demonstrates improved anomaly detection performance on both synthetic and real data.
Study examines how discretization affects anomaly detection in datasets.
problem Detecting six types of anomalies in datasets using different discretization methods.
method Conducted experiments with SECODA, a general-purpose algorithm for unsupervised anomaly detection.
result Different discretization methods favor the discovery of certain anomaly types.
TPA-AD detects axle-box bearing anomalies using pseudo anomalies near normal boundaries.
problem Detecting axle-box bearing anomalies with only normal training data.
method Two-stage approach: pseudo anomalies, contrastive learning, KNN.
result Improves anomaly detection separability and sensitivity to degradation.
TimeInf estimates data contribution in time series data, improving model performance and anomaly detection.
problem Estimating data contribution in time series datasets with temporal dependencies.
method Model-agnostic data contribution estimation method using influence scores.
result TimeInf effectively detects time series anomalies and outperforms existing methods.
RAID algorithm detects anomalies in real-time IoT systems.
problem Anomaly detection limitations in multivariate dynamic processes.
method Adapts to non-stationary effects and handles data drift.
result Improved detection accuracy and root cause isolation.
Autoencoders misidentify anomalies due to data topology.
problem Autoencoders fail to accurately identify anomalies in data with nontrivial topology.
method Illustrative low-dimensional examples and analysis of autoencoder behavior in latent space.
result Topology of the dataset affects autoencoder performance, leading to misidentification of anomalies.
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 flow studied on flat and non-flat nilmanifolds.
problem Analyzing the Anomaly flow on nilmanifolds.
method Examined with respect to Hermitian connections, focusing on flat and non-flat cases.
result General solutions and qualitative behavior of the Anomaly flow on nilmanifolds.
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.
In gauge theory, the Faddeev-Mickelsson-Shatashvili anomaly arises as a prolongation problem for the action of the gauge group on a bundle of projective Fock spaces. In this paper, we study this anomaly from the point of view of bundle gerbes and give several equivalent descriptions of the obstruction. These include li…
Paper proposes RAN for better anomaly detection in time series data.
problem Anomaly detection algorithms often fail to accurately detect anomalies due to incomplete reconstruction of anomaly data.
method RAN uses adversarial learning and latent vector-constrained Autoencoder to ensure consistent reconstruction of anomaly data.
result RAN outperforms other algorithms in detecting meaningful anomalies with higher AUC-ROC scores.
Study G2-flows reducing to complex geometry flows, focusing on G2-anomaly and G2-Laplacian coflow.
problem Investigate flows of G2-structures in relation to complex geometry. method Analyze G2-Laplacian coflow and G2-anomaly flow, compare their properties. result Compare G2-anomaly flow to G2-Laplacian coflow, investigate short-time existence and fixed points. OracleAD detects multivariate time series anomalies without labels.
problem Rare and unlabeled multivariate time series anomalies.
method OracleAD encodes past sequences into causal embeddings, projects them into a latent space, and identifies anomalies based on deviations from a stable latent structure.
result OracleAD achieves state-of-the-art results and is interpretable.
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.
STRIC detects anomalies in time series by analyzing residual signals.
problem Anomaly detection in multivariate time series data.
method End-to-end differentiable neural network architecture with Sequential Probability Ratio Test on residuals.
result STRIC outperforms state-of-the-art methods on multiple benchmarks.
Time series data is ubiquitous in the real-world problems across various domains including healthcare, social media, and crime surveillance. Detecting anomalies, or irregular and rare events, in time series data, can enable us to find abnormal events in any natural phenomena, which may require special treatment. Moreov…
Improves interpretability of anomaly scores in GBRBM-based detection.
problem Difficulty in setting a proper threshold for anomaly scores.
method Proposes a measure based on cumulative distribution and uses simulated annealing for evaluation.
result Established a guideline for setting the threshold using the interpretable measure.
Anomaly detection algorithms are often thought to be limited because they don't facilitate the process of validating results performed by domain experts. In Contrast, deep learning algorithms for anomaly detection, such as autoencoders, point out the outliers, saving experts the time-consuming task of examining normal …
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…