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.
GUIDE detects anomalies in attributed networks by reconstructing node attributes and higher-order structures.
problem Lack of effective mechanisms for detecting anomalies in complex network interactions.
method GUIDE uses attribute and structure autoencoders, graph attention, and reconstruction errors to identify anomalies.
result GUIDE significantly outperforms state-of-the-art methods on multiple real-world datasets.
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.
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.
New anomaly estimator reduces bias in MLE for normally distributed data.
problem Bias in Maximum Likelihood Estimation of structured anomalies.
method Derive a new anomaly estimator using a mixture model.
result New estimator is asymptotically unbiased regardless of anomaly family size.
ReGEN-TAD detects anomalies in financial time series with interpretable models.
problem Detecting anomalies in complex financial time series with high-dimensional data.
method Integrates machine learning with econometric diagnostics in a refined convolutional--transformer architecture.
result Unified anomaly score without labeled data, robust to structured deviations.
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. DPGIIL clusters structural anomalies using transmissibility functions with deep learning and Dirichlet process.
problem Clustering structural anomalies in high-dimensional streaming data with optimal cluster number determination.
method Combines Dirichlet process and deep generative models for incremental learning and anomaly detection.
result DPGIIL outperforms traditional methods in anomaly detection and clustering.
Unified model for signed networks separates balance and anomaly effects.
problem Ignoring sign information in signed networks leads to inaccurate analysis.
method Low rank plus sparse matrix decomposition with regularized formulation.
result The model accurately detects communities and anomalies in signed networks.
New unsupervised methods for anomaly detection and clustering in structured and streaming data.
problem Anomaly detection and clustering in structured and streaming data.
method Preference Isolation Forest (PIF), Sliding-PIF, MultiLink, Online-iForest, MaxLogit.
result Methods outperform existing techniques on synthetic and real datasets.
iMondrian forest combines isolation forest and Mondrian forest for better anomaly detection.
problem Anomaly detection in batch and online settings.
method Hybrid of isolation forest and Mondrian forest, using depth in Mondrian forest structure.
result iMondrian forest outperforms existing methods in batch and online settings.
TADA detects anomalies in time series using topological data analysis.
problem Detecting global changes in dependency structure between channels in multivariate time series.
method Topological Data Analysis for detecting anomalies in multivariate time series.
result The approach is more suitable for detecting global changes of correlation structures than existing methods.
We develop a supervised machine learning model that detects anomalies in systems in real time. Our model processes unbounded streams of data into time series which then form the basis of a low-latency anomaly detection model. Moreover, we extend our preliminary goal of just anomaly detection to simultaneous anomaly pre…
Anomaly detection on attributed networks aims at finding nodes whose patterns deviate significantly from the majority of reference nodes, which is pervasive in many applications such as network intrusion detection and social spammer detection. However, most existing methods neglect the complex cross-modality interactio…
Study detects anomalies in financial markets using GNN and nonextensive entropy.
problem Detecting anomalies in global financial markets with many correlated assets.
method Used Graph Neural Networks (GNN) with nonextensive entropy to measure uncertainty.
result Anomalies are statistically different for nonextensive entropy parameters before, during, and after a crisis.
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.
In this note we revisit the subject of anomaly cancelation in string theory and M-theory on manifolds with String structure and give three observations. First, that on String manifolds there is no E8 x E8 global anomaly in heterotic string theory. Second, that the description of the anomaly in the phase of the M-theory…
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.
String structures in degree four are associated with cancellation of anomalies of string theory in ten dimensions. Fivebrane structures in degree eight have recently been shown to be associated with cancellation of anomalies associated to the NS5-brane in string theory as well as the M5-brane in M-theory. We introduce …
EHBOS enhances HBOS by capturing feature interactions, improving anomaly detection.
problem Limited ability of HBOS to detect anomalies in datasets with feature interactions.
method Incorporates two-dimensional histograms to capture feature pair dependencies.
result EHBOS outperforms HBOS on datasets with critical feature interactions, achieving notable improvements in ROC AUC.
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.
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.
This work presents a novel modeling and analysis framework for graph sequences which addresses the challenge of detecting and contextualizing anomalies in labelled, streaming graph data. We introduce a generalization of the BTER model of Seshadhri et al. by adding flexibility to community structure, and use this model …
Topological anomaly scores predict return curves in S&P 500 stocks
problem Detecting anomalies in financial time series
method BallMapper, decoder-conditional VAE, Function-on-Function regression
result Anomaly history carries predictive content for return curves
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…
Gaussian graphical models (GGMs) are probabilistic tools of choice for analyzing conditional dependencies between variables in complex systems. Finding changepoints in the structural evolution of a GGM is therefore essential to detecting anomalies in the underlying system modeled by the GGM. In order to detect structur…
In this paper, we attack the anomaly detection problem by directly modeling the data distribution with deep architectures. We propose deep structured energy based models (DSEBMs), where the energy function is the output of a deterministic deep neural network with structure. We develop novel model architectures to integ…
Paper proposes TBSD for efficient anomaly detection in textured images.
problem Challenges in anomaly detection for textured images, especially in manufacturing systems.
method Texture basis integrated smooth decomposition (TBSD) approach.
result TBSD surpasses benchmarks with less misidentification and superior performance.
Method detects anomalies on attributed graphs with few labeled instances.
problem Detecting anomalies on connected instances (attributed graphs) with limited labeled data.
method Embed nodes in latent space using GCNs, training to distinguish normal and anomalous nodes.
result Method outperforms existing methods on real-world attributed graph datasets.
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…
Nowadays, graph-structured data are increasingly used to model complex systems. Meanwhile, detecting anomalies from graph has become a vital research problem of pressing societal concerns. Anomaly detection is an unsupervised learning task of identifying rare data that differ from the majority. As one of the dominant a…
We study the cohomological physics of fivebranes in type II and heterotic string theory. We give an interpretation of the one-loop term in type IIA, which involves the first and second Pontrjagin classes of spacetime, in terms of obstructions to having bundles with certain structure groups. Using a generalization of th…
StrGNN detects anomalies in dynamic graphs by analyzing subgraphs and temporal features.
problem Detecting anomalies in dynamic graphs with structural changes.
method StrGNN is an end-to-end model that uses structural subgraphs and temporal features for anomaly detection.
result StrGNN effectively detects anomalies in dynamic graphs, as shown by extensive experiments.
Study examines how different time series cross-validation methods affect anomaly detection in multivariate time series.
problem Evaluating anomaly detection in multivariate time series requires preserving temporal dependencies, especially for subsequence anomalies.
method Systematically investigates walk-forward and sliding window methods across various validation configurations and classifier types.
result Sliding window method consistently yields higher precision-recall scores and reduced fold-to-fold performance variance, particularly for deep learning models.
GBOC detects anomalies in time series data using granular-ball vectors.
problem Challenges in modeling normal behavior in dynamic, nonlinear time series data.
method Granular-ball Vector Data Description (GVDD) and Granular-ball One-Class Network (GBOC).
result GBOC improves anomaly detection in time series data.
Dissertation tackles zero-shot anomaly detection, focusing on consistent anomalies and proposing CoDeGraph framework.
problem Consistent anomalies bias distance-based zero-shot anomaly detection methods.
method Formalized consistent anomalies, identified similarity scaling and neighbor-burnout phenomena, introduced CoDeGraph framework.
result CoDeGraph effectively suppresses consistent anomalies in zero-shot anomaly detection.
Deep AD on images outperforms traditional methods.
problem Traditional AD methods struggle with unsupervised learning due to lack of labeled data.
method Used deep learning on ImageNet to discern between normal and a few random natural images.
result Deep AD classifiers trained on a few random images outperform current state-of-the-art methods.
We develop a general regulated volume expansion for the volume of a manifold with boundary whose measure is suitably singular along a separating hypersurface. The expansion is shown to have a regulator independent anomaly term and a renormalized volume term given by the primitive of an associated anomaly operator. Thes…
Swift Hydra uses RL and generative AI to improve anomaly detection.
problem Generalization to unseen anomalies in critical systems.
method Generative AI and reinforcement learning (RL) for synthesizing diverse anomaly samples.
result Swift Hydra outperforms state-of-the-art models on ADBench benchmark.
New method detects anomalies in systems influenced by their environment.
problem Detecting anomalies in systems under environmental influence.
method Adversarial learning and time series representation learning.
result Successfully addresses label sparsity and subjectivity in anomaly detection.
Paper proposes a method to generate synthetic anomalies for robust anomaly detection.
problem Anomaly detection struggles with unbalanced data and rare anomalies.
method Two-level hierarchical latent space representation for feature distillation and synthesis.
result The method creates robust synthetic anomalies for training robust binary classifiers.
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…
Survey of deep learning methods for medical anomaly detection.
problem Medical anomaly detection using machine learning.
method Thorough review of deep learning techniques across various medical domains.
result Comparison and contrast of deep learning models and their limitations.
Paper tackles anomaly detection with missing causal knowledge.
problem Detect anomalies with missing structural knowledge.
method Simple, efficient methods for polytree causal graphs.
result Heuristic identifies root causes based on anomaly scores.
Graph regularized autoencoder improves anomaly detection performance.
problem Unsupervised anomaly detection in high-dimensional data.
method Developed a graph regularized autoencoder using MST-based distances.
result Outperforms alternative methods on 20 benchmark anomaly detection datasets.
This paper uses β-VAE for unsupervised anomaly detection in NSL-KDD.
problem Unsupervised anomaly detection in network traffic.
method β-VAE with latent space structure and reconstruction error.
result Latent space exploitation is more effective for classification tasks.
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.
Extract anomalies from 5D SCFTs using extra-dimensional η-invariants.
problem Anomalies in quantum field theories.
method Use extra-dimensional η-invariants to bypass traditional blowup techniques.
result Anomalies can be determined directly from η-invariants of asymptotic boundaries.