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.
Online-iForest detects anomalies in streaming data efficiently.
problem Offline anomaly detection methods are impractical for streaming contexts.
method Online-iForest tracks evolving data processes in real-time without periodic retraining.
result Online-iForest outperforms all competitors in efficiency.
New rules control false discoveries in online anomaly detection for time series data.
problem Controlling false discoveries in anomaly detection for time series data.
method Novel online false discovery rate control (FDRC) rules for time series anomaly detection.
result Ensures high power in detecting anomalies even when the alternative is rare and test statistics are serially dependent.
Online anomaly detection in surveillance videos with false alarm rate bounds.
problem Lack of theoretical performance analysis and online decision making in anomaly detection.
method Proposes an online anomaly detection method with asymptotic bounds on false alarm rate.
result Demonstrates effectiveness on publicly available data sets, outperforming state-of-the-art algorithms.
Paper proposes an online anomaly detection method for real-time systems.
problem Rare events endanger profitability, safety, and environmental aspects.
method Online inverse cumulative distribution-based approach with dynamic process limits.
result Eliminates common problems of offline anomaly detectors and provides low-latency detection.
Paper proposes SGP-Q for efficient online anomaly detection with concept drift adaptation.
problem Efficient online anomaly detection for time-series data with concept drift.
method Sparse Gaussian processes with Q-function (SGP-Q).
result SGP-Q achieves better anomaly detection results with concept drift adaptation.
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.
Proposes a new model for online anomaly detection in multivariate time series.
problem Inaccurate anomaly detection in multivariate time series due to spurious correlations and lack of temporal causality.
method Clusters channels based on correlations, embeds each cluster, and integrates information through a causal mixer while maintaining temporal causality.
result Consistently superior performance across six public benchmark datasets.
C-PP-COAD detects anomalies with limited real data, reducing dependency on real calibration data.
problem Limited real calibration data for online anomaly detection.
method Context-aware prediction-powered conformal online anomaly detection (C-PP-COAD).
result Significantly reduces dependency on real calibration data without compromising FDR control.
Walmart developed anomaly detection for online pricing systems.
problem Early detection of mis-priced items in online retailers.
method Unsupervised and supervised approaches for real-time and batch anomaly detection.
result The system detected the most important anomalies with high precision.
OML-AD detects anomalies in non-stationary time series data.
problem Anomaly detection in non-stationary time series data.
method Online machine learning for anomaly detection.
result OML-AD outperforms state-of-the-art methods in accuracy and efficiency.
New method tackles anomaly detection in video surveillance using continual learning.
problem Challenges in continual learning for high-dimensional applications like video surveillance.
method Transfer learning and continual learning for online anomaly detection.
result Significantly reduces training complexity and continual learning from recent data.
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.
Real-time anomaly detection for edge streams using MIDAS and MIDAS-F.
problem Detecting unusual behavior in dynamic graph edge streams.
method MIDAS-F focuses on microcluster anomalies, incorporating anomalies into internal states to reduce poisoning effect.
result MIDAS-F outperforms MIDAS in accuracy, with up to 62% higher ROC-AUC.
Bayesian model detects video anomalies.
problem Anomaly detection in video data.
method Dynamic Bayesian nonparametric topic model with Gibbs samplers.
result Dynamic model outperforms static model in anomaly detection.
Study evaluates AD methods for fraud detection in online credit card payments.
problem Fraud detection in online credit card payments using anomaly detection methods.
method Assessed several recent anomaly detection methods and compared them with standard supervised learning methods.
result LightGBM outperforms other methods but is more sensitive to distribution shifts.
A scalable system detects price anomalies in online marketplaces to improve customer experience.
problem Inaccurate prices on online marketplaces lead to poor customer experience and revenue loss.
method MoatPlus uses unsupervised statistical features and an ensemble of models to generate upper price bounds.
result Our approach improves precise anchor coverage by up to 46.6% in high-vulnerability item subsets.
DRAMA detects anomalies in high dimensions via dimensionality reduction.
problem Challenges in anomaly detection for large datasets in high dimensions.
method Dimensionality reduction and unsupervised clustering.
result DRAMA is robust and competitive in high dimensions, especially for online anomaly detection.
The paper proposes a fast anomaly detection method for high-dimensional systems.
problem Real-time detection of anomalies in high-dimensional systems.
method Sequential and multivariate anomaly detection method, data-driven, semi-supervised.
result The method can quickly and accurately detect anomalies, including changes in correlation structure and stealth attacks.
A new model detects anomalies in time series data efficiently.
problem Detect anomalies in high-dimensional time series data.
method r-ssGPFA, an unsupervised online anomaly detection model using state space Gaussian processes.
result The model detects anomalies efficiently and is computationally cheaper.
OneShotSTL efficiently decomposes time series online, improving speed and accuracy.
problem Real-time analysis of time series data with low processing delay.
method Online seasonal-trend decomposition algorithm with O(1) update time complexity.
result 1,000 times faster than batch methods with comparable accuracy.
An unsupervised anomaly detection method for irregularly sampled time-series data.
problem Anomaly detection in irregularly sampled or missing valued time-series data.
method Uses LSTM networks with time modulation gates to extract temporal features and SVDD for anomaly labeling.
result Significantly outperforms standard approaches on real-life datasets.
AKM2D framework speeds up anomaly detection in point-based sensing.
problem Efficient anomaly detection in point-based sensing systems.
method Adaptive Kernelized Maximum-Minimum Distance (AKM2D) framework for intelligent sequential sampling. result Balances exploration and exploitation for accurate anomaly quantification.
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.
Donut detects anomalies in web KPIs without labels.
problem Anomaly detection for seasonal KPIs with varying patterns and data quality.
method Unsupervised anomaly detection via Variational Auto-Encoder (VAE) with key techniques.
result Donut outperforms state-of-the-art approaches, achieving F-scores up to 0.9.
Robust PCA detects anomalies and fills gaps in seasonal time series data.
problem Anomaly detection and data imputation in seasonal time series.
method Online robust PCA framework for temporal observations.
result Empirically compared and showed effectiveness in practical situations.
MSTREAM detects anomalies in multi-aspect data streams.
problem Detecting unusual group anomalies in dynamic multi-aspect data.
method Online streaming multi-aspect anomaly detection framework.
result MSTREAM outperforms state-of-the-art baselines.
Microsoft develops a time-series anomaly detection service.
problem Real-time monitoring of various metrics in applications and services.
method Pipeline consisting of data ingestion, experimentation platform, and online compute; novel algorithm combining Spectral Residual and Convolutional Neural Network.
result Superior experimental results compared to state-of-the-art baselines on public and Microsoft production data.
Paper tackles anomaly detection in e-commerce using Bayesian semi-supervised tensor decomposition.
problem Detecting anomalies in seller-reviewer data in e-commerce.
method Bayesian semi-supervised tensor decomposition with Polya-Gamma data augmentation and partial natural gradient learning.
result Semi-supervised approach outperforms state-of-the-art unsupervised baselines.
Paper tackles anomaly detection in large-scale networks.
problem Inferring network-level anomalies from indirect link measurements.
method Online subspace tracking of Hankelized traffic tensor using Candecomp/PARAFAC decomposition and RLS algorithm for normal flows; outlier detection for abnormal flows.
result Proposed algorithm achieves faster convergence and better anomaly detection performance.
Paper proposes RL for real-time smart grid cyber attack detection.
problem Real-time detection of cyber-attacks in smart grids.
method Formulated as POMDP, uses model-free reinforcement learning.
result Effective in timely and accurate detection of cyber-attacks.
RobustTAD detects anomalies in diverse time series data.
problem Effective anomaly detection for complex time series data.
method Robust seasonal-trend decomposition + CNN architecture with data augmentation.
result RobustTAD outperforms other methods on public datasets.
Anomaly detection for aviation safety using SMS-VAR models.
problem Detecting anomalous flight segments in aviation systems.
method Semi-Markov switching vector autoregressive (SMS-VAR) model for anomaly detection.
result The framework can detect various types of anomalies and key parameters involved.
BADAC combines Bayesian methods for anomaly detection and classification.
problem Statistical uncertainties in machine learning algorithms, especially for anomaly detection.
method Unified hierarchical Bayesian framework that marginalizes over unknown data values.
result BADAC outperforms standard algorithms in classification and anomaly detection with uncertainties.
Paper proposes efficient online data thinning for expert analysis.
problem Large-scale streaming data exceeds human analysis capacity.
method Online anomaly detection using dynamic low-rank Gaussian mixture models.
result Proposed method reduces data to unique elements for timely analysis.
New method detects anomalies in computing centers' logs.
problem Anomaly detection in continuously changing log data for predictive maintenance.
method Evolving granular classifiers using Fuzzy-set-Based evolving Modeling and evolving Granular Neural Network.
result Classification model prioritizes maintenance based on anomaly severity.
Unified approach detects anomalies in evolving categorical data at multiple scales.
problem Anomaly detection in high-dimensional, time-evolving categorical data without labeled samples.
method Combines Adversarial Autoencoder and Recurrent Neural Network for cross-scale learning and anomaly detection.
result Enhanced two-resolution anomaly detector outperforms state-of-the-art methods.
We tackle anomaly detection in sparse time series data.
problem Sparse time series with low signal-to-noise ratios and non-uniform performance.
method We introduce a novel generative procedure for benchmark datasets and demonstrate how anomaly score smoothing improves performance.
result Anomaly score smoothing consistently improves performance in low-count time series anomaly detection.
Paper tackles cross-modal anomalies in multi-source data.
problem Detect anomalies in multi-modal data where patterns are inconsistent across different sources.
method Proposes a deep structured anomaly detection framework.
result Demonstrates effectiveness on real-world datasets.
Security issues are crucial in a number of machine learning applications, especially in scenarios dealing with human activity rather than natural phenomena (e.g., information ranking, spam detection, malware detection, etc.). It is to be expected in such cases that learning algorithms will have to deal with manipulated…
Deep neural network detects anomalies in time series data.
problem Detecting anomalies in diverse time series data.
method End-to-end deep feedforward neural network without feature engineering.
result Effective anomaly detection for various time series types.
Online PaLD extends PaLD for semi-supervised online applications.
problem Scalability of unsupervised clustering algorithms for large datasets.
method Adapted partitioned local depth algorithm for online semi-supervised prediction.
result Online PaLD extends cohesion network to new data points efficiently.
Adaptive algorithms improve cost-sensitive online classification with second-order information.
problem Improving cost-sensitive online classification with second-order information.
method Proposes adaptive regularization algorithms with sketching technique for better trade-off between performance and efficiency.
result Empirically validated algorithms' effectiveness and properties in real-world anomaly detection tasks.
Adaptive anomaly detection for IoT data reduces delay without sacrificing accuracy.
problem Real-time anomaly detection for IoT data in distributed edge computing systems.
method Adaptive anomaly detection approach using contextual bandit and reinforcement learning.
result Significantly reduces detection delay (e.g., 71.4% for univariate data) without sacrificing accuracy.
RPE detects anomalies robustly in time-series data.
problem Detecting anomalies in time-series data efficiently and robustly.
method Window-based, robust projection step, closed-form algorithm.
result RPE can identify anomalies in time-stamp level and outperforms existing methods.
Anomaly-aware forecast improves accuracy for extreme events.
problem Challenges in automatically detecting and learning from extreme events and anomalies in large-scale datasets.
method Proposes an anomaly-aware forecast framework that automatically detects and incorporates anomalies using an attention mechanism and dynamic uncertainty optimization.
result Demonstrated superior accuracy and reduced uncertainty on three datasets with different types of anomalies.
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.
Deep learning detects insider threats in network logs.
problem Detecting insider threats in unstructured cybersecurity data.
method Online unsupervised deep learning approach.
result Anomaly scores help identify insider threats with high accuracy.