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.
Enhances anomaly detection in financial markets using AI agents.
problem Manual verification of financial market anomalies is time-consuming and error-prone.
method A multi-agent LLM framework for automated anomaly detection.
result Framework reduces human intervention and improves efficiency and accuracy.
Representing maritime traffic patterns and detecting anomalies from them are key to vessel monitoring and maritime situational awareness. We propose a novel approach -- referred to as GeoTrackNet -- for maritime anomaly detection from AIS data streams. Our model exploits state-of-the-art neural network schemes to learn…
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.
Paper uses DBSCAN variation to detect ship anomalies.
problem Detecting anomalous ship behavior.
method Variation of DBSCAN algorithm applied to AIS data.
result Alternative anomaly metric is more statistically informative.
New AI method improves anomaly detection across different IIoT sensors.
problem Poor performance of anomaly detection models when applied to different machines.
method Robust AI method using pre-processing and multiple models on different pumps.
result Models perform well across different environments and types of pumps.
Corner cases are the main bottlenecks when applying Artificial Intelligence (AI) systems to safety-critical applications. An AI system should be intelligent enough to detect such situations so that system developers can prepare for subsequent planning. In this paper, we propose semi-supervised anomaly detection conside…
TimeVQVAE-AD detects anomalies in time series data with high accuracy and provides explainable results.
problem Detecting and explaining anomalies in time series data accurately.
method Masked latent generative modeling in time-frequency domain.
result TimeVQVAE-AD outperforms existing methods in anomaly detection and explainability.
This paper discusses challenges and opportunities in vessel behavior detection using machine and deep learning.
problem Real-time analysis of vessel behaviors is crucial for maritime safety and protection.
method Comparison of classical machine learning and deep learning approaches for vessel event and anomaly detection.
result Novel methods and tools are needed to address challenges in vessel behavior detection.
AI model enhances grid monitoring with synchro-waveform tech.
problem Dynamic, stochastic, low-inertia future grids need advanced monitoring.
method AI Foundation Model with synchro-waveform tech.
result Significantly improved fault detection accuracy and speed.
Unified AI system for data quality control and governance in regulated environments.
problem Isolated data quality control steps in existing systems.
method AI-driven framework integrating rule-based, statistical, and AI methods.
result Empirical gains in anomaly detection, reduced manual remediation, improved auditability.
Proposes manifold-based unsupervised anomaly detection for visual data.
problem Rare anomalies in unlabeled data.
method Constant curvature manifolds, hyperspherical Variational Auto-Encoders (VAE) with gyroplane layer.
result State-of-the-art results on visual anomaly benchmarks and histopathology.
AI enhances refinery optimization by detecting data errors and improving decision-making.
problem Interpreting and applying LP solutions for refinery optimization is challenging due to simplifications and data errors.
method Transformed ECOD methodology, Anomaly Detection tools, and high-dimensional data analysis.
result Identifies data supply errors and reveals business opportunities in refinery scheduling and planning.
In a world of global trading, maritime safety, security and efficiency are crucial issues. We propose a multi-task deep learning framework for vessel monitoring using Automatic Identification System (AIS) data streams. We combine recurrent neural networks with latent variable modeling and an embedding of AIS messages t…
MIAEAD detects anomalies in mixed data types.
problem Challenges of heterogeneity in feature subsets for anomaly detection.
method Multiple-Input Variational Auto-Encoder (MIVAE) for simultaneous feature subset anomaly scoring.
result MIVAE outperforms conventional methods and state-of-the-art unsupervised models in AUC score.
Physics-informed diffusion model detects anomalous trajectories in GPS data.
problem Detecting fake GPS trajectories in international waters.
method Physics-informed diffusion model integrating kinematic constraints.
result Higher prediction accuracy and lower error rate for anomaly detection.
Big data transforms accounting and auditing, enhancing insights but posing challenges.
problem Challenges in data privacy and security with increased data sources.
method Utilizing AI and machine learning for efficient data analysis and anomaly detection.
result Enhanced analytics tools and continuous learning are key to overcoming challenges.
Improves anomaly detection with contaminated unlabeled data.
problem Weakness in existing semi-supervised anomaly detection methods when unlabeled data contain anomalies.
method Integrates positive-unlabeled learning with deep anomaly detection models.
result Achieves better detection performance on various datasets.
PAC-Wrap provides provable guarantees for semi-supervised anomaly detection.
problem Ensuring reliable anomaly detection in safety-critical applications.
method PAC-Wrap wraps around existing anomaly detection methods to provide PAC guarantees.
result PAC-Wrap effectively provides rigorous guarantees for various anomaly detectors.
Study identifies high-density anomalies in normal data regions.
problem Detecting anomalies in normal data regions.
method Introduces non-parametric algorithmic frameworks for unsupervised detection.
result IPP framework yields the best detection results.
We propose a supervised anomaly detection method for data with inexact anomaly labels, where each label, which is assigned to a set of instances, indicates that at least one instance in the set is anomalous. Although many anomaly detection methods have been proposed, they cannot handle inexact anomaly labels. To measur…
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.
Deep learning improves anomaly detection across various fields.
problem Detecting anomalies in data with advanced approaches.
method Survey of deep learning methods for anomaly detection.
result Advancements in deep anomaly detection address unique challenges.
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.
Ensemble learning improves anomaly detection for milder symptoms.
problem Difficulty in detecting incipient anomalies due to similarity to normal conditions.
method Utilize uncertainty information from ensemble learning to identify misclassified incipient anomalies.
result Ensemble learning methods show improved performance on incipient anomaly detection.
Survey on LSTM-based anomaly detection for technical systems.
problem Detect anomalies in technical systems due to complex dynamics.
method Use LSTM networks and other AI techniques to detect anomalies considering temporal and contextual characteristics.
result Demonstrates the potential of LSTM networks and graph-based approaches for anomaly detection.
Survey evaluates 20 anomaly detection methods for time-series data.
problem Comparing statistical, machine learning, and deep learning methods for anomaly detection.
method Comprehensive evaluation of 20 methods from three categories on public datasets.
result Provides insights into the performance and suitability of anomaly detection methods.
VSCOUT detects anomalies in high-dimensional data using a hybrid VAE approach.
problem Challenges in classical SPC for high-dimensional, non-Gaussian data.
method Hybrid VAE architecture with ARD prior, ensemble filtering, and changepoint detection.
result VSCOUT achieves superior sensitivity to special-cause structure and controlled false alarms.
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.
RESHAPE explains financial statement anomalies by aggregating explanations from AENNs.
problem Detecting and explaining accounting anomalies in financial audits is challenging.
method Proposes RESHAPE to explain model output on an aggregated attribute-level.
result RESHAPE provides more comprehensible explanations compared to existing methods.
Ensemble unsupervised anomaly detection using IRT for hidden ground truth.
problem Challenges in constructing an ensemble from unsupervised anomaly detection methods.
method Use Item Response Theory to compute latent traits and construct an ensemble that downplays noisy methods.
result Demonstrated effectiveness of IRT ensemble on extensive data repository.
FAMDAD detects anomalies in mixed data using kurtosis-weighted Factor Analysis.
problem Detecting anomalies in high-dimensional mixed data.
method kurtosis-weighted Factor Analysis of Mixed Data (FAMDAD).
result Anomalies are highly separable in the first and last few dimensions of the FAMDAD embedding.
System states that are anomalous from the perspective of a domain expert occur frequently in some anomaly detection problems. The performance of commonly used unsupervised anomaly detection methods may suffer in that setting, because they use frequency as a proxy for anomaly. We propose a novel concept for anomaly dete…
A novel non-supervised method detects anomalies in multivariate time series.
problem Detecting anomalies in multivariate time series data.
method Partitioning based on clustering of correlation coefficients.
result Significant improvement in anomaly detection performance.
Detects systematic anomalies in consumer complaints using NLP.
problem Detecting small, frequent anomalies in consumer complaints.
method NLP conversion of narratives, followed by anomaly detection algorithm.
result Demonstrates effectiveness of NLP for detecting systematic anomalies.
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.
A new method assigns anomaly scores to features for better interpretation.
problem Interpreting anomaly scores from feature attributions.
method Proposes a characteristic function to attribute anomaly scores using Shapley value.
result Demonstrates the potential utility of the proposed attribution methods.
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…
LogAnMeta detects anomalies from log events using meta learning.
problem Poor performance of current log anomaly detection on new or unseen anomalies.
method Meta-learning-based hybrid few-shot classifier trained in an episodic manner.
result Demonstrates efficacy of LogAnMeta on detecting anomalies with few samples.
Anomaly detection aims to detect abnormal events by a model of normality. It plays an important role in many domains such as network intrusion detection, criminal activity identity and so on. With the rapidly growing size of accessible training data and high computation capacities, deep learning based anomaly detection…
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.
Proposes a fair deep anomaly detection method.
problem Ensuring fairness in deep anomaly detection.
method Adversarial network to de-correlate sensitive attributes and learned representations.
result Our approach largely removes unfairness with minimal anomaly detection performance loss.
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.
Although deep learning has been applied to successfully address many data mining problems, relatively limited work has been done on deep learning for anomaly detection. Existing deep anomaly detection methods, which focus on learning new feature representations to enable downstream anomaly detection methods, perform in…
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.
pAElla detects malware in DCs/SCs with high accuracy.
problem Real-time malware detection in high-resolution data centers.
method AI-powered edge computing with IoT-based monitoring and Power Spectral Density.
result pAElla achieves F1-score close to 1 with low false alarm and malware miss rates.
ECAD detects anomalies without data exchangeability, improving traffic flow detection.
problem Detecting anomalies in spatio-temporal data with missing values.
method ECAD uses conformal prediction to wrap around any regression algorithm, controlling Type-I error without data exchangeability.
result ECAD outperforms other methods in detecting anomalous traffic flow.
Deep Autoencoder outperforms in anomaly detection for building energy data.
problem Automated detection of faulty data in learning applications.
method Training and comparison of Simple, Deep, and Supervised Deep Autoencoders on ASHRAE building energy dataset.
result Supervised Deep Autoencoder outperforms in total anomalies detected.