Anomaly Detection

Anomaly detection focuses on identifying unusual patterns or deviations from expected behavior within data, aiming to improve system reliability and safety across diverse applications. Current research emphasizes unsupervised and self-supervised learning approaches, employing architectures like autoencoders, transformers, and graph neural networks, often incorporating techniques such as Bayesian inference and metric learning to enhance robustness and interpretability. The field's significance stems from its broad applicability, ranging from fraud detection and medical diagnosis to industrial process monitoring and network security, with ongoing efforts to develop more efficient, accurate, and explainable methods.

Papers

April 25, 2022

Topological Data Analysis for Anomaly Detection in Host-Based Logs
Thomas Davies
Anomaly Detection Topological Data Analysis Attack Dataset Host Based

April 24, 2022

A Survey on Unsupervised Anomaly Detection Algorithms for Industrial Images
Yajie Cui, Zhaoxiang Liu, Shiguo Lian
Timely Survey Anomaly Detection Unsupervised Anomaly Detection Industrial Anomaly Detection Surface Defect Detection Anomaly Detection Algorithm Industrial Image

April 23, 2022

Discriminative Feature Learning Framework with Gradient Preference for Anomaly Detection
Muhao Xu, Xueying Zhou, Xizhan Gao, WeiKai He, Sijie Niu
Anomaly Detection Discriminative Feature Anomaly Localization Medical Anomaly Detection

April 22, 2022

NLP Based Anomaly Detection for Categorical Time Series
Matthew Horak, Sowmya Chandrasekaran, Giovanni Tobar
Natural Language Processing Anomaly Detection NLP Field Unlabeled Anomaly Categorical Data

April 21, 2022

April 20, 2022

Representation Learning for Content-Sensitive Anomaly Detection in Industrial Networks
Fabian Kopp
Anomaly Detection Representation Learning Supervised Autoencoder Network Intrusion Detection Industrial Internet

April 19, 2022

April 17, 2022

April 14, 2022

LSTM-Autoencoder based Anomaly Detection for Indoor Air Quality Time Series Data
Yuanyuan Wei, Julian Jang-Jaccard, Wen Xu, Fariza Sabrina, Seyit Camtepe, Mikael Boulic
Anomaly Detection Supervised Autoencoder LSTM Network Air Quality LSTM Cell

April 7, 2022

Adversarial Machine Learning Attacks Against Video Anomaly Detection Systems
Furkan Mumcu, Keval Doshi, Yasin Yilmaz
Adversarial Attack Anomaly Detection Video Anomaly Detection Video Surveillance Adversarial Machine Learning

April 5, 2022

Learning to Adapt to Domain Shifts with Few-shot Samples in Anomalous Sound Detection
Bingqing Chen, Luca Bondi, Samarjit Das
Deep Learning LeArning Abstract Anomaly Detection Meta Learning Domain Shift Classification Task Anomalous Sound Detection Machine Condition Monitoring Shot Sample

April 4, 2022

Anomaly Detection

Papers

Topological Data Analysis for Anomaly Detection in Host-Based Logs

A Survey on Unsupervised Anomaly Detection Algorithms for Industrial Images

Discriminative Feature Learning Framework with Gradient Preference for Anomaly Detection

NLP Based Anomaly Detection for Categorical Time Series

Feature anomaly detection system (FADS) for intelligent manufacturing

A Revealing Large-Scale Evaluation of Unsupervised Anomaly Detection Algorithms

Representation Learning for Content-Sensitive Anomaly Detection in Industrial Networks

Sintel: A Machine Learning Framework to Extract Insights from Signals

"Flux+Mutability": A Conditional Generative Approach to One-Class Classification and Anomaly Detection

Anomaly Detection in Autonomous Driving: A Survey

AFSC: Adaptive Fourier Space Compression for Anomaly Detection

LSTM-Autoencoder based Anomaly Detection for Indoor Air Quality Time Series Data

Unsupervised Anomaly Detection in 3D Brain MRI using Deep Learning with impured training data

Unsupervised Anomaly and Change Detection with Multivariate Gaussianization

Self-Supervised Losses for One-Class Textual Anomaly Detection

Adversarial Machine Learning Attacks Against Video Anomaly Detection Systems

Learning to Adapt to Domain Shifts with Few-shot Samples in Anomalous Sound Detection

Do Deep Neural Networks Contribute to Multivariate Time Series Anomaly Detection?

Distributed Anomaly Detection and Estimation over Sensor Networks: Observational-Equivalence and Q-Redundant Observer Design

SAM-kNN Regressor for Online Learning in Water Distribution Networks