Progress in outlier detection techniques: A survey
Detecting outliers is a significant problem that has been studied in various research and
application areas. Researchers continue to design robust schemes to provide solutions to …
application areas. Researchers continue to design robust schemes to provide solutions to …
Pyod: A python toolbox for scalable outlier detection
PyOD is an open-source Python toolbox for performing scalable outlier detection on
multivariate data. Uniquely, it provides access to a wide range of outlier detection …
multivariate data. Uniquely, it provides access to a wide range of outlier detection …
Outlier detection using AI: a survey
MNK Sikder, FA Batarseh - AI Assurance, 2023 - Elsevier
An outlier is an event or observation that is defined as an unusual activity, intrusion, or a
suspicious data point that lies at an irregular distance from a population. The definition of an …
suspicious data point that lies at an irregular distance from a population. The definition of an …
Dynamic relationship identification for abnormality detection on financial time series
In this paper, we propose a novel strategy that identifies the dynamic relationship pattern for
abnormality detection on financial time series. In particular, we select the basis indices that …
abnormality detection on financial time series. In particular, we select the basis indices that …
Machine learning-based anomaly detection for particle accelerators
D Marcato, G Arena, D Bortolato… - … IEEE Conference on …, 2021 - ieeexplore.ieee.org
Particle accelerators are complex systems composed of multiple subsystems that must work
together to produce high quality beams employed for physics experiments. A fault or an …
together to produce high quality beams employed for physics experiments. A fault or an …
Multiworking Conditions Anomaly Detection of Mechanical System Based on Conditional Variational Auto‐Encoder
W Lei, C Li, X Dong, J Wang, H Liu - Shock and Vibration, 2023 - Wiley Online Library
Existing anomaly detection models of mechanical systems often face challenges for the
equipment under multiple working conditions: the learning model under a single working …
equipment under multiple working conditions: the learning model under a single working …
CSCAD: Correlation structure-based collective anomaly detection in complex system
Detecting anomalies in large complex systems is a critical and challenging task. The
difficulties arise from several aspects. First, collecting ground truth labels or prior knowledge …
difficulties arise from several aspects. First, collecting ground truth labels or prior knowledge …
Comparative Analysis of Anomaly Detection Approaches in Firewall Logs: Integrating Light-Weight Synthesis of Security Logs and Artificially Generated Attack …
Detecting anomalies in large networks is a major challenge. Nowadays, many studies rely
on machine learning techniques to solve this problem. However, much of this research …
on machine learning techniques to solve this problem. However, much of this research …
Developing Novel Activation Functions in Time Series Anomaly Detection with LSTM Autoencoder
MA Mercioni, S Holban - 2021 IEEE 15th International …, 2021 - ieeexplore.ieee.org
Our proposal consists of developing two novel activation functions in time series anomaly
detection, they have the capability to reduce the validation loss. The approach is based on a …
detection, they have the capability to reduce the validation loss. The approach is based on a …
Robust anomaly detection using reconstructive adversarial network
Detecting abnormal service performance is significant for Internet-based service
management and operation. Recent advances in anomaly detection methods prefer …
management and operation. Recent advances in anomaly detection methods prefer …