Papers Contextual Anomaly Detection
“Contextual Anomaly Detection” 태그가 달린 논문 13편 · 필터 해제
Uncovering Issues in the Radio Access Network by Looking at the Neighbors
Mobile network operators (MNOs) manage Radio Access Networks (RANs) with massive amounts of cells over multiple radio generations (2G-5G). To handle such complexity, operations teams rely on monitoring systems, including…
Anomaly DetectionContextual Anomaly DetectionDiscovering Antagonists in Networks of Systems: Robot Deployment
A contextual anomaly detection method is proposed and applied to the physical motions of a robot swarm executing a coverage task. Using simulations of a swarm's normal behavior, a normalizing flow is trained to predict t…
Anomaly DetectionContextual Anomaly DetectionExploring the impact of Optimised Hyperparameters on Bi-LSTM-based Contextual Anomaly Detector
The exponential growth in the usage of Internet of Things in daily life has caused immense increase in the generation of time series data. Smart homes is one such domain where bulk of data is being generated and anomaly …
Anomaly DetectionContextual Anomaly DetectionDimensionality reduction techniques to support insider trading detection
Identification of market abuse is an extremely complicated activity that requires the analysis of large and complex datasets. We propose an unsupervised machine learning method for contextual anomaly detection, which all…
Anomaly DetectionContextual Anomaly DetectionDimensionality ReductionPositionDetecting Contextual Network Anomalies with Graph Neural Networks
Detecting anomalies on network traffic is a complex task due to the massive amount of traffic flows in today's networks, as well as the highly-dynamic nature of traffic over time. In this paper, we propose the use of Gra…
Anomaly DetectionContextual Anomaly DetectionExplainable Contextual Anomaly Detection using Quantile Regression Forests
Traditional anomaly detection methods aim to identify objects that deviate from most other objects by treating all features equally. In contrast, contextual anomaly detection methods aim to detect objects that deviate fr…
Anomaly DetectionContextual Anomaly Detectionquantile regressionregressionNeural Contextual Anomaly Detection for Time Series
We introduce Neural Contextual Anomaly Detection (NCAD), a framework for anomaly detection on time series that scales seamlessly from the unsupervised to supervised setting, and is applicable to both univariate and multi…
Anomaly DetectionContextual Anomaly DetectionRepresentation LearningTime Series+1Context-Dependent Anomaly Detection for Low Altitude Traffic Surveillance
The detection of contextual anomalies is a challenging task for surveillance since an observation can be considered anomalous or normal in a specific environmental context. An unmanned aerial vehicle (UAV) can utilize it…
Anomaly DetectionContextual Anomaly DetectionWisdom of the Contexts: Active Ensemble Learning for Contextual Anomaly Detection
In contextual anomaly detection, an object is only considered anomalous within a specific context. Most existing methods for CAD use a single context based on a set of user-specified contextual features. However, identif…
Active LearningAnomaly DetectionContextual Anomaly DetectionEnsemble LearningA Causal-based Framework for Multimodal Multivariate Time Series Validation Enhanced by Unsupervised Deep Learning as an Enabler for Industry 4.0
An advanced conceptual validation framework for multimodal multivariate time series defines a multi-level contextual anomaly detection ranging from an univariate context definition, to a multimodal abstract context repre…
Anomaly DetectionCausal DiscoveryContextual Anomaly DetectionRepresentation Learning+2Self-Attentive, Multi-Context One-Class Classification for Unsupervised Anomaly Detection on Text
There exist few text-specific methods for unsupervised anomaly detection, and for those that do exist, none utilize pre-trained models for distributed vector representations of words. In this paper we introduce a new ano…
Anomaly DetectionContextual Anomaly DetectionGeneral ClassificationOne-Class Classification+2Unsupervised Contextual Anomaly Detection using Joint Deep Variational Generative Models
A method for unsupervised contextual anomaly detection is proposed using a cross-linked pair of Variational Auto-Encoders for assigning a normality score to an observation. The method enables a distinct separation of con…
Anomaly DetectionContextual Anomaly DetectionUnsupervised Anomaly DetectionUnsupervised Contextual Anomaly DetectionTwitch Plays Pokemon, Machine Learns Twitch: Unsupervised Context-Aware Anomaly Detection for Identifying Trolls in Streaming Data
With the increasing importance of online communities, discussion forums, and customer reviews, Internet "trolls" have proliferated thereby making it difficult for information seekers to find relevant and correct informat…
Anomaly DetectionClusteringContextual Anomaly DetectionFAD