… neural network (OC-NN) model to detect anomalies in complex data sets. OC-NN combines
the ability of deep networks … objective and is thus customized for anomaly detection. This is a …

… This paper presents a novel Neural Network based approach to detect anomaly from
system logs. A CNN-based approach is implemented with different filters for convoluing with …

… can detect anomalies in time series data. This model is based on recurrent neural networks,
… , there are few data labeled as anomalies, since anomalies are hopefully rare events in a …

… only interested in detecting anomalies in patterns, not in the detection of outlier points. Many
existing anomaly detection methods require datasets containing pattern anomalies, which …

… neural network for anomaly detection and try to find out the impact of social network statistical
properties on anomaly detection… which is experimented to detect anomalies. Initially, social …

… This study aims to identify relevant articles on anomaly detection using neural networks.
Therefore we select primary papers based on keywords, search period and, inclusion and …

… More recently, graph neural networks (GNNs) have been adopted to efficiently and intuitively
detect anomalies from graphs due to the highly expressive capability via the message …

… Our results demonstrate that GDN detects anomalies more accurately than baseline … Our
goal is to detect anomalies in testing data, which comes from the same N sensors but over a …

… anomaly-based intrusion detection system. For this research, we … anomaly detection models
based on different deep neural network structures, including convolutional neural networks, …

… This paper describes new process-based intrusion detection approaches that provide the
ability … neural networks (ANNs), and can be used for both anomaly detection in order to detect …