… In contrast, deep learning-based anomaly detection reduces … -based intelligent anomaly
detection framework that can … frames are valuable in capturing anomalous events. We then …

… Secondly, we intend to implement this anomaly detection technique for evaluating anomalies
… algorithm in autoencoders [25] will be a great benefit to anomaly detection applications. …

… This paper presents an anomaly detection approach based on deep … A bidirectional
long-short-term memory (Bi-LSTM) was applied on the UNSW-NB15 dataset to detect the anomalies. …

… anomaly detection, we propose a generative adversarial network based on bidirectional long
shortterm memory (LSTM) … ) model to identify and detect the multidimensional datasets. In …

… This paper presents an anomaly detection process to find outliers observed in the … , bidirectional
long short-term memory (BiLSTM) based autoencoder is used and finds the anomalous …

… outcomes in successive learning and enhance anomaly detection in a network system. …
of a conventional LSTM architecture. Then, we explain in detail the bidirectional LSTM (BiDLSTM…

… multi-layered anomaly detection scheme to train feature extraction and to test anomaly
prediction by using Convolutional Neural Networks (CNNs) layer, Bidirectional and Unidirectional …

… ) and Long Short Term Memory (LSTM) network were used to extract spatial … anomaly
detection algorithms further, we propose an anomaly detection framework based on bidirectional …

… 3) Investing the number of layers and memory elements to improve the Bi-LSTM on the NSL-…
network anomaly detection using a Bi-LSTM-based RNN model that can detect anomalies …

… Autoencoder neural network to detect abnormal CPU data usage [6, 7]. In further works [8–11],
the use of compression autoassociators for outlier detection was studied and in [12], the …