  Anomaly detection is referred to as a process in which the aim is to detect
data points that follow a different pattern from the majority of data points.
Anomaly detection methods suffer from several well-known challenges that hinder
their performance such as high dimensionality. Autoencoders are unsupervised
neural networks that have been used for the purpose of reducing dimensionality
and also detecting network anomalies in large datasets. The performance of
autoencoders debilitates when the training set contains noise and anomalies. In
this paper, a new gradient-reversal method is proposed to overcome the
influence of anomalies on the training phase for the purpose of detecting
network anomalies. The method is different from other approaches as it does not
require an anomaly-free training set and is based on reconstruction error. Once
latent variables are extracted from the network, Local Outlier Factor is used
to separate normal data points from anomalies. A simple pruning approach and
data augmentation is also added to further improve performance. The
experimental results show that the proposed model can outperform other
well-know approaches.
