Intrusion detection is essential for the security of the components
of any network. For that reason, several strategies can be used in
Intrusion Detection Systems (IDS) to identify the increasing attempts to
gain unauthorized access with malicious purposes including those base
on machine learning. Anomaly detection has been applied successfully to
numerous domains and might help to identify unknown attacks. However,
there are existing issues such as high error rates or large dimensionality
of data that make its deployment di cult in real-life scenarios. Representation
learning allows to estimate new latent features of data in a
low-dimensionality space. In this work, anomaly detection is performed
using a previous feature learning stage in order to compare these methods
for the detection of intrusions in network tra c. For that purpose,
four di erent anomaly detection algorithms are applied to recent network
datasets using two di erent feature learning methods such as principal
component analysis and autoencoders. Several evaluation metrics such
as accuracy, F1 score or ROC curves are used for comparing their performance.
The experimental results show an improvement for two of the
anomaly detection methods using autoencoder and no signi cant variations
for the linear feature transformation
