  Intrusion detection into computer networks has become one of the most
important issues in cybersecurity. Attackers keep on researching and coding to
discover new vulnerabilities to penetrate information security system. In
consequence computer systems must be daily upgraded using up-to-date techniques
to keep hackers at bay. This paper focuses on the design and implementation of
an intrusion detection system based on Deep Learning architectures. As a first
step, a shallow network is trained with labelled log-in [into a computer
network] data taken from the Dataset CICIDS2017. The internal behaviour of this
network is carefully tracked and tuned by using plotting and exploring codes
until it reaches a functional peak in intrusion prediction accuracy. As a
second step, an autoencoder, trained with big unlabelled data, is used as a
middle processor which feeds compressed information and abstract representation
to the original shallow network. It is proven that the resultant deep
architecture has a better performance than any version of the shallow network
alone. The resultant functional code scripts, written in MATLAB, represent a
re-trainable system which has been proved using real data, producing good
precision and fast response.
