Intrusion detection in computer systems by using artificial neural networks with Deep Learning approaches

TL;DR

This paper presents a deep learning-based intrusion detection system that combines shallow neural networks with autoencoders, demonstrating improved accuracy and efficiency in identifying cyber threats using real CICIDS2017 data.

Contribution

It introduces a novel deep architecture integrating autoencoders with shallow networks for enhanced intrusion detection performance.

Findings

Deep architecture outperforms shallow networks in accuracy.

System achieves good precision and fast response.

Proven effectiveness using real CICIDS2017 data.

Abstract

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.