Application of deep learning to enhance the accuracy of intrusion detection in modern computer networks

TL;DR

This paper explores how deep learning techniques can significantly improve the accuracy of intrusion detection systems in modern computer networks, focusing on both attack and anomaly detection methods.

Contribution

It introduces a deep learning-based approach that achieves high accuracy, precision, and recall while reducing training time for intrusion detection.

Findings

High accuracy, precision, and recall achieved

Reduced training time compared to traditional methods

Potential to handle zero-day attacks in future work

Abstract

Application of deep learning to enhance the accuracy of intrusion detection in modern computer networks were studied in this paper. The identification of attacks in computer networks is divided in to two categories of intrusion detection and anomaly detection in terms of the information used in the learning phase. Intrusion detection uses both routine traffic and attack traffic. Abnormal detection methods attempt to model the normal behavior of the system, and any incident that violates this model is considered to be a suspicious behavior. For example, if the web server, which is usually passive, tries to There are many addresses that are likely to be infected with the worm. The abnormal diagnostic methods are Statistical models, Secure system approach, Review protocol, Check files, Create White list, Neural Networks, Genetic Algorithm, Vector Machines, decision tree. Our results have demonstrated that our approach offers high levels of accuracy, precision and recall together with reduced training time. In our future work, the first avenue of exploration for improvement will be to assess and extend the capability of our model to handle zero-day attacks.