Deep Learning for Cybersecurity: Advancing Intrusion Detection Systems with Neural Network Architectures

Abstract

In the face of escalating cybersecurity threats, enhancing the effectiveness of Intrusion Detection Systems (IDS) is indispensable. With the speed of network communications increasing daily and the growing complexity of attacks, the necessity to protect computers from these cybersecurity threats grows exponentially. Because new attacks are released daily, it is imperative to be able to identify these new types of attacks as quickly as possible. Traditional IDS are based on the premise that if an attack has been studied for long enough, it can be identified through these well-known patterns or signatures. However, some of these new, previously unseen types of attacks cannot be detected by these traditional IDS. To address these shortcomings, Deep Learning techniques can be applied to attain the goal of true cybersecurity and to help limit the amount of damage that cyber attacks can perform.

Deep Learning has been examined considerably as a machine learning technique to classify and predict computer-based data. Deep Learning’s benefit is that by combining more than five layers of machine learning, Deep Learning has shown a rise in accuracy and superior results in the classification and prediction of computer-based data. The primary goal of this essay is to examine how Deep Learning can be utilized to improve IDS involving the use of neural network architectures. The research specifically aims to complete a literature review on recent Deep Learning research applied to IDS and to assess how these can be used to improve current IDS. Additionally, there is an objective to compare and understand the different types of Deep Learning neural networks that have been used in IDS, and to apprehend how and why they can improve IDS over traditional IDS. A second objective is to provide recommendations and to speculate on the future of IDS by utilizing Deep Learning, and then distribution will be done on how this research can enable a new way forward in improving IDS to appropriately tackle the ever-escalating threat landscape of the increasingly interconnected world