A Deep Learning Approach to Network Intrusion Detection Using Deep Autoencoder

Abstract

The security of computer networks is critical for network intrusion detection systems (NIDS). However, concerns exist about the suitability and sustainable development of current approaches in light of modern networks. Such concerns are particularly related to increasing levels of human interaction required and decreased detection accuracy. These concerns are also highlighted. This post presents a modern intrusion prevention deep learning methodology. For unattended function instruction, we clarify our proposed Symmetric Deep Autoencoder (SDAE). Also, we are proposing our latest deep research classification model developed with stacked SDAEs. The classification proposed by the Network Security Laboratory-Knowledge Discovery in Databases (NSL-KDD) and Canadian Institute for Cybersecurity -Intrusion Detection System (CICIDS 2017) data sets was implemented in Tensor Flow, a Graphics Procedure Unit (GPU) enabled and evaluated. We implemented and tested our experiment with different batch sizes using Adam optimizer. Promising findings from our model have been achieved so far, which demonstrates improvements over current solutions and the subsequent improvement for use in advanced NIDS.