Network intrusion detection: systematic evaluation using deep learning

Hackers have always regarded getting information on the health of computer networks to be one of the most significant aspects that they need consider. This may include breaking into databases as well as computer networks that are utilised in defensive systems. As a result, these networks are constantly vulnerable to potentially harmful assaults. This paper provides an assessment technique that is based on a collection of tests, with the goal of measuring the effectiveness of the individual elements of an IDS as well as the influence those components have on the whole system. It evaluates the deep neural network's potential efficacy as a classification for the many kinds of intrusion assaults that may be carried out. Based on the results of the studies, it seems that the level of accuracy achieved by intrusion detection using deep convolutional neural network is satisfactory.