A Novel Intrusion Detection Model for Detecting Known and Innovative Cyberattacks Using Convolutional Neural Network
Samson Ho, Saleh Al-Jufout, Khalil Dajani, Mohammad Mozumdar
Abstract
As a tremendous amount of service being streamed online to their users along with massive digital privacy information transmitted in recent years, the internet has become the backbone of most people's everyday workflow. The extending usage of the internet, however, also expands the attack surface for cyberattacks. If no effective protection mechanism is implemented, the internet will only be much vulnerable and this will raise the risk of data getting leaked or hacked. The focus of this paper is to propose an Intrusion Detection System (IDS) based on the Convolutional Neural Network (CNN) to reinforce the security of the internet. The proposed IDS model is aimed at detecting network intrusions by classifying all the packet traffic in the network as benign or malicious classes. The Canadian Institute for Cybersecurity Intrusion Detection System (CICIDS2017) dataset has been used to train and validate the proposed model. The model has been evaluated in terms of the overall accuracy, attack detection rate, false alarm rate, and training overhead. A comparative study of the proposed model's performance against nine other well-known classifiers has been presented.