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.

中文翻译：

---

利用卷积神经网络检测已知创新攻击的新型入侵检测模型

近年来，随着大量的服务在线流传输给用户，同时又传输了大量的数字隐私信息，互联网已成为大多数人日常工作流程的骨干。但是，互联网的广泛使用也扩大了网络攻击的攻击面。如果未实施有效的保护机制，则互联网只会变得非常脆弱，这将增加数据泄漏或被黑客入侵的风险。本文的重点是提出一种基于卷积神经网络（CNN）的入侵检测系统（IDS），以增强Internet的安全性。提出的IDS模型旨在通过将网络中的所有数据包流量分类为良性或恶意类来检测网络入侵。加拿大网络安全研究所入侵检测系统（CICIDS2017）数据集已用于训练和验证所提出的模型。该模型已在总体准确性，攻击检测率，误报率和训练开销方面进行了评估。提出的模型与其他九个知名分类器的性能比较研究。