An Improved Network Intrusion Detection Based on Deep Neural Network

2019 International Conference on Advanced Electronic Materials, Computers and Materials Engineering

无线电电子学;计算机科学;材料科学

Zhang, Lin^1 ; Li, Meng^1 ; Wang, Xiaoming^1 ; Huang, Yan^1

Chengdu Chengdian Electric Power Engineering Design Co. Ltd, Chengdu

610000, China^1

关键词: Changing environment;    Continuous learning;    Convolutional neural network;    High-accuracy;    In networks;    Local area networks (LAN);    Network attack;    Network intrusion detection;   

Others  :  https://iopscience.iop.org/article/10.1088/1757-899X/563/5/052019/pdf
DOI  :  10.1088/1757-899X/563/5/052019

来源: IOP

【 摘 要 】

Network intrusion detection is of great significance for network security in Local Area Network (LAN). Traditional methods such as firewalls do not completely protect against attacks on the LAN due to lack of continuous learning. Recently, the ability of convolutional neural networks (CNN) to extract features in the field of computer vision has received extensive attention. CNN can automatically extract effective complex features to adapt to constantly changing environments, which is especially important in network intrusion detection. In this paper, we focus on network security in the LAN. We propose an approach based on CNN to implement intrusion detection in LAN. This approach can effectively identify network attacks and has an accuracy of 98.34% on the KDD99 dataset. The experimental results show that the proposed approach based on the CNN has high accuracy in intrusion detection.