An Improved Convolutional Neural Network model for Intrusion Detection in Networks

EasyChair Preprint 638

Abstract

Network intrusion detection is an important component of network security. Currently, the popular detection technology used the traditional machine learning algorithm to train the intrusion samples, so as to obtain the intrusion detection model. However, these algorithms have the disadvantage of low detection rate. Deep learning is more advanced technology that automatically extracts features from samples. In view of the fact that the accuracy of intrusion detection is not high in traditional machine learning technology, this paper proposes a network intrusion detection model based on convolutional neural network algorithm. The model can automatically extract the effective features of intrusion samples, so that the intrusion samples can be accurately classified. Experimental results on KDD99 datasets show that the proposed model can greatly improve the accuracy of intrusion detection.

Keyphrases: CNN, Cyber Security, Intrusion Detection, Network Security

@booklet{EasyChair:638,
  author    = {Riaz Ullah Khan and Zhang Xiaosong and Mamoun Alazab and Rajesh Kumar},
  title     = {An Improved Convolutional Neural Network model for Intrusion Detection in Networks},
  howpublished = {EasyChair Preprint 638},
  year      = {EasyChair, 2018}}