Application and Research of Deep Neural Network Model in Computer Network Intrusion Detection

Abstract

With the rapid improvement of network broadband at present and the complexity of network
topology, network intrusion behavior is becoming more and more diverse. The resulting huge
data flow and diversified intrusion alarm data characteristics have become an important factor
that plagued the performance of intrusion detection system. Faced with such huge data flow and
feature information, how to select effective features as standard for intrusion evaluation is a
major challenge problem in intrusion detection field. In intrusion detection based on feature
processing in the past, feature processing is usually based on simple feature selection or
extraction, and the enhancement of the performance of the intrusion detection system is not
obvious. In recent years, the introduction of deep learning technology and its successful
application in many fields have made people pay more and more attention to their excellent
characteristic learning ability. In view of the current predicament of intrusion detection and the
characteristic learning ability of deep learning, this paper proposes a research on intrusion
detection based on deep learning. In this paper, deep neural network is used to train data,
compared with classical BP neural network and SVM algorithm, and it can effectively improve
the accuracy of classification of intrusion detection and recognition, and improve the rate of
network intrusion detection.