Netinfo Security ›› 2018, Vol. 18 ›› Issue (5): 1-11.doi: 10.3969/j.issn.1671-1122.2018.05.001

• Orginal Article •     Next Articles

Comparative Study of Intrusion Detection Methods Based on Machine Learning

Xiang HE1, Sheng LIU1(), Jiguo JIANG2   

  1. 1. College of Information and Communication, National University of Defense Technology, Wuhan Hubei 430000, China
    2. Public Security Department of Shandong Province, Jinan Shandong 250001, China
  • Received:2018-03-10 Online:2018-05-15 Published:2020-05-11

Abstract:

With the network security situation becomes more and more severe, intrusion detection technology has already become an important means to ensure network security. Therefore, it has become a consensus to introduce the theory and method of machine learning to intrusion detection. In recent years, considerable progress has been made in this field. The article analyzes the application of different machine learning methods in intrusion detection. First of all, The article introduces the general process of machine learning and compares and analyzes the theories of typical machine learning methods. Then the article uses different machine learning methods for simulation study to observe the performance changes. Finally, the article carries out the horizontal comparison of different models on the basis of simulation. Based on the simulation experiments, the article draws a more reliable conclusion, which is of great significances to search for a machine learning algorithm which has better performances.

Key words: intrusion detection, machine learning, decision tree, support vector machine, neural network

CLC Number: