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Network Intrusion Detection Based on Hidden Markov Model and Conditional Entropy

  • Linying XiaoEmail author
  • Huaibin Wang
Conference paper
Part of the Communications in Computer and Information Science book series (CCIS, volume 1122)

Abstract

Nowadays, more and more machine learning algorithms are introduced into intrusion detection. Some researchers improved existing algorithms, while others combined a variety of methods. Each method have their benefits but limitations are inevitable. In this paper, we proposed a novel model of network intrusion detection based on anomaly traffic. And hidden Markov model (HMM) is utilized into this field, which effectively combines statistics and traffic classification. Based on network, some extracted traffic features based PCA are used as the input value of HMM. Eventually, the types of the traffic are judged by the probability value of output. If the traffic type is abnormal, the network is already under attack. Conversely, it’s under security. During model training, we creatively use conditional entropy to optimize the Baum-Welch algorithm, and the performance evaluation results indicate HMM achieve better precision and lower computational cost compared with others.

Keywords

Network security Intrusion detection Conditional entropy Hidden Markov model (HMM) Baum-Welch algorithm 

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Copyright information

© Springer Nature Singapore Pte Ltd. 2019

Authors and Affiliations

  1. 1.Tianjin University of TechnologyTianjinChina

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