Advertisement

Research on Application of Artificial Immune System in 3G Mobile Network Security

Conference paper
Part of the Lecture Notes in Electrical Engineering book series (LNEE, volume 269)

Abstract

Currently, 3G communication technology develops very rapidly. Ensuring the security of 3G communication, monitoring network attack and avoiding risk become a hotspot concerned by the academic circle. This paper puts forward an intrusion detection algorithm based on artificial immunity, which is an important development direction of the improvement of security and reliability of 3G mobile network and detects network attack and real-time risk of mobile communication network with a good monitoring effect and high accuracy. Compared to RBF method, it has more accurate risk prediction of communication network and such characteristics as distributivity, diversity, auto-answer, self-maintenance, high accuracy and short time.

Keywords

Artificial immunity 3G Network security 

References

  1. 1.
    Wang F, Liu Z, Li J (2003) A computer security system model imitating biological immunity. Mini Micro Comput Syst 4:698–701Google Scholar
  2. 2.
    Tang J (2004) An architectural design of intrusion detection system based on computer immunology. Comput Knowl Technol 11:70–72Google Scholar
  3. 3.
    Xiao R, Wang L (2002) Artificial immune system: principle, model, analysis and prospect. Chinese J Comput 12:1281–1291MathSciNetGoogle Scholar
  4. 4.
    Li B (2007) Computer network security strategies based on artificial immune system. Appl Technol 8:94–95Google Scholar
  5. 5.
    Fu H, Yuan X (2008) Artificial immune model based on bilayer defense architecture. Comput Eng 1:178–180Google Scholar
  6. 6.
    Run Q, Jiang Y, Wu J (2005) Antibody forming and detection component of network intrusion detection system based on immune mechanism. Chinese J Comput 10:1601–1606Google Scholar

Copyright information

© Springer Science+Business Media Dordrecht 2014

Authors and Affiliations

  1. 1.Langfang Teachers CollegeLangfangChina

Personalised recommendations