• DocumentCode
    3025873
  • Title

    Predators Combat Good Point Set Scanning-Based Self-Learning Worms

  • Author

    Wang, Fangwei ; Zhang, Yunkai ; Wang, Changguang ; Ma, Jianfeng

  • Author_Institution
    Network Center, Hebei Normal Univ., Shijiazhuang, China
  • Volume
    1
  • fYear
    2009
  • fDate
    11-14 Dec. 2009
  • Firstpage
    418
  • Lastpage
    422
  • Abstract
    Good point set scanning-based self-learning worms can reach a stupendous propagation speed in virtue of the non-uniform vulnerable-host distribution than that of traditional worms. In order to combat self-learning worms, this paper proposes an interaction model. Using the interaction model, we obtain the basic reproduction number. The impact of different parameters of predators is studied. Simulation results show that the performance of our proposed models is effective in combating such worms, in terms of decreasing the prey infectives and reducing the prey propagation speed.
  • Keywords
    invasive software; learning (artificial intelligence); good point set scanning; interaction model; predators; prey propagation; self-learning worms; Computational intelligence; Computer science; Computer security; Computer worms; Educational institutions; Internet; Physics; Stability; Uniform resource locators; Web server; good point set scanning; interaction model; predator; self-learning worms;
  • fLanguage
    English
  • Publisher
    ieee
  • Conference_Titel
    Computational Intelligence and Security, 2009. CIS '09. International Conference on
  • Conference_Location
    Beijing
  • Print_ISBN
    978-1-4244-5411-2
  • Type

    conf

  • DOI
    10.1109/CIS.2009.20
  • Filename
    5376503