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
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;
Conference_Titel :
Computational Intelligence and Security, 2009. CIS '09. International Conference on
Conference_Location :
Beijing
Print_ISBN :
978-1-4244-5411-2
DOI :
10.1109/CIS.2009.20