The Dendritic Cell Algorithm Optimized by Chemokine Simulator in Spyware Detection
Spywares changeable and complex hidden acts and behavior correlation increase the difficulty in detecting. Most of current detection methods put emphasis on the hidden characteristics whereas ignore the other. Dendritic Cell Algorithm can combine a set of input signals deriving from antigens and classify them. In human immune system, the chemokine can stimulate dendritic cells chemotaxis, guide them to the infected tissue. This passage takes behaviors and correlation among different behaviors fully into consideration, and employs the Dendritic Cell Algorithm to detect spywares. Moreover, it can make the detection more efficient and accurate that applying chemokine simulator which imitates the function that chemokine does on immune system to drive the DCA and referencing concentration of chemokine into output signals to quicken the trend rate of CSM.
Danger Theory Dendritic Cell Algorithm spyware hidden behavior chemokine
Jie Yuan Chengyu Tan Yuxi Chen Yue Xiao Yichun Gu
Computer School, Wuhan University,Wuhan, China International School of Software, Wuhan University,Wuhan, China
国际会议
哈尔滨
英文
1012-1016
2011-12-24（万方平台首次上网日期，不代表论文的发表时间）