Reverse Detector Generation Algorithm for Massive Self Set
CAI Tao1，WANG Wei-sheng1，NIU De-jiao1，NI Xiao-rong1，HU Yong-liang2
1（School of Computer Science and Telecommunication Engineering，Jiangsu University，Zhenjiang 212013，China）2（School of Mathematics and Information Engineering，Taizhou University，Taizhou 317000，China）
Abstract：Detector generation algorithm is important for artificial immune system.When implementing the artificial immune algorithm in big data system，the huge self will lead to the large time and space cost.In this paper，we analyze of the major factors affecting the efficiency of the distributed detector generation algorithm base on MapReduce model.Then we present the MapReverseReduce model and use it to design the reverse detector generation algorithm.We use Reverse stage to reverse the result of initial detector inspection from Map stage，then send the key-value pair of illegal detector to select the mature detector in Reduce stage.The Map，Reverse and Reduce can work in parallel.It can decrease the inspection between initial detectors and self，reduce the number of key-value pair that Reduce stage should deal with and the communication between Map and Reduce stage.The efficiency of detector generation can be improved obviously.Finally，we realize the prototype of distributed detector generation algorithm using MapReduce model and reverse detector generation algorithm respectively.Using the CERT synthethic sendmail data set to test the time overhead of detector generation，and different stage in reverse detector generation algorithm.The results show that the time overhead of reverse detector generation algorithm is of 5.22 to 19.07 percent of using MapReduce model，and time overhead can been maintained stability with the number of self increasing.
2016, Vol. 37 
