Due to the poor detection performance of traditional methods,an efficient detection method for communication network worm viruses based on ReliefF algorithm is proposed.Firstly,the ReliefF algorithm is used to collect the characteristics of communication network worm viruses,and the features are filtered based on Application Programming Interface(API)calls.Secondly,the selected worm virus features are used as training samples,and the selected training samples are input into the classifier for model construction.Finally,the model was optimized using cross validation methods and the constructed model was used to detect the presence of worm viruses in the file.The experimental results show that the communication network worm virus detection method based on ReliefF algorithm has a high accuracy and is superior to the other two methods.
data miningcommunication networkworm virusdetection method
NETL
NSTL
万方数据
