Description: Firstly, in order to extract the information of network security situation accurately and effectively, a hierarchical frame feature acquisition method based on enhanced probabilistic neural network is proposed. According to different functions of Agent node, the hierarchical feature acquisition framework is divided into different levels. The principal component analysis (PCA) is used to reduce the training sample attributes and the special attribute encoding fusion. The result can be used to optimize the structure of the probabilistic neural network (PNN) so as to reduce the system complexity. Then, the improved PNN is used as the base classifier. Combined with the adaptive enhancement algorithm, the final strong classifier is formed through repeated iteration, weight replacement and weighted fusion. The experimental results show that the proposed model achieve higher accuracy and better generalization ability than other methods.
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libsvm-3.17\heart_scale
libsvm-3.17\matlab\libsvmread.c
libsvm-3.17\matlab\libsvmread.mexw64
libsvm-3.17\matlab\libsvmwrite.c
libsvm-3.17\matlab\libsvmwrite.mexw64
libsvm-3.17\matlab\make.m
libsvm-3.17\matlab\Makefile
libsvm-3.17\matlab\README
libsvm-3.17\matlab\svmpredict.c
libsvm-3.17\matlab\svmpredict.mexw64
libsvm-3.17\matlab\svmtrain.c
libsvm-3.17\matlab\svmtrain1.mexw64
libsvm-3.17\matlab\svm_model_matlab.c
libsvm-3.17\matlab\svm_model_matlab.h
libsvm-3.17\svm-predict.c
libsvm-3.17\svm-scale.c
libsvm-3.17\svm-train.c
libsvm-3.17\svm.cpp
libsvm-3.17\svm.def
libsvm-3.17\svm.h
libsvm-3.17\matlab
libsvm-3.17
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