Malicious code data imbalance processing method based on swarm intelligence algorithm and cGAN

The invention discloses a malicious code data imbalance processing method based on a swarm intelligence algorithm and a cGAN. A malicious code generation model is constructed. And calculating an acceptable optimal initial sample proportion of the malicious code by adopting a swarm intelligence algorithm. Generating malicious codes of each family, and constructing a relatively balanced malicious code data set. According to the method, an acceptable optimal sample proportion of each malicious code family is obtained by utilizing a swarm intelligence algorithm, cGAN is introduced to learn data distribution of different families of malicious codes and perform sample generation, and finally, an unbalanced data set is processed to construct a malicious code data set with relatively balanced samples, so that the malicious code data set is optimized. Different types of malicious codes reach an ideal proportion during selection, positive and negative samples have the same status in the training process, and the proble