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

Abstract

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 problem of data imbalance is more effectively solved.

Description

technical field [0001] The invention belongs to the field of information security, in particular to a method for processing imbalanced malicious code data based on a swarm intelligence algorithm and cGAN, and belongs to a data balancing strategy in malicious code classification problems. Background technique [0002] With the rapid development of information technology, the Internet has become an important part of our daily life, which brings many benefits to our life, study, and work, but at the same time hides many security threats such as Trojan horse viruses, phishing websites, and malware. problem, where malicious code is one of the main security threats. Driven by economic interests, the number of new malware samples is exploding. Anti-malware vendors are faced with millions of potentially malicious code samples every year. In order to continue to combat the increase of malicious code samples, research needs to rely on large, high-quality samples to build efficient ma...

AI Technical Summary

Problems solved by technology

[0010] In order to solve the problem of poor performance of the trained model caused by the unbalanced sample data set in the malicious code detection problem, the present invention proposes a new method to solve the unbalanced data. Generative Adversarial Networks) enhances the sample data of each family, and then selects the typical swarm intelligence algorithm Particle Swarm Optimization (PSO) to calculate the sample ratio of each family of malicious code according to the characteristics of swarm intelligence algorithm that is good at solving optimization combination problems , data enhancement is performed according to the ratio; finally, the original data set and the data set generated according to the ratio are used to construct a malicious code data set with relatively balanced sample data

Images