Intelligent malware detection based on graph convolutional network.Malware has seriously threatened the safety of computer systems for a long time.  To solve the problem, a malware classifier based on graph convolutional network is designed to adapt to the difference of malware characteristics.  The specific method is to firstly extract the API call sequence from the malware code and generate a directed cycle graph, then use the Markov chain and principal component analysis method to extract the feature map of the graph, and design a classifier based on graph convolutional network, and finally analyze and compare the performance of the method. 
graph convolutional network
With its better predictive performance, AI-based malware detection has been increasingly used to deal with malware in recent years. 
AI-based malware detection
The specific method is to firstly extract the API call sequence from the malware code and generate a directed cycle graph, then use the Markov chain and principal component analysis method to extract the feature map of the graph, and design a classifier based on graph convolutional network, and finally analyze and compare the performance of the method. 
Markov chain
The specific method is to firstly extract the API call sequence from the malware code and generate a directed cycle graph, then use the Markov chain and principal component analysis method to extract the feature map of the graph, and design a classifier based on graph convolutional network, and finally analyze and compare the performance of the method. 
principal component analysis
Intelligent malware detection based on graph convolutional network.Malware has seriously threatened the safety of computer systems for a long time.  To solve the problem, a malware classifier based on graph convolutional network is designed to adapt to the difference of malware characteristics.  The specific method is to firstly extract the API call sequence from the malware code and generate a directed cycle graph, then use the Markov chain and principal component analysis method to extract the feature map of the graph, and design a classifier based on graph convolutional network, and finally analyze and compare the performance of the method. 
graph convolutional network