Focused on the issues that the traditional detection methods cannot cope with malicious PDF documents effectively and always result in false positives,a detection model based on graph neural network and deep learning(DGNN)was introduced.The tracking tool captured the system calls once opening a document,and system call graphs were constructed,accompanied by the division according to the threads.Simultaneously,a method of graph sampling based on the H-index was proposed for down-scaling.The sampled subgraphs were used as the input of the model.Subsequently,the association relations were extracted through the graph convolution network,and the attribute features were extracted using deep learning for fusion.The final detec-tion was completed according to the nature of system call graphs.Experimental results show that,compared with other methods,the proposed model has outstanding performances in feature extracting and training,effectively improving the accuracy of PDF detection.
关键词
PDF文档检测/图神经网络/深度学习/图采样/特征分析/性能评价/系统调用
Key words
PDF document detection/graph neural network/deep learning/graph sampling/feature analysis/performance eva-luation/system call
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