The development of the Internet had led to a wide variety of types of malware information.In order to detect and obtain deeper levels of malware information,research was being conducted on malware information detection methods based on knowledge graphs to improve the effectiveness of malware information detection.Using Python web crawl-er technology in text mining to collect effective software information;Extract software information features from the collected software effective information through information gain algorithm;Input software information features into a bidirectional long short-term memory neural network,output software information entity recognition results,and extract relationships between software information entities;Based on entity disambiguation technology,knowledge fusion was performed on the extracted software information entity relationships to obtain a software information knowledge graph;Using graph inference algorithms to process software information knowledge graphs and obtain malware information detection results.Experimental results had shown that this method could effectively collect effective software information,extract software information fea-tures,and establish a software information knowledge graph;This method could effectively detect malicious software infor-mation and has high detection accuracy.
knowledge graphmalicious softwareinformation detectionpython web crawlerneural net-worksgraph inference algorithm
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