Unsupervised deep auto-encoding network-based unknown threat detection method and system for HTTP data

The invention discloses an unknown threat detection method and system based on an unsupervised deep self-encoding network for HTTP data, and the method comprises the following steps: S101, data access: accessing HTTP request data; s102, data cleaning: cleaning the HTTP request data; s103, feature extraction: carrying out feature extraction on the cleaned HTTP data; s104, model matching: carrying out model matching on the extracted feature data; and S105, threat detection: carrying out threat detection on a model result. According to the unknown threat detection method and system for the HTTP data based on the unsupervised deep self-encoding network, unknown threats in the HTTP data can be effectively detected through the unsupervised deep self-encoding network, and the defect that a traditional threat detection method based on rules cannot effectively detect the unknown threats is effectively overcome. 本发明公开了一种针对HTTP数据基于无监督深度自编码网络的未知威胁检测方法及系统，包括如下步骤：S101、数据接入，接入HTTP请求数据；S102、数据清洗，对HTTP请求数据进行清洗；S103、特征提取，对清洗后的