Log monitoring method for behavior anomaly detection based on deep learning improved IFOREST
A deep learning and anomaly detection technology, applied in the field of network security, can solve problems such as long running time and achieve the effect of improving accuracy
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[0057] The present invention will be described in detail below in conjunction with the accompanying drawings and specific embodiments.
[0058] A log monitoring method based on deep learning to improve IFOREST for abnormal behavior detection, the specific operation steps are as follows:
[0059] Step 1, use the log user information generated in the platform system to extract, and take the user's operation on the database as an example to make long-term statistics on the number of operations performed by the user on different behaviors such as adding tables, deleting tables, changing tables, and looking up tables every day , generate user log behavior vectors and store them in MySQL;
[0060] Step 2. Obtain the log user behavior vector and use Auto-Encoder to reduce the dimensionality. According to the ratio of 8:2, the data is randomly divided into the user behavior training set and the user behavior test set. The user behavior training set is used to train and generate user b...
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