lstm model and network attack identification method and system based on the model

Abstract

The present invention relates to an LSTM cyclic neural network model and a network attack identification method based on the model. The establishment process of the LSTM cyclic neural network model includes the following steps: S100: Collecting a plurality of network request data in text format as a training data set, and according to The content of the network request data sets the label category for each network request data; S200: Preprocess the network request data in the training data set and convert it into digital sequence data of a preset length; S300: According to the digital sequence in the training data set Type data, training, and constructing LSTM recurrent neural network model. The invention transforms the network request data into digital sequence data, and then uses the training data set composed of the data for training, constructs an LSTM cycle neural network model, and then realizes the prediction of the category of the network request data.

Description

technical field [0001] The invention relates to the technical field of network security, in particular to an LSTM cycle neural network model and a network attack identification method based on the model. Background technique [0002] With the continuous advancement of information strategy and the vigorous development of technologies such as the Internet and cloud computing, more and more enterprise-related businesses have completed digital transformation and moved their business to the network. However, due to the openness and uncontrollability of network applications and the limitations of network application developers, network applications are very likely to have network vulnerabilities that can be exploited. Hackers can use these vulnerabilities to carry out network attacks such as SQL injection and XSS attacks, which bring risks such as website paralysis, information leakage, web page tampering, and Trojan horses, and bring huge losses to the main body of the website an...

AI Technical Summary

Problems solved by technology

This rule-based protection method often leads to false positives or false negatives in the face of flexible and changeable network attacks, and the formulation and maintenance of rules requires professional security-related personnel to be responsible. However, it is still difficult to cover attacks With various deformations, it is difficult to effectively deal with unknown attacks and 0day attacks, and there may even be conflicts between rules. In addition, it is difficult to grasp the balance between misjudgments and missed judgments in the formulation of rules. Too strict rules can easily kill normal business by mistake. flow, misjudgment

Rules that are too loose are easily bypassed, resulting in missed judgments

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