Network intrusion detection method and system based on data enhancement and BiLSTM

Abstract

The invention provides a network intrusion detection method and system based on data enhancement and BiLSTM (Bidirectional Long Short Term Memory), and solves the problem that the existing network intrusion detection method is low in intrusion detection and recognition accuracy of minority class attack samples, firstly, network intrusion detection flow data is collected, then, preliminary feature extraction is performed, a training data set is formed, attack type data samples with small data volume are confirmed and then data enhancement is carried out, then a BiLSTM neural network model is constructed and iterative learning training is carried out, the model automatically extracts feature information at a higher level, high-dimensional nonlinear network traffic features can be better processed, manual limitation caused by the fact that traditional shallow machine learning depends on manual feature extraction is overcome, the problem that class distribution in a training data set is unbalanced can be solved through data enhancement operation, and the recognition accuracy of minority class attack samples is improved on the premise that the model keeps a high overall detection rate and a low false alarm rate.

Description

technical field [0001] The present invention relates to the technical field of network security, more specifically, to a network intrusion detection method and system based on data enhancement and BiLSTM. Background technique [0002] With the continuous development of network technology, while the Internet has brought great help to our lives, the number of computer network attacks is also increasing sharply. The behavior of realizing unauthorized access on the Internet, the network intrusion detection technology is of great significance to protect people's life and maintain the security of cyberspace, but how to obtain better network intrusion detection effect in the massive unbalanced data environment is an urgent need to solve technical issues. [0003] The current practice of applying machine learning to network intrusion detection makes up for the problems of traditional intrusion detectors, such as poor adaptation, high false alarm rate and false alarm rate. However, ...

AI Technical Summary

Problems solved by technology

[0004] In order to solve the problem that the existing network intrusion detection method has a low accuracy rate of intrusion detection and recognition for minority attack samples, the present invention proposes a network intrusion detection method and system based on data enhancement and BiLSTM, which maintains a high overall detection rate and Under the premise of low false positive rate, improve the recognition accuracy of minority class attack samples

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