Network intrusion detection method based on improved convolutional neural network

Abstract

The invention provides a network intrusion detection method based on an improved convolutional neural network. The method comprises the steps of acquiring a data set, performing numeralization, performing normalization processing, improving convolutional neural network model training, performing iterative optimization of model parameters, acquiring five classifiers, outputting a five-dimensional confusion matrix to serve as a classification result, and evaluating the classification result; the network intrusion detection method based on the improved convolutional neural network provided by theinvention is based on an improved convolutional neural network model, model training is performed by using a preprocessed original sample data set in combination with a cross-layer design manner, themodel achieves a good convergence effect through continuous feature extraction and iterative optimization, and then a classification test is performed by using the trained classifier, so that the feasibility and effectiveness of an intrusion detection effect can be improved by the method.

Description

technical field [0001] The invention belongs to the technical field of network information security, in particular to a network intrusion detection method based on an improved convolutional neural network. Background technique [0002] Network Intrusion Detection System (NIDS) refers to the combination of software and hardware that detects behaviors that endanger computer system security, such as collecting vulnerability information, denying access, and obtaining system control rights beyond the legal scope. With the continuous emergence of new network attack characteristics, an adaptable, stable and effective intrusion detection method has become an urgent need. At present, although the general-purpose network authentication mechanism and firewall technology can meet the basic security protection needs of users, their protection capabilities are relatively weak. Once they encounter malicious attacks by professional hackers, these protection measures will be useless. At pre...

AI Technical Summary

Problems solved by technology

[0003] Artificial intelligence methods currently used in intrusion detection methods mainly include neural networks, genetic algorithms, and immune algorithms. Although these methods have improved sample recognition capabilities and performance, they have overfitting and generalization capabilities in network training. Insufficient in poor quality, detection accuracy and detection efficiency need to be improved

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