Network-oriented intrusion data detection method
A data detection and network technology, applied in data exchange networks, digital transmission systems, electrical components, etc., can solve problems such as difficulty in classifying data sets and low classification efficiency, eliminating irrelevant features, reducing false alarms, and improving classification. The effect of efficiency
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[0046] The working principle and technical solutions of the present invention will be further described in detail below in conjunction with the accompanying drawings and specific implementation.
[0047] Dimensionality reduction technology, as a means to improve computing performance, can be used as a preprocessing step in machine learning algorithms to eliminate irrelevant features and retain the most relevant features. In practice, there are two ways to re-determine the number of attributes in a dataset. The first approach is feature selection, which retrieves a subset of the original features without modifying data attributes. Feature selection can be divided into three categories by type: filters, wrappers, and embedding methods. Filter methods select features based on their intrinsic properties, i.e. regardless of classifier performance. In the second feature extraction method new features are created by generating new combinations of original features.
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