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Network attack detection system and method based on two-stage learning model

A network attack and learning model technology, which is applied in the two-stage detection system field of network attack data preprocessing and network attack data identification, can solve the problems of long model training time, increase the difficulty of network attack detection model and system deployment, and reduce the volume effect

Active Publication Date: 2022-05-10
HANGZHOU DIANZI UNIV
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Problems solved by technology

At the same time, the existing deep learning models for network attack detection often believe that the learning model is powerful, and feature preprocessing is not performed, which leads to a long training time for the model and increases the difficulty of deploying the network attack detection model and system

Method used

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  • Network attack detection system and method based on two-stage learning model
  • Network attack detection system and method based on two-stage learning model
  • Network attack detection system and method based on two-stage learning model

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Embodiment 1

[0041] Such as figure 1 As shown, the network attack detection method based on the two-stage learning model in this embodiment is carried out in the following steps:

[0042] Stage 1: Reduction of the feature dimension of the network dataset; in this stage, the feature subset of the network dataset is used as an inseparable unit for feature combination evaluation, and the feature dimension reduction of the network dataset is realized; the details are as follows:

[0043] Step 1.1, preprocessing of massive network data. Firstly, the data instances that exceed the limit of the missing feature threshold are screened out; then, the features with low information content are deleted in the network data set after the initial screening, such as serial number, timestamp, etc.; finally, the non-numeric data in the network data set The features are one-hot encoded and mapped to binary vectors;

[0044] Step 1.2, construct a feature subset evaluation function for the network dataset. B...

Embodiment 2

[0053] Such as Figure 4 As shown, the network attack detection system based on the two-stage learning model in this embodiment includes the following modules:

[0054] The feature dimension reduction module of the network dataset: this module evaluates the feature combination of the feature subset of the network dataset as an inseparable unit, and realizes the feature dimension reduction of the network dataset; specifically, it includes the following sub-modules:

[0055] Preprocessing module of massive network data: preprocessing of massive network data. Firstly, the data instances that exceed the limit of the missing feature threshold are screened out; then, the features with low information content are deleted in the network data set after the initial screening, such as serial number, timestamp, etc.; finally, the non-numeric data in the network data set The features are one-hot encoded and mapped to binary vectors;

[0056] Feature Subset Evaluation Function Building Bl...

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Abstract

The invention discloses a network attack detection system and method based on a two-stage learning model. The method of the invention is carried out according to the following steps: Step 1: The feature subset of the network data set is used as an inseparable unit for feature combination evaluation to realize network attack detection. The feature dimension reduction of the data set; Step 2: Use the reduced network data set as training data and use deep learning technology to implement a real-time network attack detection model. The two-stage network attack detection technical scheme of the present invention fully considers the feature combination effect of high-risk network data, and uses feature selection technology, evolutionary search technology and deep learning model to improve the accuracy and timeliness of network attack detection. The recognition accuracy of network attack detection can greatly reduce the model training time.

Description

technical field [0001] The invention belongs to the technical field of network attack detection, in particular to a two-stage detection system and method for network attack data preprocessing and network attack data identification. Background technique [0002] How to effectively implement network attack detection in the context of the Internet of Everything is one of the key issues faced in the big data environment. In recent years, various recognition methods based on deep neural networks have been widely used in network attack detection systems. Compared with traditional machine learning methods, deep neural network models can often achieve higher detection accuracy. The most critical factor affecting various network attack detection models is the use of data features of high-dimensional network attacks. Therefore, the first basic task of extracting valuable information from large-scale high-dimensional network attack data is to find the key features of network attack dat...

Claims

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Application Information

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Patent Type & Authority Patents(China)
IPC IPC(8): H04L9/40G06N3/08G06N3/04G06K9/62
CPCH04L63/1416G06N3/08G06N3/045G06F18/214
Inventor 滕旭阳张云啸何美霖毕美华仇兆炀
Owner HANGZHOU DIANZI UNIV
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