Network anomaly detection system and detection method based on a neural network

A neural network and network anomaly technology, applied in biological neural network models, transmission systems, electrical components, etc., can solve problems such as the decline of cybercrime professional skills, and achieve the effects of high accuracy, improved detection efficiency, and short running time.

Inactive Publication Date: 2020-01-21
NORTHEASTERN UNIV
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In addition, the specialized skills needed for cybercrime

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  • Network anomaly detection system and detection method based on a neural network
  • Network anomaly detection system and detection method based on a neural network
  • Network anomaly detection system and detection method based on a neural network

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[0034]The following describes the technical solution of the present invention in detail in conjunction with the accompanying drawings and specific embodiments. First, data preprocessing is performed on the KDDCup99 data set, and data processing is performed on discrete features and continuous features, and then PCA dimensionality reduction is performed on the data. , and then use the neural network to detect the abnormality of the data, and compare it with KNN (K nearest neighbor algorithm) and SVM (support vector machine algorithm). Several commonly used evaluation indicators are used, mainly including accuracy rate, precision rate, recall rate, F-score, and ROC curve.

[0035] A detection system based on a neural network-based network anomaly detection system, including a data preprocessing unit and an abnormal data unit for identifying network attacks, the data preprocessing unit is used to encode and process the messy and numerous original data in the KDDCup99 data set Nor...

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Abstract

The invention provides a network anomaly detection system and detection method based on a neural network. The detection system comprises a coding processing module, a data normalization module and a feature selection module, an accuracy rate module and an observer operation characteristic curve drawing module. The detection method comprises the following steps: firstly, processing discrete characteristics in a KDCUP 99 data set into a numeric type by adopting one-hot coding; then, carrying out feature processing by adopting Min-Max; secondly, carrying out dimension reduction processing and inputting the processed data into an MLPClassifier multi-layer perceptron classifier to obtain a prediction result; and finally, inputting the prediction result into an observer operation characteristiccurve drawing module to draw an ROC curve. A multi-layer perceptron neural network is adopted, over-fitting is prevented through an L2 regularization method, hidden nodes are adjusted, meanwhile, a cross validation method is adopted for continuous training and debugging, compared with a KNN method and an SVM method, the operation time and the accuracy are superior to those of the two methods, andthe superiority of the method is verified.

Description

technical field [0001] The technology relates to the field of neural networks, in particular to a network anomaly detection system and detection method based on neural networks. Background technique [0002] Due to the wide application of computer networks, it is inevitable to detect network attacks and protect information security. The extensive use of computer systems has caused many threats, and the wide spread of networks has led to various types of attacks, such as zero-day vulnerability attacks. The development of computer networks has greatly exacerbated computer security issues, especially the network security of today's network environment and advanced computing equipment. Although the Internet protocol suite was not designed for security issues, network administrators must now deal with individuals and individuals with malicious intentions. Mass intrusions of large botnets. According to the Symantec Network Security Threat Report, there were more than 3 billion ma...

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Application Information

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IPC IPC(8): H04L29/06G06N3/02
CPCG06N3/02H04L63/1416H04L63/1425
Inventor 张钧桓任涛刘子瑜杨可舟丁匀泰
Owner NORTHEASTERN UNIV
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