Network intrusion detection method based on transfer learning and integrated learning

A network intrusion detection and integrated learning technology, applied in the field of network security, can solve the problems of complex network traffic, difficult generation of detection sample data sets, and huge workload of labeling data sets, so as to speed up convergence, save training time, and improve detection. The effect of accuracy

Inactive Publication Date: 2018-07-24
EAST CHINA ELECTRIC POWER TEST & RES INST +1
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Problems solved by technology

[0004] 1. Unable to detect new types of attacks
[0005] The generalization performance of the attack detection method based on machine learning is limited. It has high accuracy for known types of attack behaviors, and has a high false positive rate for new types of attack b

Method used

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  • Network intrusion detection method based on transfer learning and integrated learning

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Embodiment

[0031] Existing network intrusion detection methods include:

[0032] 1. Intrusion detection based on pattern matching

[0033] The research on intrusion detection began in the 1980s, and after the rise of the Internet, it received high attention and developed rapidly. As early as 1980, J Anderson and others made a technical report entitled "Computer Security Threat Monitoring and Surveillance" for the U.S. Air Force. This report is recognized as the pioneering achievement of intrusion detection. This report proposes the concept of intrusion detection for the first time, proposes to use audit messages to track suspicious behaviors of users, and simply divides intrusion behaviors. In 1985, Denning proposed an intrusion detection technology based on statistical analysis, real-time and user behavior profiles in Oakland, and made the first real-time intrusion detection system model, which is a milestone in the field of intrusion detection research. Since then, a large number of ...

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Abstract

The invention relates to a network instruction detection method based on transfer learning and integrated learning. The method comprises the following steps: 1) training and generating multiple independent intrusion detection machine learning judgment models according to standard data; 2) forming an integrated judgment model for the intrusion detection according to the integrated judgment; 3) producing various network traffic data containing attack behaviors by using a network attack tool, and screening the network traffic data by using the integrated judgment model; and 4) taking the screenednetwork traffic data as the attack data, performing transfer learning on the machine learning model, thereby finally obtaining the optimal learning judgment model. Compared with the prior art, the network intrusion detection method disclosed by the invention has the advantages of saving the training time, accelerating the convergence time and high accuracy.

Description

technical field [0001] The invention relates to the field of network security, in particular to a network intrusion detection method based on transfer learning and integrated learning. Background technique [0002] Network intrusion is the act of using the loopholes of the target system to achieve unauthorized access on the attacked computer through network communication protocols. Intrusion detection is a technical method used to detect intrusion behavior. Traditional intrusion detection methods detect abnormal traffic by detecting the characteristics of traffic on the network. New attack methods emerge in an endless stream on the Internet. From the generation of a new attack method to its use to attack the target system, the existing detection systems have no way to obtain information about this attack, so this attack is also called " Zero-day attack". For this "zero-day attack" behavior, traditional detection methods cannot be effectively detected, so using new methods ...

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Application Information

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IPC IPC(8): H04L29/06G06N99/00
CPCG06N20/00H04L63/1416H04L63/1425
Inventor 郭乃网苏运杨洪山范磊方炯朱旻捷解梁军葛网华陈睿宋岩庞天宇沈泉江张启芳
Owner EAST CHINA ELECTRIC POWER TEST & RES INST
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