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An intrusion detection system based on distinct network

A unique, network technology, applied in transmission systems, digital transmission systems, data exchange networks, etc., can solve problems such as difficult identification, inability to clone and amplify antibodies, and inability to retain features, and achieve good detection performance and false positives. Low rate and false negative rate, avoid the effect of regular updates

Inactive Publication Date: 2007-11-28
BEIJING UNION UNIVERSITY
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  • Summary
  • Abstract
  • Description
  • Claims
  • Application Information

AI Technical Summary

Problems solved by technology

[0011] The traditional network model method has the following defects: 1) With the increase of the number of antibodies in the network, the interaction between antibodies will be far greater than the stimulation effect of the antigen on the antibody, so that the antibody that can recognize the antigen cannot be cloned and expanded, so the network Cannot be tuned correctly; 2) Cannot preserve emerging features; 3) Does not readily recognize other antigens similar to existing antigens

Method used

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  • An intrusion detection system based on distinct network
  • An intrusion detection system based on distinct network
  • An intrusion detection system based on distinct network

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Embodiment Construction

[0019] The detailed description and technical contents of the present invention are described below in conjunction with the accompanying drawings.

[0020] Referring now to FIG. 1, the method for establishing an idiosyncratic network of the present invention is illustrated.

[0021] Antibody Ab 1 Because it matches the antigen Ag and recognizes the antigen Ag, it is stimulated (ps). Ab 1 with another antibody Ab 4 match and are therefore stimulated (ns) and inhibited (nn). Antibody Ab 4 It is called the internal image of the antigen Ag in the network, because it can be recognized by the same antibody Ab of the antigen Ag 1 recognized. Usually there is more than one internal image, and it is usually a set, because there are more than one antibody that can recognize the antigen, as shown in Figure 1 Ab 2 and Ab 3 . In addition, antibody Ab 2 and Ab 3 It can also be matched with other antibodies, and there is also a stimulating and inhibitory effect between them, thus ...

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Abstract

The invention discloses a building method of unique typed artificial network, which comprises the following steps: 1. initiating network; 2. establishing the connection relationship among all antibodies in the network; 3. adding antigen as exercise data for each antibody; (1)calculating the exciting level of each antibody; (2) selecting the most excited antibody to clone and vary; (3) adding the new generated antibody into the network; (4) recalculating the connection relationship among all antibodies; 4. testing whether the exercise is finished; outputting network if finishing; returning to the step 3 if not finishing.

Description

Technical field: [0001] The invention relates to a method for establishing an artificial idiosyncratic network, and an adaptive intrusion detection system of a computer network, in particular to an intrusion detection system utilizing the immune network theory of a biological immune system. Background technique: [0002] Using pattern recognition technology for intrusion detection, that is, all behaviors are divided into two categories: normal and attack, through the study of normal and attack behavior, using pattern matching to determine the category attribute of the behavior to be tested. This detection method combines the two methods of anomaly detection and misuse detection, which helps to improve the detection performance of the system. [0003] The artificial idiosyncratic network consists of a large number of high-performance units, such as noise tolerance, generalization ability, learning ability and memory ability, and parallel distributed processing ability realize...

Claims

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Application Information

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Patent Type & Authority Applications(China)
IPC IPC(8): H04L12/24
Inventor 赵林惠
Owner BEIJING UNION UNIVERSITY
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