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A Multi-source Generation Algorithm for Neighborhood Morphological Space Artificial Immune Detector

An artificial immune and algorithm generation technology, applied in the field of artificial intelligence immune system, can solve the problems of increasing the space-time cost of each algorithm of the detector, decreasing the detection performance of the system, and low coverage of the detector, so as to improve the generation efficiency and detection performance, avoid The curse of dimensionality, the effect of reducing the amount of computation

Inactive Publication Date: 2021-03-05
HARBIN UNIV OF SCI & TECH
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AI Technical Summary

Problems solved by technology

Among them, using the number of detectors or statistical inference as the end condition of the algorithm will cause the redundancy of detectors and the "black hole" problem caused by low detector coverage.
At the same time, the sample matching strategy in the real-valued morphological space is mainly based on Euclidean distance or Manhattan distance, and the real-valued detector that maps complex environmental features has a high dimensionality, which will cause the problem of "dimension disaster". The cost increases sharply, so that the detection performance of the system drops sharply

Method used

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  • A Multi-source Generation Algorithm for Neighborhood Morphological Space Artificial Immune Detector
  • A Multi-source Generation Algorithm for Neighborhood Morphological Space Artificial Immune Detector
  • A Multi-source Generation Algorithm for Neighborhood Morphological Space Artificial Immune Detector

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Embodiment 1

[0046] A multi-source generating algorithm for a neighborhood morphological space artificial immune detector, the generating method comprising:

[0047] Step 1, the sample is subjected to standardized preprocessing;

[0048] Step 2, setting parameters, said parameters include: mature detector maximum scale N d , the training threshold ρ, the neighborhood division step size step and the space coverage rate p of the algorithm end condition, the maximum evolution algebra T of the genetic algorithm;

[0049]Step 3: Divide the domain morphological space according to the neighborhood division step step, map the self-samples to the neighborhood morphological space, and determine the boundary of each dimension attribute in the sample and the step size of the sample under each dimension attribute; wherein, the neighborhood Domain shape space is the mathematical basis of this algorithm, and its mathematical model is formed based on discrete topological space theory.

[0050] Step 4: U...

Embodiment 2

[0078] combined with figure 1 , 2 , 3 and this embodiment further describe in detail a multi-source generation algorithm for a neighborhood morphological space artificial immune detector.

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Abstract

The invention proposes a multi-source generating algorithm for an artificial immune detector in a neighborhood morphological space, belonging to the technical field of artificial intelligence immune systems. The multi-source generation algorithm of the artificial immune detector in the neighborhood morphological space uses the neighborhood morphological space, improves the neighborhood negative selection algorithm, and introduces chaos theory and genetic algorithm to form a detector multi-source generation algorithm, thereby solving real-valued Problems in various algorithms of detectors in morphological space.

Description

technical field [0001] The invention relates to a multi-source generating algorithm for an artificial immune detector in a neighborhood shape space, and belongs to the technical field of artificial intelligence immune systems. Background technique [0002] Anomaly detection establishes a pattern profile of normal behavior, and current activity is considered anomalous if it violates its rules. Artificial immune system is one of the important branches of artificial intelligence technology. It is an intelligent method that imitates the function of biological immune system. It is widely used in abnormal detection, data mining, machine learning and other fields. The detector is its core set, and its generation, optimization, and detection operations determine the application effect. [0003] At present, the artificial immune system usually converts the problem domain to the real-valued morphological space, but there are many problems in the traditional detector algorithms based ...

Claims

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Application Information

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Patent Type & Authority Patents(China)
IPC IPC(8): G06F30/27G06N3/12
CPCG06N3/126G06N7/08
Inventor 席亮姚之钰赵一霖张凤斌
Owner HARBIN UNIV OF SCI & TECH
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