Feature selection method of grey wolf optimization algorithm based on fused random black hole strategy

A feature selection method and optimization algorithm technology, applied in calculation, calculation model, computer components, etc., can solve problems such as large randomness and easy to fall into local optimum, and achieve the effect of improving accuracy and efficiency

Inactive Publication Date: 2020-03-13
GUIZHOU UNIV +1
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Problems solved by technology

Optimization algorithms are often used to solve feature selection problems, but commonly used optimization algorithms are more random in feature selection or easily fall into local optimum

Method used

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  • Feature selection method of grey wolf optimization algorithm based on fused random black hole strategy
  • Feature selection method of grey wolf optimization algorithm based on fused random black hole strategy
  • Feature selection method of grey wolf optimization algorithm based on fused random black hole strategy

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Embodiment 1

[0044] In this embodiment, the gray wolf optimization algorithm (Grey Wolf Optimization, GWO) is a meta-heuristic biological intelligence algorithm, which solves the optimal value by simulating the predation mechanism of wolves. In the feature selection process of industrial control system intrusion detection, because each candidate solution of the optimal population is determined according to the positions of the three wolves, it will fall into a local optimal situation. This embodiment provides a gray wolf optimization algorithm that integrates the random black hole strategy. Through the disturbance of the candidate solutions of the population by the random black hole strategy, the ability of the gray wolf optimization algorithm to jump out of the local optimal solution is enhanced, so that it can select influential feature sequence.

[0045] The random black hole strategy (Random Black Hole, RBH) is a new population update strategy. The random black hole strategy simulates ...

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Abstract

The invention discloses a feature selection method of a grey wolf optimization algorithm based on a fused random black hole strategy. The method comprises the steps of initializing a grey wolf population, obtaining the position update of the grey wolf population, updating the positions of the three head wolves by fusing a random black hole strategy, solving the relative distance between the threehead wolves and the remaining wolves, adjusting the positions of the remaining wolves, obtaining the position of the current wolf after the position update according to the approximate distance, and predicting the value of the optimal solution according to the positions of the three head wolves. The method provided by the invention is used in the intrusion detection process of the industrial control system, the characteristics of the intrusion detection data set are calculated, analyzed and selected, and then the characteristic sequence which is beneficial to improving the intrusion detectionaccuracy and efficiency of the industrial control system is obtained, so that the intrusion detection accuracy and efficiency can be effectively improved.

Description

technical field [0001] The invention relates to the technical field of intrusion detection, in particular to a feature selection method based on a gray wolf optimization algorithm fused with a random black hole strategy. Background technique [0002] In recent years, the wave of informatization has swept the world, and information and communication technology and manufacturing technology have cross-integrated. In terms of industry, with the integration and complementarity of informatization and industrialization, industrial control systems gradually use standardized network protocols and open application software, which gives criminals an opportunity, and more or less industrial The situation that the control system is destroyed, so intrusion detection technology has become one of the important components of industrial control information security protection technology. In the process of detecting intrusions, detection algorithms often have to face large-volume and high-dim...

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Application Information

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Patent Type & Authority Applications(China)
IPC IPC(8): G06N3/00G06K9/62G06F21/55
CPCG06F21/55G06N3/006G06F18/214
Inventor 耿志强曾荣甫韩永明汪鹏欧阳智
Owner GUIZHOU UNIV
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