Method for detecting dynamic gridding instruction based on artificial immunity

Abstract

A method for detecting dynamic gridding instruction based on artificial immunity, is a method for detecting instruction facing to gridding which takes use the artificial immunity technique for reference. According to the dynamic and real time requirement of the instruction detection under gridding surroundings, the method takes the prior clonal selection algorithm as main body, combines negative selection, clonal selection, affinity maturation and memory detector gene bank method, so at to dynamic handle the instruction detection under gridding surroundings. The method includes a dynamic detector evolvement process and a gridding instruction detection process which are based on artificial immunity, which is characterized in by using the artificial immunity technique for reference, and combining the negative selection, clonal selection, affinity maturation and memory detector gene bank method; firstly obtaining an evolvement matured detector; and then dynamically handling the instruction detection problem in the gridding surroundings under the coordination of the artificial immunity mechanism, to complete the entire process of dynamic gridding instruction detection.

Description

technical field [0001] The invention is a dynamic grid intrusion detection method based on artificial immunity. On the basis of the existing clone selection method, it integrates negative selection, clone selection, and memory detector gene library methods, and proposes clone selection embedded with negative operators. The method solves the problems of lack of dynamics, too long detection time and low efficiency in the grid intrusion detection method, and improves the accuracy and real-time detection of intrusion detection in the grid environment. The technology belongs to the grid security technical field. Background technique [0002] A grid is a set of resources that interconnect heterogeneous resources distributed in different geographical locations through a high-speed network to achieve full sharing and form a huge virtual computer to provide high-performance computing, management and services. The grid has the following characteristics different from the general netwo...

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Problems solved by technology

[0005] Due to the dynamic nature of the grid environment and the huge number of users, it is difficult to handle the heavy tasks in the grid system by using the negative selection algorithm alone in the intrusion detection of the grid system, and it is easy to cause the bottleneck of system scalability.

As far as the intrusion detection in the grid environment is concerned, various grid service behaviors that dynamically join or leave often change the definition of legal self-behavior patterns, so self and non-self behaviors will also change accordingly. Novel self and non-self exhibit poor discrimination, yielding high false positive rates when detecting newly added patterns

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