Safety perception model construction method based on immune theory

Abstract

The invention discloses a safety perception model construction method based on an immune theory. The method comprises the following steps: 1, determining the affinity; 2, determining the antigen number; 3, determining the number of network layer security detectors; 4, obtaining energy. According to the invention, for increasingly enlarged networks and endless attacks, the current detection methodlacks adaptivity and expansibility, so that a more automatic and intelligent method needs to be studied to construct a detection model with strong adaptivity and dynamic expansibility.

Description

technical field [0001] The invention relates to the technical field of Internet of Things security, in particular to a method for constructing a security perception model based on immune theory. Background technique [0002] Traditional immune algorithms use deterministic identification methods to calculate the matching degree of antibodies and antigens, which is difficult to guarantee the accuracy of dynamic risk identification. Most of the traditional detection systems build detection models based on known pattern matching methods, and detect known network attacks by learning and training detection models from known samples and applying them to the security detection of the Internet of Things network layer. This type of detection model has problems such as slow system construction speed and high model update cost, making it difficult to detect complex and changeable attack types on the Internet of Things network layer. Contents of the invention [0003] In order to solv...

AI Technical Summary

Problems solved by technology

This type of detection model has problems such as slow system construction speed and high model update cost, making it difficult to detect complex and changeable attack types on the Internet of Things network layer

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