计算机免疫危险理论中危险信号的提取方法研究

Research of Danger Signal Extraction Based on Changes in Danger Theory

危险理论是人工免疫系统的一个重要研究分支,它从危险的角度出发对免疫系统的工作原理进行了新的阐述,目前已广泛应用于入侵检测、机器学习和数据挖掘等领域。建立危险理论模型的首要问题是如何自适应地提取危险信号。从变化导致危险这一思想出发,建立了一套基于变化特征的危险信号自适应提取模型;针对不同类型系统资源的特点,设计了基于值变化和特征变化的两种危险信号提取方法。同时,通过实验验证了该模型在不依赖先验知识的情况下,能够自适应地提取危险信号。

Danger theory is an important research branch in artificial immune system. It starts from the perspective of danger to describe the working principle of immune system in a new way, which has been widely used in intrusion detec- tion, machine learning, data mining and so on. The primary issue of establishing a danger theory model is how to extract danger signals adaptively. This paper started from the main idea of changes leading to danger, and established an adaptive danger signal extraction model based on finding changes. According to the characteristics of different types of system resources, it designed two danger signal extraction methods：value changes and feature changes. The experiment verifies that this model can adaptively extract danger signals without relying on prior knowledge.