基于免疫的检测漏洞问题研究

Study on leaks of detection based on immunity

针对已有实值非选择算法中检测漏洞问题,提出一种改进的算法提高对检测漏洞的覆盖。算法基于可变长实值检测器实现,主要思想是把自体样本分为边界自体样本和非边界自体样本。在检测器的生成过程中,鉴别和记录边界自体样本;在对新样本的检测过程中,检测是否匹配边界自体。通过人工合成数据集2DSyntheticData和实际Iris数据集对算法进行了验证。实验结果表明,算法检测率较高,在覆盖自体和非自体边界处的漏洞方面明显优于已有的算法。

For the leaks of the available real-valued negative selection algorithm, an improved negative selection algorithm is proposed. The algorithm is based on variable-length real-valued detectors. The samples are divided into boundary samples and non-boundary samples. In the detector generation process, it identifies and records the boundary samples. While new samples is tested, whether the detectors match the boundary samples are recorded. Detailed realization and advantages of the algorithm are given. The experiments of synthetic and real data sets（Iris data set and biomedical data set）are done to test the algorithm. The results show that the algorithm has higher detection rate and need less detector numbers. It is superior to available algorithms in coverage of leaks at boundary of self and non-self.