基于改进负选择算法的异常检测

Anomaly Detection Using Modified Negative Selection Algorithm

为实现用较少数目的检测器覆盖较大的非自体空间,提出一种基于渐增式矩形检测器的负选择算法。该方法采用D∞距离匹配原则,检测器在每一维的方向上呈指数形式逐渐增长,直至与自体空间相匹配,从而使得产生的每个检测器在空间的每一维都延伸至最大,能够产生足够优秀的检测器集覆盖非自体空间。通过对检测器的合并,消除重叠等简化处理,实现了检测器的数目和大小的双重优化。对不同几何形状的数据集合进行了仿真。实验结果表明,该算法在对非自体空间的覆盖和检测率的提高方面有显著的效果。

In order to occupy more coverage of the non- self space using fewer detectors, a new negative selection algorithm based on incremental rectangle detectors is proposed. In this scheme, the D∞ distance is employed to meassure the self and nonself space coverage. The sizes of the detectors are increased exponentially in each dimension until they overlap with the self samples. The generation strategy of detectors ensures that every detector is extended to its maximum size in each dimension. The number and size of these detectors are optimized by diminating the redundancy among the existing detectors. Consequently, the detection efficiency of individual detector is increased. Some datasets of different geometry shapes are applied to examine the proposed NSA. Experimental results show that the proposed algorithm has remarkable advantages in both ooverage of anomaly state space and detection rate.