一种自适应的人工免疫异常检测算法

Self-adaptive anomaly detection algorithm with artificial immune

提出了一种基于免疫的自适应异常检测算法SAIM,该算法通过对训练抗原的学习,形成最优的抗体对记忆细胞集进行进化和更新,通过记忆细胞集采用KNN方法投票进行异常检测。实验采用著名UCI机器学习数据库的Hepatitis标准数据集,获得的分类准确率为93.5,与现有同类算法进行比较,SAIM所取得的准确率具有一定的优越性。

A self-adaptive anomaly detection algorithm based on artificial immune system,named as SAIM,is proposed.In SAIM,the optimal antibodies will be obtained through the learning of each training antigen and the memory cell will be evolved and updated by the optimal antibodies.Anomaly detection process is accomplished by majority vote of the k nearest neighbor antibody in the artificial immune system.The experiments use the famous Hepatitis Benchmark dataset,which is taken from the UCI machine learning database.The obtained detection accuracy of SAIM is 93.5,which is very promising with regard to the other classification applications in the literature for this problem.