A classification algorithm for big data based on parallel immune network
1. School of Computer Science and Technology, Harbin University of Science and Technology, Harbin, Heilongjiang 150080, China; 2. College of Computer and Information Engineering, Heilongjiang University of Science and Technology, Harbin, Heilongjiang 150022, China
Abstract：To solve the problem of processing difficulty for big data in the serial immune network algorithm, the parallel immune network training and classification model were proposed, and a parallel immune network classification algorithm was designed under the framework of parallel Spark. The background knowledge of intrusion detection big data was introduced to establish Ainet parallel algorithm framework, and the algorithm steps of the proposed algorithm were described in detail. The cup99 intrusion detection data set was adopted in the experiments, and the Ainet algorithm was compared with other algorithms. The experimental results show that compared with the serial Ainet algorithm, the parallel Ainet algorithm can reduce the training time by 11/12 and the detection time by 19/20 and can improve the accuracy by 10% and the detection rate by 5% with reduced false alarm rate of 20%. The parallel Ainet algorithm achieves good effect in all aspects. The experimental verification of classification illuminates that the number of training data set has sensitive feature. The parallel Ainet algorithm outperforms other algorithms in accuracy, detection rate and false alarm rate with poor run time .
范大鹏1, 2, 张凤斌1. 一种基于并行免疫网络的大数据分类算法[J]. 江苏大学学报（自然科学版）, 2018, 39(5): 581-585.
FAN Da-Peng-1, 2 , ZHANG Feng-Bin-1. A classification algorithm for big data based on parallel immune network[J]. Journal of Jiangsu University(Natural Science Eidtion)
, 2018, 39(5): 581-585.
2018, Vol. 39 
