基于标签影响力传播的人工免疫检测器生成算法研究  

Study on Artificial Immune Detector Generation Algorithm Based on Label Influence Propagation

作　　者：周遵龙 陈文 马欣蕾 ZHOU Zunlong;CHEN Wen;MA Xinlei(School of Cyber Science and Engineering,Sichuan University,Chengdu 610065,China)

机构地区：[1]四川大学网络空间安全学院,成都610065

出　　处：《计算机科学》2024年第5期346-354,共9页Computer Science

摘　　要：人工免疫系统利用训练样本对候选检测器进行筛选训练,以产生覆盖非自体区域的成熟检测器用于自体和非自体的区分。传统基于否定选择的检测器生成算法(Negative Selection Algorithm,NSA)通常需要大量有标记的自体训练样本,而实际应用中已标记样本有限,导致检测器训练不足,限制了检测器的检测精度。针对这一问题,提出了一种基于标签影响力传播的免疫检测器训练方法。在属于同一聚类的样本中,通过少量的已标记聚类成员进行标签影响力传播,为聚类中的未标记样本进行伪标记。随后,基于噪声学习的伪标记评估去除低可信的新标记样本。通过了标签评估的新标记样本被加入训练样本集合,以扩展已标记样本规模,提升免疫检测器的训练质量。在7类不同维度和规模的UCI公开数据集上的对比实验结果表明,所提基于标签影响力传播的免疫检测训练算法能够有效提升检测器的训练性能,尤其在训练样本有限或数据集不均衡的情况下,检测器的性能明显优于传统方法,相较于PSA,co-PSA和GFNSA等检测生成算法,检测器的识别精度平均提升了10%。Artificial immune systems utilize training samples to screen and train candidate detectors,so as to generate mature detectors covering non-self regions for self and non-self differentiation.The traditional negative selection algorithm(NSA)based detector generation algorithm usually requires a large number of labeled self training samples,while the limited number of labeled samples in practical applications leads to insufficient detector training,which restricts the detection accuracy of detectors.To address this problem,this paper proposes an immune detector training method based on label influence propagation,where label influence propagation is performed by a small number of labeled cluster members among samples belonging to the same cluster,and pseudo-labeling is performed for the unlabeled samples in the cluster.Subsequently,this paper removes low-confidence newly labeled samples based on noise-learning-based pseudo-labeling assessment.The newly labeled samples that passed the labeling assessment are added to the training sample set to extend the labeled sample size and improve the training quality of the immune detector.Comparative experimental results on seven types of UCI public datasets of different dimensions and sizes show that the proposed label influence propagation-based immune detection training algorithm is able to effectively improve the training performance of the detector,especially in the case of limited training samples or unbalanced datasets,the detector's performance is significantly better than the traditional methods.Compared with the detection generation algorithms such as PSA,co-PSA,GFNSA,etc,the recognition accuracy of the detector is improved by 10%on average.

关 键 词：标签影响力传播 人工免疫 检测器生成算法 标签评估 

分 类 号：TP391[自动化与计算机技术—计算机应用技术]