吉首大学学报（自然科学版）2024，Vol.45Issue(1)：30-35,6.DOI:10.13438/j.cnki.jdzk.2024.01.006
基于潜在数据挖掘的小样本数据库对抗攻击防御算法
Anti-Attack Defense Algorithm of Small Sample Database Based on Potential Data Mining
摘要
Abstract
In order to reduce the deception rate of small sample databases and improve the attack defense effectiveness of small sample databases,a small sample database adversarial attack defense algorithm based on latent data mining was designed.With the improved Apriori algorithm,accurate strong associa-tion rule advantages are obtained through the working process of frequent attribute value sets,and poten-tial data is mined from small sample databases to resist attacks,with the process of finding frequent sets from candidate sets optimized.On this basis,adversarial attacks are detected through association analy-sis,and the access rate is controlled through credibility scheduling to defend against malicious sessions,a-chieving defense against small sample database adversarial attacks.The experimental results show that defense algorithms for potential data mining can effectively defend against various types of attacks on small sample databases,reduce the database spoofing rate caused by attacks,and thus ensure the stability of server utilization in small sample databases.关键词
数据挖掘/关联规则/强关联规则/小样本数据库/攻击检测/Apriori算法Key words
data mining/association rules/strong association rules/small sample database/attack detec-tion/Apriori algorithm分类
信息技术与安全科学引用本文复制引用
曹卿..基于潜在数据挖掘的小样本数据库对抗攻击防御算法[J].吉首大学学报（自然科学版）,2024,45(1):30-35,6.基金项目
福建省中青年教师教育科研项目(JAT220424) （JAT220424）
