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

为了降低小样本数据库欺骗率,提升小样本数据库的攻击防御效果,设计了一种基于潜在数据挖掘的小样本数据库对抗攻击的防御算法(潜在数据挖掘的防御算法).采用改进的Apriori算法,通过频繁属性值集的工作过程获取准确的强关联规则优势,并从小样本数据库中挖掘潜在数据对抗攻击,同时优化候选集寻找频繁集的过程,然后利用关联分析检测对抗攻击,并通过可信度调度控制访问速率来防止产生恶意会话,实现小样本数据库对抗攻击防御.实验结果表明,潜在数据挖掘的防御算法可有效防御小样本数据库遭受的多种类型攻击,降低攻击产生的数据库欺骗率,保障小样本数据库服务器利用率的稳定性.

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 association rule advantages are obtained through the working process of frequent attribute value sets,and potential 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 analysis,and the access rate is controlled through credibility scheduling to defend against malicious sessions,achieving 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.