基于审计日志的关联规则挖掘

为解决审计日志信息利用不充分的问题，通过改进FP-Growth方法研究了审计日志关联规则，提出了基于相互关联规则的KAFP-Growth算法。将现有的审计日志与先进的数据挖掘分析技术结合，在详细研究Apriori算法、PCY算法和FP-Growth算法后，引入了重要属性的概念，对审计日志中的重要属性进行约束，减小了频繁项集，提高了计算性能。实验结果验证KAFP-Growth算法有效提高了审计日志的关联分析效率，相较于FP-Growth算法，在最小支持度相同的情况下，改进算法运行时间速度提升了49.3%，随着数据集规模的增大，改进算法运行时间可提升60%以上。

Reasearch on mining association rules based on audit log

In order to solve the problem of insufficient audit log utilization, a related rule-based KAFP-Growth algorithm was proposed. Data mining technology is the discovery of potential relationships between transactions in many transactions, based on theoretical categories and practical methods in many areas. Combining existing audit logs with advanced data mining analysis techniques, after the PCY, Apriori, and FP-Growth algorithms were used to investigate, the concept of key attributes is introduced to limit the audit logs. The time complexity of the algorithm is reduced, and the calculation performance is improved. The experimental results show that the KAFP-Growth algorithm effectively improves the correlation analysis efficiency of audit logs. It is the conclusion that compared with the FP-Growth algorithm when the minimum support is the same, the running time is increased by 49.3%. As the size of the data set increases, the running time of the improved algorithm can be increased by more than 60%.