本文在对关联规则挖掘中Apriori算法的深入研究和分析的基础上，发现并指出了该算法存在的不足，改进了在由K阶频繁项集生成K+1阶候选项集时的连接和剪枝策略及对事务数据库的处理方式，它在时间和空间上提高了Apriori算法的效率。根据改进后的算法提出了入侵检测方法，该方法实现了规则库的自动更新，极大地提高了系统的安全性和可靠性。实验结果表明，该方法明显提高了频繁项目集的生成效率，入侵检测系统知识规则库的生成效率也得到改善。

On the basis of a deep research and analysis of the Apriori algorithm in association rule mining，some shortages of the algorithm are discovered. The strategy of the join step,the prune step and the method of dealing with the transaction database is improved, which improves the efficiency of the Apriori algorithm both in time and space. An intrusion detection method based on the improved algorithm is proposed. The rule library of the system can be updated automatically. The security and the dependability of the system can be greatly improved. The experimental results of the improved algorithm show that the method improves the production efficiency of frequent item sets and the knowledge rule library.