基于数据挖掘的入侵检测

Data Mining Based Intrusion Detection

结合入侵检测系统,比较各种数据挖掘算法,笔者发现最适合用于挖掘规则的是关联规则和聚类分析。基于此,笔者重点介绍和分析了Apriori算法和K-means算法,然后改进了传统的Apriori算法,最后应用联合数据挖掘思想,进一步提高了算法的执行效率。基于数据挖掘的入侵检测系统能有效地提高检测效率和降低误检率,具备一定的研究意义和实用价值。

By comparing various data mining algorithms to intrusion detection system,the authors found that association rules and clustering analysis are the most suitable for mining rules.Based onthis,the authors focus on introducing and analyzing the Apriori algorithm and K-means algorithm,then improves the traditional Apriori algorithm,and finally applies the idea of joint data mining to further improve the efficiency of the algorithm.Data mining based intrusion detection system can effectively improve the detection rate and reduce the false detection rate,which has certain research significance and practical value.