基于FP-Growth的入侵检测研究

数据挖掘可以利用各种分析工具从海量数据中发现模型和数据间的关系并做出预测。为了解决入侵检测在不降低精度的同时提高检测速度的问题,提高算法的效率,将FP-Growth算法应用于入侵检测系统中,提出对FP-Growth算法改进FP-tree的头表结构并引入关键属性来挖掘原始审计数据中的频繁模式,实验结果表明改进后的算法比传统的关联算法在入侵检测中的应用效果更好。可以看出,将FP-Growth算法应用于入侵检测中是可行的。

A Study of Intrusion Detection Based on Algorithm of FP-Growth

Data mining can find the relation between pattern and data from the large number of data and the forecast will be made.In order to adapt to the real-time nature of the intrusion of testing requirements,and enhance the efficiency of algorithms,presented to the FP-Growth algorithms that improves FP-tree table structures and the introduction of key attributes to intrusion detection system.The experimental results showed improved algorithm has better results than traditional association algorithm in the application of intrusion detection.Accordig to the result,FP-Growth algorithm is useful to intrusion detection.