The rise of the cloud computing model has resulted in more than terabytes of data being stored in the cloud platform every day on the Internet. Mining valuable information from these massive data has become an emerging industry direction, but the current Intrusion-detection system (IDS) has been unable to adapt to large-scale log information mining. Therefore, an association rule mining algorithm based on MapReduce parallel computing framework is proposed. Firstly, the frequent itemsets mining algorithm Apriori is analyzed, and the MapReduce model is used to parallelize and improve it to more efficiently complete the mining of frequent itemsets. Secondly, the parallel Apriori is designed to run on IDS. Finally, the simulation experiment was carried out by building an open source cloud computing framework Hadoop cluster. Finally, the simulation experiment was carried out by building an open source cloud computing framework Hadoop cluster. The results show that the proposed method has higher detection efficiency when processing massive data, and requires less processing time.

中文翻译：

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MapReduce并行关联挖掘在IDS在云计算环境中的应用

云计算模型的兴起导致每天在Internet上将超过TB的数据存储在云平台中。从这些海量数据中挖掘有价值的信息已成为新兴的行业方向，但是当前的入侵检测系统（IDS）一直无法适应大规模的日志信息挖掘。因此，提出了一种基于MapReduce并行计算框架的关联规则挖掘算法。首先，分析了频繁项集挖掘算法Apriori，并使用MapReduce模型对其进行并行化和改进，以更有效地完成频繁项集的挖掘。其次，并行Apriori设计为在IDS上运行。最后，通过构建开源云计算框架Hadoop集群进行了仿真实验。最后，仿真实验是通过构建一个开源云计算框架Hadoop集群进行的。结果表明，该方法在处理海量数据时具有较高的检测效率，并且所需的处理时间更少。