Affiliations: [a] School of Information Engineering, Mianyang Normal University, Mianyang, P.R. China | [b] School of Electronic and Information Engineering, Beibu Gulf University, Qinzhou, P.R. China
Correspondence:
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Corresponding author. Mr. Shengchao Xu, School of Electronic and Information Engineering, Beibu Gulf University, Qinzhou 535011, P.R. China. E-mail: scxu_scholar@sina.com.
Abstract: 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.
Keywords: Cloud computing, intrusion detection, association rule data mining, Apriori, Hadoop, MapReduce, parallelization
