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首页> 外文期刊>International Journal of Embedded Systems >Design of network intrusion detection system based on parallel DPC clustering algorithm
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Design of network intrusion detection system based on parallel DPC clustering algorithm

机译：基于并行DPC聚类算法的网络入侵检测系统设计

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摘要

With the advent of the era of big data, network intrusion detection systems based on K-means algorithm cannot meet the detection efficiency and detection speed requirements in big data environment. The DPC algorithm can be applied to high-dimensional network traffic and large-scale data application environments, but there are problems of large calculated amount and limited serial processing capability. Aiming at the problems of DPC algorithm, the DPC algorithm is adjusted firstly to improve the clustering accuracy of the algorithm. Then, the DPC algorithm a parallelised on the Spark platform, so that the processing ability and running speed of the DPC algorithm is greatly improved by running in parallel in the memory of multiple virtual machines. The experimental results show that the network intrusion detection system based on parallel DPC clustering algorithm has higher detection rate and lower false rate. The parallelisation clustering efficiency is much higher than the single-computer clustering efficiency.
机译：随着大数据时代的出现，基于K-Means算法的网络入侵检测系统无法满足大数据环境中的检测效率和检测速度要求。 DPC算法可以应用于高维网流量和大规模数据应用环境，但是计算量大的数量和串行处理能力有限的问题。针对DPC算法问题，首先调整DPC算法以提高算法的聚类精度。然后，通过在多个虚拟机的存储器中并行运行，通过在多个虚拟机的存储器中运行，使DPC算法是在火花平台上并行化的，使得DPC算法的处理能力和运行速度大大提高。实验结果表明，基于并行DPC聚类算法的网络入侵检测系统具有较高的检测率和较低的假速率。平行聚类效率远高于单计算机聚类效率。

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