To detect perimeter intrusion accurately and quickly, a stream computing technology was\nused to improve real-time data processing in perimeter intrusion detection systems. Based on the\ntraditional density-based spatial clustering of applications with noise (T-DBSCAN) algorithm, which\ndepends on manual adjustments of neighborhood parameters, an adaptive parameters DBSCAN\n(AP-DBSCAN) method that can achieve unsupervised calculations was proposed. The proposed\nAP-DBSCAN method was implemented on a Spark Streaming platform to deal with the problems of\ndata stream collection and real-time analysis, as well as judging and identifying the different types of\nintrusion. A number of sensing and processing experiments were finished and the experimental data\nindicated that the proposed AP-DBSCAN method on the Spark Streaming platform exhibited a fine\ncalibration capacity for the adaptive parameters and the same accuracy as the T-DBSCAN method\nwithout the artificial setting of neighborhood parameters, in addition to achieving good performances\nin the perimeter intrusion detection systems.
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