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Boosting Optimum-Path Forest clustering through harmony Search and its applications for intrusion detection in computer networks

机译:通过和声搜索提升最佳路径森林聚类及其在计算机网络中的入侵检测中的应用

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In this paper we propose a nature-inspired approach that can boost the Optimum-Path Forest (OPF) clustering algorithm by optimizing its parameters in a discrete lattice. The experiments in two public datasets have shown that the proposed algorithm can achieve similar parameters' values compared to the exhaustive search. Although, the proposed technique is faster than the traditional one, being interesting for intrusion detection in large scale traffic networks.
机译:在本文中,我们提出了一种自然启发方法,可以通过在离散格中优化其参数来提高最佳路径森林(OPF)聚类算法。两个公共数据集中的实验表明,与详尽的搜索相比,所提出的算法可以实现类似的参数值。虽然,所提出的技术比传统的技术更快,但对于大规模交通网络中的入侵检测是有趣的。

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