Application of High-Dimensional Outlier Mining Based on the Maximum Frequent Pattern Factor in Intrusion Detection

<table class="table-group" id="tab1"><tr><td><table class="table"><tr><td class="thead-hr" colspan="2"><hr/></td></tr><tr class="thead"><td class="align_left">Attribute types</td><td class="align_center">Column</td></tr><tr><td class="thead-hr" colspan="2"><hr/></td></tr><tr><td class="align_left">Text type</td><td class="align_center">2, 3, and 4</td></tr><tr><td class="align_left">Numerical discrete type</td><td class="align_center">7, 12, 14, 15, 21, and 22</td></tr><tr><td class="align_left">Continuous numerical data</td><td class="align_center">1, 5, 6, and other columns</td></tr><tr class="table-tr"><td colspan="2"><hr class="tbody-hr"/></td></tr></table></td></tr></table>

<div>NSL-KDD dataset attribute data types.</div>

Mathematical Problems in Engineering

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Table 1

Table 1: Application of High-Dimensional Outlier Mining Based on the Maximum Frequent Pattern Factor in Intrusion Detection 