Trust in Intrusion Detection Systems: An Investigation of Performance Analysis for Machine Learning and Deep Learning Models

<table class="table-group" id="tab9"><tr><td><table class="table"><tr><td class="thead-hr" colspan="2"><hr/></td></tr><tr class="thead"><td class="align_left">Parameter</td><td class="align_center">The value</td></tr><tr><td class="thead-hr" colspan="2"><hr/></td></tr><tr><td class="align_left">Dropout rate</td><td class="align_center">Within the range of 0.1 rate to 0.5 rate</td></tr><tr><td class="align_left">The number of neurons</td><td class="align_center">Within the range of 10 neuron to 200 neurons</td></tr><tr><td class="align_left">Regularization l2</td><td class="align_center">0.01, 0.05, 0.1, 0.2, 0.3, 0.4, 0.5</td></tr><tr class="table-tr"><td colspan="2"><hr class="tbody-hr"/></td></tr></table></td></tr></table>

<div>Hyperparameters’ configurations selected by Keras Tuner.</div>

Complexity

tab9

Table 9

Table 9: Trust in Intrusion Detection Systems: An Investigation of Performance Analysis for Machine Learning and Deep Learning Models 