A Novel Way to Generate Adversarial Network Traffic Samples against Network Traffic Classification

<table class="table-group" id="tab4"><tr><td><table class="table"><tr><td class="thead-hr" colspan="2"><hr/></td></tr><tr class="thead"><td class="align_left">Environment</td><td class="align_center">Parameters</td></tr><tr><td class="thead-hr" colspan="2"><hr/></td></tr><tr><td class="align_left">OS</td><td class="align_center">Win10,64bit</td></tr><tr><td class="align_left">Processor</td><td class="align_center">CPU: Intel Core i5-7200 U;<br/>GPU: NVIDIA GeForce 940MX</td></tr><tr><td class="align_left">Memory</td><td class="align_center">8 GB, LPDDR3, 2133 MHz</td></tr><tr><td class="align_left">Pycharm</td><td class="align_center">Community Edition 2019.3.1</td></tr><tr><td class="align_left">Tensorflow</td><td class="align_center">Ver. 2.1.0</td></tr><tr><td class="align_left">Library support</td><td class="align_center">Cleverhans 3.0.1 [<a href="/journals/wcmc/2021/7367107/#B32" target="_blank">32</a>]</td></tr><tr class="table-tr"><td colspan="2"><hr class="tbody-hr"/></td></tr></table></td></tr></table>

<div>Experiment’s environment and parameters.</div>

Wireless Communications and Mobile Computing

tab4

Table 4

Table 4: A Novel Way to Generate Adversarial Network Traffic Samples against Network Traffic Classification 