PIoT Malicious Traffic Detection Method Based on GAN Sample Enhancement

<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">Time</td><td class="align_center">Types</td></tr><tr><td class="thead-hr" colspan="2"><hr/></td></tr><tr><td class="align_left">Monday</td><td class="align_center">Benign</td></tr><tr><td class="align_left">Tuesday</td><td class="align_center">FTP – Patator (9:20-10：20), SSH – Patator (14:00-15:00)</td></tr><tr><td class="align_left">Wednesday</td><td class="align_center">DoS Slowloris (9:47-10:10), DoS Slowhttptest (10:14-10:35), DoS Hulk (10:43-11:00), DoS GoldenEye (11:10-12:23), Heartbleed Attack (15:12-15:23)</td></tr><tr><td class="align_left">Thurs.</td><td class="align_center">Web Attack - Brute Force (9:20-10:00), Web Attack – XSS (10:15-10:35), Web Attack - Sql Injection (10:40-10:42), Infiltration Attacks (14:19-15:45)</td></tr><tr><td class="align_left">Friday</td><td class="align_center">Botnet (10:02-11:02), PortScan (13:55-15:23), DDoS (15:57-16:16)</td></tr><tr class="table-tr"><td colspan="2"><hr class="tbody-hr"/></td></tr></table></td></tr></table>

<div>The dataset CICIDS2017 malicious traffic list.</div>

Security and Communication Networks

tab1

Table 1

Table 1: PIoT Malicious Traffic Detection Method Based on GAN Sample Enhancement 