Predictive Abuse Detection for a PLC Smart Lighting Network Based on Automatically Created Models of Exponential Smoothing

<table class="table-group" id="tab2"><tr><td><table class="table"><tr><td class="thead-hr" colspan="2"><hr/></td></tr><tr class="thead"><td class="align_left">Network feature</td><td class="align_center">PLC smart lights network traffic feature description</td></tr><tr><td class="thead-hr" colspan="2"><hr/></td></tr><tr><td class="align_left">APL<sub>1</sub></td><td class="align_center">ENE: power consumption by PLC lamp [Wh]</td></tr><tr><td class="align_left">APL<sub>2</sub></td><td class="align_center">TEMP: lamp temperature [°C]</td></tr><tr><td class="align_left">APL<sub>3</sub></td><td class="align_center">LUL: lamp luminosity level in % (value: 0–100%)</td></tr><tr><td class="align_left">APL<sub>4</sub></td><td class="align_center">NR: number of lamp resets per time interval</td></tr><tr><td class="align_left">APL<sub>5</sub></td><td class="align_center">PS: power supply value [V]</td></tr><tr class="table-tr"><td colspan="2"><hr class="tbody-hr"/></td></tr></table></td></tr></table>

PLC application layer traffic features extracted from the traffic concentrators.

Security and Communication Networks

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Table 2

Table 2: Predictive Abuse Detection for a PLC Smart Lighting Network Based on Automatically Created Models of Exponential Smoothing 