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

<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">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">DLN<sub>1</sub></td><td class="align_center">RSSI: received signal strength indication for PLC lamps [dBm]</td></tr><tr><td class="align_left">DLN<sub>2</sub></td><td class="align_center">SNR: signal-to-noise ratio [dBu]</td></tr><tr><td class="align_left">DLN<sub>3</sub></td><td class="align_center">PER: packet error rate per time interval [%]</td></tr><tr><td class="align_left">DLN<sub>4</sub></td><td class="align_center">PPTM: number of packets per time interval</td></tr><tr><td class="align_left">DLN<sub>5</sub></td><td class="align_center">TTL: packet time-to-live value</td></tr><tr class="table-tr"><td colspan="2"><hr class="tbody-hr"/></td></tr></table></td></tr></table>

PLC data link and network layer traffic features extracted from the traffic concentrators.

Security and Communication Networks

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Table 1

Table 1: Predictive Abuse Detection for a PLC Smart Lighting Network Based on Automatically Created Models of Exponential Smoothing 