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Ensemble of Probabilistic Learning Networks for IoT Edge Intrusion Detection

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IJCNCJournal
IJCNCJournal

This paper proposes an intelligent and compact machine learning model for IoT intrusion detection using an ensemble of semi-parametric models with Ada boost. The proposed model provides an adequate realtime intrusion detection at an affordable computational complexity suitable for the IoT edge networks. The proposed model is evaluated against other comparable models using the benchmark data on IoT-IDS and shows comparable performance with reduced computations as required.

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DOI: 10.5121/ijcnc.2018.10608 135
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International Journal of Computer Networks & Communications (IJCNC) Vol.10, No.6, November 2018 138
International Journal of Computer Networks & Communications (IJCNC) Vol.10, No.6, November 2018 139
International Journal of Computer Networks & Communications (IJCNC) Vol.10, No.6, November 2018 140
International Journal of Computer Networks & Communications (IJCNC) Vol.10, No.6, November 2018 141
International Journal of Computer Networks & Communications (IJCNC) Vol.10, No.6, November 2018 142
International Journal of Computer Networks & Communications (IJCNC) Vol.10, No.6, November 2018 143
International Journal of Computer Networks & Communications (IJCNC) Vol.10, No.6, November 2018 144
International Journal of Computer Networks & Communications (IJCNC) Vol.10, No.6, November 2018 145
International Journal of Computer Networks & Communications (IJCNC) Vol.10, No.6, November 2018 146
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