Ensemble of probabilistic learning networks for IOT edge intrusion detection

Tony Jan; A. S.M. Sajeev

doi:10.5121/IJCNC.2018.10608

Ensemble of probabilistic learning networks for IOT edge intrusion detection

Tony Jan, A. S.M. Sajeev

Research output: Contribution to journal › Article › peer-review

2 Citations (Scopus)

Abstract

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 real- time 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.

Original language	English
Pages (from-to)	135-147
Number of pages	13
Journal	International Journal of Computer Networks and Communications
Volume	10
Issue number	6
DOIs	https://doi.org/10.5121/IJCNC.2018.10608
Publication status	Published - 2018
Externally published	Yes

Keywords

Adaboosted ensemble learning
IoT edge security
Machine learning for IoT

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10.5121/IJCNC.2018.10608

Cite this

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title = "Ensemble of probabilistic learning networks for IOT edge intrusion detection",

abstract = "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 real- time 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.",

keywords = "Adaboosted ensemble learning, IoT edge security, Machine learning for IoT",

author = "Tony Jan and Sajeev, \{A. S.M.\}",

note = "Publisher Copyright: {\textcopyright} 2018, International Journal of Computer Networks \& Communications (IJCNC).",

year = "2018",

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AB - 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 real- time 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.

KW - Adaboosted ensemble learning

KW - IoT edge security

KW - Machine learning for IoT

UR - https://www.scopus.com/pages/publications/85062350771

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DO - 10.5121/IJCNC.2018.10608

M3 - Article

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JO - International Journal of Computer Networks and Communications

JF - International Journal of Computer Networks and Communications

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ER -

Ensemble of probabilistic learning networks for IOT edge intrusion detection

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Keywords

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