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A Machine Learning Based Intrusion Detection System for Mobile Internet of Things

Intrusion detection systems plays a pivotal role in detecting malicious activities that denigrate the performance of the network. Mobile adhoc networks (MANETs) and wireless sensor networks (WSNs) are a form of wireless network that can transfer data without any need of infrastructure for their oper...

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Detalles Bibliográficos
Autores principales: Amouri, Amar, Alaparthy, Vishwa T., Morgera, Salvatore D.
Formato: Online Artículo Texto
Lenguaje:English
Publicado: MDPI 2020
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC7013568/
https://www.ncbi.nlm.nih.gov/pubmed/31947567
http://dx.doi.org/10.3390/s20020461
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author Amouri, Amar
Alaparthy, Vishwa T.
Morgera, Salvatore D.
author_facet Amouri, Amar
Alaparthy, Vishwa T.
Morgera, Salvatore D.
author_sort Amouri, Amar
collection PubMed
description Intrusion detection systems plays a pivotal role in detecting malicious activities that denigrate the performance of the network. Mobile adhoc networks (MANETs) and wireless sensor networks (WSNs) are a form of wireless network that can transfer data without any need of infrastructure for their operation. A more novel paradigm of networking, namely Internet of Things (IoT) has emerged recently which can be considered as a superset to the afore mentioned paradigms. Their distributed nature and the limited resources available, present a considerable challenge for providing security to these networks. The need for an intrusion detection system (IDS) that can acclimate with such challenges is of extreme significance. Previously, we proposed a cross layer-based IDS with two layers of detection. It uses a heuristic approach which is based on the variability of the correctly classified instances (CCIs), which we refer to as the accumulated measure of fluctuation (AMoF). The current, proposed IDS is composed of two stages; stage one collects data through dedicated sniffers (DSs) and generates the CCI which is sent in a periodic fashion to the super node (SN), and in stage two the SN performs the linear regression process for the collected CCIs from different DSs in order to differentiate the benign from the malicious nodes. In this work, the detection characterization is presented for different extreme scenarios in the network, pertaining to the power level and node velocity for two different mobility models: Random way point (RWP), and Gauss Markov (GM). Malicious activity used in the work are the blackhole and the distributed denial of service (DDoS) attacks. Detection rates are in excess of 98% for high power/node velocity scenarios while they drop to around 90% for low power/node velocity scenarios.
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spelling pubmed-70135682020-03-09 A Machine Learning Based Intrusion Detection System for Mobile Internet of Things Amouri, Amar Alaparthy, Vishwa T. Morgera, Salvatore D. Sensors (Basel) Article Intrusion detection systems plays a pivotal role in detecting malicious activities that denigrate the performance of the network. Mobile adhoc networks (MANETs) and wireless sensor networks (WSNs) are a form of wireless network that can transfer data without any need of infrastructure for their operation. A more novel paradigm of networking, namely Internet of Things (IoT) has emerged recently which can be considered as a superset to the afore mentioned paradigms. Their distributed nature and the limited resources available, present a considerable challenge for providing security to these networks. The need for an intrusion detection system (IDS) that can acclimate with such challenges is of extreme significance. Previously, we proposed a cross layer-based IDS with two layers of detection. It uses a heuristic approach which is based on the variability of the correctly classified instances (CCIs), which we refer to as the accumulated measure of fluctuation (AMoF). The current, proposed IDS is composed of two stages; stage one collects data through dedicated sniffers (DSs) and generates the CCI which is sent in a periodic fashion to the super node (SN), and in stage two the SN performs the linear regression process for the collected CCIs from different DSs in order to differentiate the benign from the malicious nodes. In this work, the detection characterization is presented for different extreme scenarios in the network, pertaining to the power level and node velocity for two different mobility models: Random way point (RWP), and Gauss Markov (GM). Malicious activity used in the work are the blackhole and the distributed denial of service (DDoS) attacks. Detection rates are in excess of 98% for high power/node velocity scenarios while they drop to around 90% for low power/node velocity scenarios. MDPI 2020-01-14 /pmc/articles/PMC7013568/ /pubmed/31947567 http://dx.doi.org/10.3390/s20020461 Text en © 2020 by the authors. Licensee MDPI, Basel, Switzerland. This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY) license (http://creativecommons.org/licenses/by/4.0/).
spellingShingle Article
Amouri, Amar
Alaparthy, Vishwa T.
Morgera, Salvatore D.
A Machine Learning Based Intrusion Detection System for Mobile Internet of Things
title A Machine Learning Based Intrusion Detection System for Mobile Internet of Things
title_full A Machine Learning Based Intrusion Detection System for Mobile Internet of Things
title_fullStr A Machine Learning Based Intrusion Detection System for Mobile Internet of Things
title_full_unstemmed A Machine Learning Based Intrusion Detection System for Mobile Internet of Things
title_short A Machine Learning Based Intrusion Detection System for Mobile Internet of Things
title_sort machine learning based intrusion detection system for mobile internet of things
topic Article
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC7013568/
https://www.ncbi.nlm.nih.gov/pubmed/31947567
http://dx.doi.org/10.3390/s20020461
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