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

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          Abstract

          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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          Most cited references32

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          Distributed attack detection scheme using deep learning approach for Internet of Things

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            SVELTE: Real-time intrusion detection in the Internet of Things

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              Robust Malware Detection for Internet Of (Battlefield) Things Devices Using Deep Eigenspace Learning

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                Author and article information

                Journal
                Sensors (Basel)
                Sensors (Basel)
                sensors
                Sensors (Basel, Switzerland)
                MDPI
                1424-8220
                14 January 2020
                January 2020
                : 20
                : 2
                : 461
                Affiliations
                [1 ]Department of Electrical Engineering, University of South Florida, Tampa, FL 33620, USA; aamouri@ 123456mail.usf.edu
                [2 ]Department of Electrical and Computer Engineering, Duke University, Durham, NC 27708, USA; vishwa.alaparthy@ 123456duke.edu
                Author notes
                [* ]Correspondence: sdmorgera@ 123456usf.edu ; Tel.: +1-813-974-1004
                Author information
                https://orcid.org/0000-0002-6499-584X
                https://orcid.org/0000-0001-8074-6803
                Article
                sensors-20-00461
                10.3390/s20020461
                7013568
                31947567
                004091d5-0c02-4e11-9fc7-0d9068fb5b86
                © 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/).

                History
                : 10 October 2019
                : 11 January 2020
                Categories
                Article

                Biomedical engineering
                intrusion detection systems,wsn,iot,random forest,amof,linear regression
                Biomedical engineering
                intrusion detection systems, wsn, iot, random forest, amof, linear regression

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