Design of Anomaly-Based Intrusion Detection System Using Fog Computing for IoT Network
With increase in the demand for Internet of Things (IoT)-based services, the capability to detect anomalies such as malicious control, spying and other threats within IoT-based network has become a major issue. Traditional Intrusion Detection Systems (IDSs) cannot be used in typical IoT-based networ...
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| Vydané v: | Automatic control and computer sciences Ročník 55; číslo 2; s. 137 - 147 |
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| Hlavní autori: | , , |
| Médium: | Journal Article |
| Jazyk: | English |
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Moscow
Pleiades Publishing
01.03.2021
Springer Nature B.V |
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| ISSN: | 0146-4116, 1558-108X |
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| Abstract | With increase in the demand for Internet of Things (IoT)-based services, the capability to detect anomalies such as malicious control, spying and other threats within IoT-based network has become a major issue. Traditional Intrusion Detection Systems (IDSs) cannot be used in typical IoT-based network due to various constraints in terms of battery life, memory capacity and computational capability. In order to address these issues, various IDSs have been proposed in literature. However, most of the IDSs face problem of high false alarm rate and low accuracy in anomaly detection process. In this paper, we have proposed a anomaly-based intrusion detection system by decentralizing the existing cloud based security architecture to local fog nodes. In order to evaluate the effectiveness of the proposed model various machine learning algorithms such as Random Forest, K-Nearest Neighbor and Decision Tree are used. Performance of our proposed model is tested using actual IoT-based dataset. The evaluation of the underlying approach outperforms in high detection accuracy and low false alarm rate using Random Forest algorithm. |
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| AbstractList | With increase in the demand for Internet of Things (IoT)-based services, the capability to detect anomalies such as malicious control, spying and other threats within IoT-based network has become a major issue. Traditional Intrusion Detection Systems (IDSs) cannot be used in typical IoT-based network due to various constraints in terms of battery life, memory capacity and computational capability. In order to address these issues, various IDSs have been proposed in literature. However, most of the IDSs face problem of high false alarm rate and low accuracy in anomaly detection process. In this paper, we have proposed a anomaly-based intrusion detection system by decentralizing the existing cloud based security architecture to local fog nodes. In order to evaluate the effectiveness of the proposed model various machine learning algorithms such as Random Forest, K-Nearest Neighbor and Decision Tree are used. Performance of our proposed model is tested using actual IoT-based dataset. The evaluation of the underlying approach outperforms in high detection accuracy and low false alarm rate using Random Forest algorithm. |
| Author | Tripathi, Rakesh Prabhat Kumar Gupta, Govind P. |
| Author_xml | – sequence: 1 surname: Prabhat Kumar fullname: Prabhat Kumar email: pkumar.phd2019.it@nitrr.ac.in organization: Department of Information Technology, National Institute of Technology – sequence: 2 givenname: Govind P. surname: Gupta fullname: Gupta, Govind P. email: gpgupta.it@nitrr.ac.in organization: Department of Information Technology, National Institute of Technology – sequence: 3 givenname: Rakesh surname: Tripathi fullname: Tripathi, Rakesh email: rtripathi.it@nitrr.ac.in organization: Department of Information Technology, National Institute of Technology |
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| Copyright | Allerton Press, Inc. 2021. ISSN 0146-4116, Automatic Control and Computer Sciences, 2021, Vol. 55, No. 2, pp. 137–147. © Allerton Press, Inc., 2021. |
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Elrawy (7339_CR2) 2018; 7 I. Stojmenovic (7339_CR5) 2014; 2015 7339_CR1 7339_CR8 D. Etherington (7339_CR3) 2016 N. Chaabouni (7339_CR6) 2019; 21 7339_CR15 7339_CR16 7339_CR7 K.A.P. da Costa (7339_CR22) 2019; 151 R.M. Swarna Priya (7339_CR24) 2020; 160 |
| References_xml | – reference: Hasan, M., Islam, M.M., Zarif, M.I.I., and Hashem, M.M.A., Attack and anomaly detection in IoT sensors in IoT sites using machine learning approaches, Internet Things, 2019, vol. 7, artic. no. 100059. https://doi.org/10.1016/j.iot.2019.100059 – reference: PrabavathyS.SundarakanthamK.ShalinieS.M.Design of cognitive fog computing for intrusion detection in Internet of ThingsJ. Commun. Networks20182029129810.1109/JCN.2018.000041 – reference: ChaabouniN.MosbahM.ZemmariA.SauvignacC.FarukiP.Network intrusion detection for IoT security based on learning techniquesIEEE Commun. Surv. Tutorials2019212671270110.1109/COMST.2019.2896380 – reference: DengL.LiD.YaoX.CoxD.WangH.Mobile network intrusion detection for IoT system based on transfer learning algorithmCluster Comput.2019229889990410.1007/s10586-018-1847-2 – reference: Pahl, M.O. and Aubet, F.X., All eyes on you: Distributed multi-dimensional IoT microservice anomaly detection, 14th Int. Conf. Netw. Serv. Manag. CNSM 2018 Work. 1st Int. Work. High-Precision Networks Oper. Control. HiPNet 2018 1st Work. Segm. Routing Serv. Funct. Chain. SR+SFC 2, 2018, pp. 72–80. – reference: HajiheidariS.WakilK.BadriM.NavimipourN.J.Intrusion detection systems in the Internet of things: A comprehensive investigationComput. Networks201916016519110.1016/j.comnet.2019.05.014 – reference: Reddy, G.T., Kaluri, R., Reddy, P.K., Lakshmanna, K., Koppu, S., and Rajput, D.S., A novel approach for home surveillance system using IoT adaptive security, Proceedings of International Conference on Sustainable Computing in Science, Technology and Management (SUSCOM), Amity University Rajasthan, Jaipur - India, February 26–28,2019, 2019, pp. 1616–1620. https://doi.org/10.2139/ssrn.3356525 – reference: Kumar, V., Das, A.K., and Sinha, D., UIDS: A unified intrusion detection system for IoT environment, Evol. Intell., 2019, artic. no. 0123456789. https://doi.org/10.1007/s12065-019-00291-w – reference: MehmoodA.MukherjeeM.AhmedS.H.SongH.MalikK.M.NBC-MAIDS: Naïve Bayesian classification technique in multi-agent system-enriched IDS for securing IoT against DDoS attacksJ. Supercomput.2018745156517010.1007/s11227-018-2413-7 – reference: Evans, D., The Internet of Things: How the Next Evolution of the Internet Is Changing Everything, Cisco White Paper, 2011. – reference: BenestyJ.ChenJ.HuangY.CohenI.Noise Reduction in Speech Processing200910.1007/978-3-642-00296-0 – reference: PajouhH.H.JavidanR.KhayamiR.DehghantanhaA.ChooK.K.R.A two-layer dimension reduction and two-tier classification model for anomaly-based intrusion detection in IoT backbone networksIEEE Trans. Emerg. Top. Comput.2019731432310.1109/TETC.2016.2633228 – reference: TrentS.C.ArtilesA.J.EnglertC.S.From deficit thinking to social constructivism: A review of theory, research, and practice in special educationRev. Res. Educ.19982327730710.2307/1167293 – reference: TaliaD.TrunfioP.MarozzoF.Data Analysis in the Cloud: Models, Techniques and Applications2015 – reference: Anthi, E., Williams, L., and Burnap, P., Pulse: An adaptive intrusion detection for the internet of things, IET Conf. Publ., 2018, vol. 2018, no. CP740. https://doi.org/10.1049/cp.2018.0035 – reference: da CostaK.A.P.PapaJ.P.LisboaC.O.MunozR.de AlbuquerqueV.H.C.Internet of Things: A survey on machine learning-based intrusion detection approachesComput. Networks201915114715710.1016/j.comnet.2019.01.023 – reference: DiroA.ChilamkurtiN.Distributed attack detection scheme using deep learning approach for Internet of ThingsFuture Gener. Comput. Syst.20188276176810.1016/j.future.2017.08.043 – reference: Swarna PriyaR.M.An effective feature engineering for DNN using hybrid PCA-GWO for intrusion detection in IoMT architectureComput. Commun.202016013914910.1016/j.comcom.2020.05.048 – reference: Benmessahel, I., Xie, K., Chellal, M., and Semong, T., A new evolutionary neural networks based on intrusion detection systems using locust swarm optimization, Evol. Intell., 20190, vol. 12, no. 2, pp. 131–146. https://doi.org/10.1007/s12065-019-00199-5 – reference: EtheringtonD.CongerK.Large DDoS attacks cause outages at Twitter, Spotify, and other sites2016 – reference: MilosevicJ.RegazzoniF.MalekM.Malware threats and solutions for trustworthy mobile systems design, Hardware Security and Trust: Design and Deployment of Integrated Circuits in a Threatened Environment2017 – reference: ElrawyM.F.AwadA.I.HamedH.F.A.Intrusion detection systems for IoT-based smart environments: A surveyJ. Cloud Comput.2018712010.1186/s13677-018-0123-6 – reference: DS2OS traffic traces, Kaggle. https://www.kaggle.com/francoisxa/ds2ostraffictraces. 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| SubjectTerms | Accuracy Algorithms Anomalies Cloud computing Computer Science Control Structures and Microprogramming Decision trees Espionage False alarms Internet of Things Intrusion detection systems Machine learning Model testing |
| Title | Design of Anomaly-Based Intrusion Detection System Using Fog Computing for IoT Network |
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