Context-Aware Device Classification and Clustering for Smarter and Secure Connectivity in Internet of Things
DOI:
https://doi.org/10.4108/eetinis.v10i3.3874Keywords:
Internet of Things, IoT, Clusters, Context Parameters, Cluster Head Update, Authentication, Access ControlAbstract
With the increasing prevalence of the Internet of Things (IoT), there is a growing need for effective access control methods to secure IoT systems and data. Traditional access control models often prove inadequate when dealing with the specific challenges presented by IoT, characterized by a variety of heterogeneous devices, ever-changing network structures, and diverse contextual elements. Managing IoT devices effectively is a complex task in maintaining network security.
This study introduces a context-driven approach for IoT Device Classification and Clustering, aiming to address the unique characteristics of IoT systems and the limitations of existing access control methods. The proposed context-based model utilizes contextual information such as device attributes, location, time, and communication patterns to dynamically establish clusters and cluster leaders. By incorporating contextual factors, the model provides a more accurate and adaptable clustering mechanism that aligns with the dynamic nature of IoT systems. Consequently, network administrators can configure dynamic access policies for these clusters.
Downloads
References
Ashton K (2009) That ‘internet of things’ thing. RFID J 22(7):97–114 June 2009, http://www.rfidjournal.com/article/view/4986 (04- 04-2014)J. Clerk Maxwell, A Treatise on electricity and magnetism, 3rd ed., vol. 2. Oxford: Clarendon, 1892, pp.68–73.
Inayat Ali et. al, “Internet of Things Security, device authentication and access control: A review”, international Jour. of comp. sci. and info. secu. (IJCSIS), vol. 14, no. 8, pp. 1-11, 2016., August 2016, https://doi.org/10.48550/arXiv.1901.07309.
Hassani et. al., “Efficient execution of complex context queries to enable near real-time smart iot applications”, sensors (Basel). 2019 Dec 11;19(24):5457. doi: 10.3390/s19245457. PMID: 31835743; PMCID: PMC6960719 DOI: https://doi.org/10.3390/s19245457
Pal et. al, “Protocol-based and hybrid access control for the IoT: approaches and research opportunities”, sensors (Basel). 2021 Oct 14;21(20):6832. PMID: 34696053; PMCID: PMC8539538, https://doi.org/10.3390/s21206832 DOI: https://doi.org/10.3390/s21206832
Abowd et. al, “Towards a better understanding of context and context-awareness”, In: Gellersen, HW. (eds) handheld and ubi. Comp. HUC 1999. Lecture notes in comp. sci., vol 1707. Springer, Berlin, Heidelberg. https://doi.org/10.1007/3-540-48157-5_29 DOI: https://doi.org/10.1007/3-540-48157-5_29
Miettinen et. al, “IoT sentinel demo: automated device-type identification for security enforcement in iot”, In K. Lee, & L. Liu (Eds.), 2017 IEEE 37th Intern. Conf. on Distributed Comp. Syst. (ICDSC 2017) (pp. 2511-2514). (Interna. Conf. on Distri. Comp. Sys.). IEEE. https://doi.org/10.1109/ICDCS.2017.284 DOI: https://doi.org/10.1109/ICDCS.2017.284
Yair Meidan et. al, “, “Detection of unauthorized IoT devices using machine learning tech.”, CoRR, vol. abs/1709.04647, 2017, http: //arxiv.org/abs/1709.04647
Bekerman et. al, “Unknown malware detection using network traffic classification.” 2015 IEEE Conf. on Commu. & Network Secu. (CNS) (2015): 134-142. DOI: https://doi.org/10.1109/CNS.2015.7346821
Lopez-Martin et. al, “ (2017), “Network traffic classifier with convolutional and recurrent neural networks for Internet of Things”, IEEE Access. PP. 1-1. 10.1109/ACCESS.2017.2747560 DOI: https://doi.org/10.1109/ACCESS.2017.2747560
HAMZA, Muhammad et al, “Clustering of iot devices using device profiling and behavioral analysis to build efficient network policies”, Mehran University research jour. of engi. and tech., [S.l.], v. 40, n. 2, p. 335 - 345, apr. 2021. ISSN 2413-7219. https://search.informit.org/doi/10.3316/informit.734269516532678 DOI: https://doi.org/10.22581/muet1982.2102.08
Xiaobo Ma et. al, 2021, “Inferring hidden iot devices and user interactions via Spatial-Temporal traffic
fingerprinting”, IEEE/ACM Trans. Netw. 30, 1 (Feb. 2022), 394–408. https://doi.org/10.1109/TNET.2021.3112480 DOI: https://doi.org/10.1109/TNET.2021.3112480
Cvitić, et al, “Ensemble machine learning approach for classification of IoT devices in smart home”, Int. J. mach. Learn. & Cyber. 12, 3179–3202 (2021). https://doi.org/10.1007/s13042-020-01241-0 DOI: https://doi.org/10.1007/s13042-020-01241-0
10 internet of things (IoT) healthcare examples (ordr.net)
Priyanka More, Sachin Sakhare, Payas Sawane, “Implementation and Analysis of ECC (Elliptic Curve Cryptography) security routing protocol in NS2”, IEEE 3rd ASIANCON2023, India, in Press
Priyanka More et. al, “Generating network simulations and comparing different routing protocols using NS2”, IEEE 3rd ASIANCON2023, India, in Press
Priyanka More, Sachin Sakhare, P Mahalle, “Identity Management in Internet of Things: A Survey of State of the Art”, IEEE Sys., Man, and Cybernetics Maga., 10.1109/MSMC.2022.3230215, in Press
Perera et. al, "CA4IOT: Context Awareness for Internet of Things," 2012 IEEE Intern. Conf. on Green Computing and Commu., Besancon, France, 2012, pp. 775-782, doi: 10.1109/GreenCom.2012.128. DOI: https://doi.org/10.1109/GreenCom.2012.128
Gallacher, S et. al, MH 2014, “Dynamic context-aware personalisation in a pervasive environment', Pervasive and Mobile Computing”, vol. 10, no. PART B, pp. 120-137. https://doi.org/10.1016/j.pmcj.2012.11.002 DOI: https://doi.org/10.1016/j.pmcj.2012.11.002
Moradeyo et. al, 2017, “Context-Aware personalization using Neighborhood-Based Context Similarity. Wirel. Pers. Commun. 94, 3 (June 2017), 1595–1618. https://doi.org/10.1007/s11277-016-3701-2 DOI: https://doi.org/10.1007/s11277-016-3701-2
Rajarshi Roy Chowdhury, Azam Che Idris, Pg Emeroylariffion Abas, “Device identification using optimized digital footprints”, IAES Intern. Journal of artificial intelligence (IJ-AI) Vol. 12, No. 1, March 2023, pp. 232~240 ISSN: 2252-8938, DOI: 10.11591/ijai.v12.i1.pp232-240 DOI: https://doi.org/10.11591/ijai.v12.i1.pp232-240
Sivanathan et al., "Characterizing and classifying IoT traffic in smart cities and campuses," 2017 IEEE Confe. on Comp. Commun. Workshops (INFOCOM WKSHPS), Atlanta, GA, USA, 2017, pp. 559-564, doi: 10.1109/INFCOMW.2017.8116438. DOI: https://doi.org/10.1109/INFCOMW.2017.8116438
R. Kumar et. al. “IoT network traffic classification using machine learning algorithms: an experimental analysis," in IEEE IoT , vol. 9, no. 2, pp. 989-1008, 15 Jan.15, 2022, doi: 10.1109/JIOT.2021.3121517. DOI: https://doi.org/10.1109/JIOT.2021.3121517
A. Aksoy, M. H. Gunes, “Automated IoT device identification using network traffic”, in procee. of the 2019, IEEE Intern. Confe. on Comm., ICC 2019, Shanghai, China, 20–24 May 2019; pp. 1–7 DOI: https://doi.org/10.1109/ICC.2019.8761559
H. Jmila, et. al, "A survey of smart home iot device classification using machine learning-based network traffic analysis," in IEEE Access, vol. 10, pp. 97117-97141, 2022, doi: 10.1109/ACCESS.2022.3205023. DOI: https://doi.org/10.1109/ACCESS.2022.3205023
EAI
Downloads
Published
How to Cite
Issue
Section
Categories
License
Copyright (c) 2023 EAI Endorsed Transactions on Industrial Networks and Intelligent Systems
This work is licensed under a Creative Commons Attribution 3.0 Unported License.
This is an open-access article distributed under the terms of the Creative Commons Attribution CC BY 3.0 license, which permits unlimited use, distribution, and reproduction in any medium so long as the original work is properly cited.
