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Trust-based support vector regressive (TSVR) security mechanism to identify malicious nodes in the Internet of Battlefield Things (IoBT)

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Abstract

The Internet of Things is an advanced and prominent wireless technology that enables our daily lives easier and more comfortable. The same way it is getting more attention in the military environment and the result of the application is Internet of Battle Field Things (IoBT). IoBT offers many advantages to the battle field environment and at the same time it is getting attention from a security perspective. The battle field things are affected by various attacks because of their significant nature among black hole attack is one of the considerable attacks that affects the IoBT environment more seriously. It is caused by lack of authentication. The battle field things are communicating with other battlefield things without any prior interactions and communications. This blindness leads to security violations. To overcome these issues, in this paper Trust based Support Vector Regression has been proposed. The aim is to detect and eliminate black hole attacks by the way authentication can be ensured. The proposed method makes use of multiple trust metrics to evaluate the trustworthiness of particular IoBT things. Besides, the machine learning algorithm, called Support Vector Regression, is used to classify the black hole nodes and also it predicts the future behaviors of the battle field things. The proposed model has been analyzed in terms of simulation results and this model has been compared with traditional Routing Protocol for Low Power Lossy Network and existing similar model. The proposed model shows prominent results compared with other two models.

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References

  • Airehrour D, Gutierrez JA, Ray SK (2019) SecTrust-RPL: a secure trust-aware RPL routing protocol for Internet of Things. Futur Gener Comput Syst 93:860–876. https://doi.org/10.1016/j.future.2018.03.021

    Article  Google Scholar 

  • Ashwini Telang (2014) RPL objective function & simulation using DGRM model in cooja from http://anrg.usc.edu/contiki/index.php/RPL_objective_function_%25simulation_using_DGRM_model_in_cooja

  • Dunkels A, Eriksson J, Finne N, Tsiftes N (2011) Powertrace: network-level power profiling for low-power wireless networks. SICS Techn Rep T2011(05):1–14

    Google Scholar 

  • Gali S, Venkatram N (2022) Energy-efficient cluster-based trust-aware routing for Internet of Things. Expert clouds and applications, pp 493–509

  • Hammi MT, Hammi B, Bellot P, Serhrouchni A (2018) Bubbles of trust: a decentralized blockchain-based authentication system for IoT. Comput Secur 78:126–142

    Article  Google Scholar 

  • Khan ZA, Ullrich J, Voyiatzis AG, Herrmann P (2017) A Trust-based resilient routing mechanism for the Internet of Things. In: Proceedings of the 12th international conference on availability, reliability and security (ARES '17). Association for computing machinery, New York, Article 27, pp 1–6. https://doi.org/10.1145/3098954.3098963.

  • Liang W, Long J, Weng T-H, Chen X, Li K-C, Zomaya AY (2018) TBRS: a trust based recommendation scheme for vehicular CPS network. Futur Gener Comput Syst. https://doi.org/10.1016/j.future.2018.09.002

    Article  Google Scholar 

  • Liu L, Ma Z, Meng W (2019) Detection of multiple-mix-attack malicious nodes using perceptron-based trust in IoT networks. Fut Gener Comput Syst 101:865–879

    Article  Google Scholar 

  • Lshehri MD, Hussain FK (2015) A Comparative analysis of scalable and context-aware trust management approaches for Internet of Things. Lect Notes Comput Sci. https://doi.org/10.1007/978-3-319-26561-2_70

    Article  Google Scholar 

  • Medaglia CM, Serbanati A (2010) An overview of privacy and security issues in the Internet of Things. Internet of Things. https://doi.org/10.1007/978-1-4419-1674-7_382

    Article  Google Scholar 

  • Meena U, Sharma P (2022) Secret dynamic key authentication and decision trust secure routing framework for internet of things based WSN. Wirel Pers Commun:1–29

  • Online document T. Winter (2012) RPL: IPv6 routing protocol for low-power and lossy networks. https://tools.ietf.org/html/rfc6550. Accessed 10 March 2022

  • Prathapchandran K, Janani T (2021) A trust-based security model to detect misbehaving nodes in Internet of Things (IoT) environment using logistic regression. Journal of physics: conference series, p 1850.

  • Rahamathullah U, Karthikeyan EA (2021) Lightweight trust-based system to ensure security on the Internet of Battlefield Things (IoBT) environment. Int J Syst Assur Eng Manag. https://doi.org/10.1007/s13198-021-01250-4

    Article  Google Scholar 

  • Selvaraj S, Thangarajan R, Saravanan M (2022) Trust-based and optimized RPL routing in social Internet of Things Network. Mobile computing and sustainable informatics, pp 513–529

  • Sharma A, Pilli ES, Mazumdar AP, Govil MC (2016) A framework to manage trust in internet of Things. In: 2016 international conference on emerging trends in communication technologies (ETCT). https://doi.org/10.1109/etct.2016.7882970

  • Tan L, Wang N (2010) Future internet: The Internet of Things. In: 2010 3rd International Conference on Advanced Computer Theory and Engineering (ICACTE). https://doi.org/10.1109/icacte.2010.5579543

  • Theobald O (2021) Machine learning for absolute beginners: a plain english introduction. Scatterplot Press. 2nd edition

  • ul Hassan T, Asim M, Baker T, Hassan J, Tariq N (2021) CTrust-RPL: a control layer-based trust mechanism for supporting secure routing in routing protocol for low power and lossy networks-based Internet of Things applications. Trans Emerg Tel Tech. 32:e4224. https://doi.org/10.1002/ett.4224

    Article  Google Scholar 

  • Venkanna U, Velusamy RL (2011) Black hole attack and their counter measure based on trust management in manet. A survey. In: 3rd international conference on advances in recent technologies in communication and computing (ARTCom 2011), 2011, pp 232–236, https://doi.org/10.1049/ic.2011.0087

  • Wang KH, Chen CM, Fang W, Wu TY (2017) A secure authentication scheme for internet of things. Pervasive Mob Comput 42:15–26

    Article  Google Scholar 

  • Witten IH, Frank E, Hall MA, Christopher Pal. (2016) Data mining: practical machine learning tools and techniques (Morgan Kaufmann Series in Data Management Systems), 4th Edition, Morgan Kaufmann; 4th edition

  • Zahra SR, Chishti MA (2020) Fuzzy logic and fog based secure architecture for internet of things (flfsiot). J Ambient Intell Humaniz Comput:1–25

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Authors and Affiliations

  1. Department of Computer Applications, Karpagam Academy of Higher Education, Coimbatore, 641021, Tamilnadu, India

    P. Rutravigneshwaran & G. Anitha

  2. Department of Computer Applications, Nehru Arts and Science College (Autonomous), Coimbatore, 641105, Tamilnadu, India

    K. Prathapchandran

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  1. P. Rutravigneshwaran
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  2. G. Anitha
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  3. K. Prathapchandran
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Correspondence to K. Prathapchandran.

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Rutravigneshwaran, P., Anitha, G. & Prathapchandran, K. Trust-based support vector regressive (TSVR) security mechanism to identify malicious nodes in the Internet of Battlefield Things (IoBT). Int J Syst Assur Eng Manag 15, 287–299 (2024). https://doi.org/10.1007/s13198-022-01719-w

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  • DOI: https://doi.org/10.1007/s13198-022-01719-w

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