International Journal of Computer Networks and Applications

Open Access Open Access  Restricted Access Subscription Access

A Novel Trust Negotiation Protocol for Analysing and Approving IoT Edge Computing Devices Using Machine Learning Algorithm


Affiliations
1 Department of Computer Science Engineering, Sathyabama Institute of Science and Technology, Jeppiaar Nagar, Chennai, Tamil Nadu,, India
2 Department of Computer Science and Engineering, Sathyabama Institute of Science and Technology, Jeppiaar Nagar, Chennai, Tamil Nadu,, India
 

In this paper, we come up with an effective approach for the management of security using machine learning, and we derive a solution for problems with privacy and security in Internet of Things devices. Recent apps' connections to numerous IoT devices, use of edge computing, and use of fog computing cause numerous DDoS attacks to be launched against the servers of the dynamic network. For computing on the edge of the Internet of Things, the upgraded Trust Negotiation Protocol is used, making use of better period data. The application of security management is used to maintain the automation, minimize the risk level, and reduce the complexity of the system. The fundamental objective of this system is to enable user-level security in all edge computing devices related to the Internet of Things. Using Machine Learning techniques, a proposed model is utilized to develop a secure environment for E2E IoT security at the user level. A low-cost solution is obtained using machine-learning-based security management techniques. The Enhanced Trust Negotiation Protocol is simulated, and the experiment results demonstrate that the suggested model is superior to the current one in terms of the efficiency with which security management approaches may be implemented.

Keywords

Secured IoT, IoT Network, Security Algorithm, Trust Protocol, Edge Computing, MLA (Machine Learning Algorithm).
User
Notifications
Font Size

  • Tawalbeh L, Muheidat F, Tawalbeh M, Quwaider M. IoT Privacy and Security: Challenges and Solutions. Applied Sciences. 2020; 10(12):4102. https://doi.org/10.3390/app10124102.

  • Carsten Maple (2017) Security and privacy in the Internet of things, Journal of Cyber Policy, 2:2, 155- 184, DOI: 10.1080/23738871.2017.1366536.

  • Mouha, R. (2021) Internet of Things (IoT). Journal of Data Analysis and Information Processing, 9, 77-101. doi: 10.4236/jdaip.2021.92006.

  • A. Al-Fuqaha, M. Guizani, M. Mohammadi, M. Aledhari, and M.Ayyash, “Internet of Things: A survey on enabling technologies, protocols, and applications,'' IEEE Commun. Surveys Tuts., vol. 17, no. 4, pp. 2347_2376, 4th Quart., 2015.

  • Khvoynitskaya, S. The History and Future of the Internet of Things. 2020. Available online: https://www.itransition.com/:https://www.itransition.com/blog/iothistory (accessed on 25 March 2020).

  • Conti, M.; Dehghantanha, A.; Franke, K.; Watson, S. Internet of Things security and forensics: Challengesand opportunities. Future Gener. Comput. Syst. 2018, 78, 544–546. [CrossRef]

  • Monther, A.A.; Tawalbeh, L. Security techniques for intelligent spam sensing and anomaly detection in online social platforms. Int. J. Electr. Comput. Eng. 2020, 10, 2088–8708.

  • Makhdoom, I.; Abolhasan, M.; Lipman, J.; Liu, R.P.; Ni, W. Anatomy of threats to the Internet of things.IEEECommun. Surv. Tutor. 2018, 21, 1636–1675. [CrossRef]

  • Meng, Y.; Zhang, W.; Zhu, H.; Shen, X.S. Securing consumer IoT in the smart home: Architecture, challenges,and countermeasures. IEEE Wirel. Commun. 2018, 25, 53–59. [CrossRef]

  • Siby, S.; Maiti, R.R.; Tippenhauer, N.O. Iotscanner: Detecting privacy threats in IoT neighborhoods. In Proceedings of the 3rd ACM International Workshop on IoT Privacy, Trust, and Security, Abu Dhabi United Arab Emirates, 2 April 2017; pp. 23–30.

  • Hassan, W.H. Current research on Internet of Things (IoT) security: A survey. Comput. Netw. 2019, 148, 283–294.

  • Balamurugan, S., Ayyasamy, A., Suresh Joseph, K., (2021), IoTBlockchain driven traceability techniques for improved safety measures in food supply chain. International journal of information technology, https://doi.org/10.1007/s41870-020-0058.].

  • Liu, X.; Zhao, M.; Li, S.; Zhang, F.; Trappe, W. A security framework for the internet of things in the future internet architecture. Future Internet 2017, 9, 27. [CrossRef]

  • Ali, S.; Bosche, A.; Ford, F. Cybersecurity Is the Key to Unlocking Demand in the Internet of Things; Bain and Company: Boston, MA, USA, 2018.

  • Antos, M.A., Munoz, R., Olivares, R., RebouçasFilho, P. P., Del Ser, J. and de Albuquerque, V.H.C. (2020). Online heart monitoring systems on the internet of health things environments: A survey, a reference model and an outlook. Information Fusion 53, 222–239.

  • Culbert, D. Personal Data Breaches and Securing IoT Devices. 2020. Available online: https://betanews.com/ 2019/08/13/securing-iotdevices/ (accessed on 15 September 2019).

  • Gemalto. Securing the IoT-Building Trust in IoT Devices and Data. 2020. Available online: https://www.gemalto.com/:https://www.gemalto.com/iot/iot-security. (accessed on 17 February 2020).

  • He, H.; Maple, C.; Watson, T.; Tiwari, A.; Mehnen, J.; Jin, Y.; Gabrys, B. The security challenges in the IoT enabled cyber-physical systems and opportunities for evolutionary computing & other computational intelligence. In Proceedings of the Evolutionary Computation (CEC), Vancouver, BC, Canada, 24–29 July 2016; pp. 1015–1021.

  • Boursianis, A.D., Papadopoulou, M.S. and Diamantoulakisetal, P. (2020), Internet of Things (IoT) and Agricultural Unmanned Aerial Vehicles (UAVs) in smart farming: A comprehensive review, Internet of Things, https://doi.org/10.1016/j.iot.2020.100187.

  • Estrada, D.; Tawalbeh, L.; Vinaja, R. How Secure Having IoT Devices in Our Home. J. Inf. Secur. 2020, 11. [CrossRef]

  • Sun, Y.; Song, H.; Jara, A.J.; Bie, R. Internet of Things and Big Data Analytics for Smart and Connected Communities. 2016. Available online:https://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=740 6686(accessed on 4 April 2020).

  • Tawalbeh, M.; Quwaider, M.; Tawalbeh, L.A. Authorization Model for IoT Healthcare Systems: Case Study. In Proceedings of the 2020 11th International Conference on Information and Communication Systems (ICICS), Irbid, Jordan, 7–9 April 2020; pp. 337–342. [CrossRef]

  • Balamurugan, S., Ayyasamy, A., Suresh Joseph, K., (2020), Enhanced Petri Nets for Traceability of Food Management using Internet of Things, Peer-to-Peer Networking and Applications..

  • Wei, X., Zhipeng, Z., Hongxun, W., Yang, Y. and Yanpeng, Z. (2020), Optimization of monitoring network system for Eco safety on Internet of Things platform and environmental food supply chain, Computer Communications, Volume 151, 2020, Pages 320-330, ISSN 0140- 3664, https://doi.org/10.1016/j.comcom.2019.12.033.

  • The HIPAA Privacy Rule. Available online: https://www.hhs.gov/hipaa/for-professionals/privacy/index.html (accessed on 19 October 2019).

  • https://docs.aws.amazon.com/iot/latest/developerguide/what-is-awsiot.html (accessed on 17 March 2020) [27] Xiao, L, X. Wan, X. Lu, Y. Zhang and D. Wu, 2018. IoT security techniques based on machine learning. Cryptography Secur. 1: 1-20.

  • Chen, F., P. Deng, J. Wan, D. Zhang and A. V. Vasilakos et al., 2015. Data mining for the internet of things: Literature review and challenges. Intl. J. Distrib. Sens. Networks, 2015: 1-14.

  • M. Trebar, A. Grah, A. A. Melcon, and A. Parreno, “Towards RFID traceability systems of farmed fish supply chain,” in Proceedings of the 19th International Conference on Software, Telecommunications and Computer Networks (SoftCOM’11), pp. 6–11, Hvar, Croatia, September 2011.

  • T. T. L. Tran, L. Peng, Y. Diao, A. McGregor, and A. Liu, “CLARO: modeling and processing uncertain data streams,” VLDB Journal, vol. 21, no. 5, pp. 651–676, 2012


Abstract Views: 257

PDF Views: 1




  • A Novel Trust Negotiation Protocol for Analysing and Approving IoT Edge Computing Devices Using Machine Learning Algorithm

Abstract Views: 257  |  PDF Views: 1

Authors

V. Maruthi Prasad
Department of Computer Science Engineering, Sathyabama Institute of Science and Technology, Jeppiaar Nagar, Chennai, Tamil Nadu,, India
B. Bharathi
Department of Computer Science and Engineering, Sathyabama Institute of Science and Technology, Jeppiaar Nagar, Chennai, Tamil Nadu,, India

Abstract


In this paper, we come up with an effective approach for the management of security using machine learning, and we derive a solution for problems with privacy and security in Internet of Things devices. Recent apps' connections to numerous IoT devices, use of edge computing, and use of fog computing cause numerous DDoS attacks to be launched against the servers of the dynamic network. For computing on the edge of the Internet of Things, the upgraded Trust Negotiation Protocol is used, making use of better period data. The application of security management is used to maintain the automation, minimize the risk level, and reduce the complexity of the system. The fundamental objective of this system is to enable user-level security in all edge computing devices related to the Internet of Things. Using Machine Learning techniques, a proposed model is utilized to develop a secure environment for E2E IoT security at the user level. A low-cost solution is obtained using machine-learning-based security management techniques. The Enhanced Trust Negotiation Protocol is simulated, and the experiment results demonstrate that the suggested model is superior to the current one in terms of the efficiency with which security management approaches may be implemented.

Keywords


Secured IoT, IoT Network, Security Algorithm, Trust Protocol, Edge Computing, MLA (Machine Learning Algorithm).

References





DOI: https://doi.org/10.22247/ijcna%2F2022%2F217704