Providing a Method to Improve the Level of Trust in IOT Networks Using Deep Learning Structure

Subject Areas : Artificial Intelligence Tools in Software and Data Engineering

Farshad Kiyoumarsi ^{1 *} , Parham Kiyoumarsi ² , Behzad Zamani ³

1 - Department of Engineering, Faculty of Computer, Shahrekord Branch, Islamic Azad University, Shahrekord, Iran
2 - Department of Engineering, Faculty of Computer, Esfahan University, Esfahan, Iran
3 - Department of Engineering, Faculty of Computer, Shahrekord Branch, Islamic Azad University, Shahrekord, Iran

Received: 2023-12-24 Accepted : 2025-01-22 Published : 2025-02-23

Keywords: Trust Management, IoT Services, Long Short-Term Memory, Multi-Criteria Decision Making,

Abstract :

Recently, the Internet of Things (IoT) technology has been integrated into various aspects of life, including transportation, healthcare, and even education. This technology encompasses intelligent services that enable devices to interact with the physical world and provide appropriate services to users at any time and place. The objective of this research is to propose a model for trust management in IoT devices and services that effectively addresses the existing challenges in this domain. With the remarkable advancement of IoT technology, the number and complexity of attacks have increased significantly. Attackers exploit the heterogeneity of IoT to create trust issues and manipulate device behaviour. Existing trust management techniques face limitations, such as ineffectiveness in handling large volumes of data and adapting to continuously changing behaviours. This research proposes a model that combines the Simple Multi-Attribute Rating Technique (SMART) and the Long Short-Term Memory (LSTM) algorithm. SMART is employed to calculate trust values, while LSTM is utilized to identify behavioural changes based on trust thresholds. The proposed model leverages the integration of SMART and LSTM, where SMART calculates trust values and LSTM identifies and analyses behavioural variations. The effectiveness of the model has been evaluated using metrics such as accuracy, loss rate, precision, recall, and F-measure. Compared to existing deep learning and machine learning methods, the proposed model demonstrates superior performance. With 100 iterations, the model achieved an accuracy of 99.87% and an F-measure of 99.76%.

References:

[1] Mukherjee, S., Kumar, R., Banerjee, S. (2022). Smart Healthcare Remote Monitoring System Using Internet of Things. In: Mukherjee, S., Muppalaneni, N.B., Bhattacharya, S., Pradhan, A.K. (eds) Intelligent Systems for Social Good. Advanced Technologies and Societal Change. Springer, Singapore. https://doi.org/10.1007/978-981-19-0770-8_9

[2] Ruby Dwivedi, Divya Mehrotra, Shaleen Chandra,(2022),Potential of Internet of Medical Things (IoMT) applications in building a smart healthcare system: A systematic review,Journal of Oral Biology and Craniofacial Research,Volume 12, Issue 2,2022,Pages 302-318,ISSN 2212-4268.

[3] Michael Abner, Peter Kok-Yiu Wong, Jack C.P. Cheng,(2022),Battery lifespan enhancement strategies for edge computing-enabled wireless Bluetooth mesh sensor network for structural health monitoring,Automation in Construction,Volume 140,2022,104355,ISSN 0926-5805 .

[4] S. Bajpai, K. Sharma, B.K. Chaurasia, Intrusion detection framework in IoT networks, SN Comput. Sci. 4 (4) (2023) 350.

[5] Farahani B, Firouzi F, Chang V, Badaroglu M, Constant N,Mankodiya K (2021) Towards fog-driven IoTeHealth: promises and challenges of IoT in medicine and healthcare. Futur Gener Comput Syst 78:659–676. https://doi.org/10.1016/j.future.2021.04.036

[6] Azimi I, Anzanpour A, Rahmani AM, Liljeberg P, Salakoski T (2020) Medical warning system based on Internet of Things using fog computing. In: International workshop on big data and information security (IWBIS), pp 19–24. https://doi.org/10.1109/IWBIS.2020.7872884

[7] M. Ibrahim, A. Continella, A. Bianchi, Aot-attack on things: A security analysis of IoT firmware updates, in: 2023 IEEE 8th European Symposium on Security and Privacy (EuroS&P), 2023.

[8] Javadpour, P. Pinto, F. Ja’fari, W. Zhang, DMAIDPS: a distributed multi-agent intrusion detection and prevention system for cloud IoT environments, Cluster Comput. 26 (1) (2023) 367–384.

[9] Sen AAA, Yamin M (2021) Advantages of using fog in IoT applications. Int J Inf Technol 13:829–837. https://doi.org/101007/s41870-020-00514-9

[10] Isa IS, El-Gorashi TE, Musa MO, Elmirghani JM (2020) Energy efficient fog-based healthcare monitoring infrastructure. IEEE Access 8:197828–197852. https://doi.org/10.1109/ACCESS.2020.3033555

[11] M. M. Kamruzzaman, Bingxin Yan, Md Nazirul Islam Sarker,(2022), Blockchain and Fog Computing in IoT-Driven Healthcare Services for Smart Cities, Volume 2022 | Article ID 9957888 | https://doi.org/10.1155/2022/9957888

[12] Yazdinejad, M. Kazemi, R.M. Parizi, A. Dehghantanha, H. Karimipour, An ensemble deep learning model for cyber threat hunting in industrial internet of things, Digit. Commun. Netw. 9 (1) (2023) 101–110.

[13] R.Lakshmana Kumar, Firoz Khan, A Survey on blockchain for industrial Internet of Things,(2022), Volume 61, Issue 8, August 2022, Pages 6001-6022

[14] Alghofaili, Y., & Rassam, M. A. (2024). A Trust Management Model for IoT Devices and Services Based on the Multi-Criteria Decision-Making Approach and Deep Long Short-Term Memory Technique. Sensors, 24(2), 634.

[15] Ghaffari, A., Jelodari, N., Pouralish, S., Derakhshanfard, N., & Arasteh, B. (2024). Securing Internet of Things Using Machine and Deep Learning Methods: A Survey. Cluster Computing, 27, 9065–9089.

[16] Cherbal, S., Zier, A., Hebal, S., Louail, L., & Annane, B. (2024). Security in Internet of Things: A Review on Approaches Based on Blockchain, Machine Learning, Cryptography, and Quantum Computing. The Journal of Supercomputing, 80, 3738–3816.

[17] Jony, A. I., & Arnob, A. K. B. (2024). A Long Short-Term Memory Based Approach for Detecting Cyber Attacks in IoT Using CIC-IoT2023 Dataset. Journal of Edge Computing, 3(1), 28–42.

[18] Tseng, S.-M., Wang, Y.-Q., & Wang, Y.-C. (2024). Multi-Class Intrusion Detection Based on Transformer for IoT Networks Using CIC-IoT-2023 Dataset. Future Internet, 16(8), 284.

[19] Vardhan, A., Kumar, P., & Awasthi, L. K. (2024). A Resilient Intrusion Detection System for IoT Environment Based on a Modified Stacking Ensemble Classifier. SN Computer Science, 5(8).

[20] Pandey, N., & Mishra, P. K. (2025). Conditional Entropy-Based Hybrid DDoS Detection Model for IoT Networks. Computers & Security, 150, 104199.

[21] Gupta, S., Kumar, S., Singh, K., & Saini, D. (2024). Unveiling Anomalies: Harnessing Machine Learning for Detection and Insights. Engineering Research Express, 6(3), 035215.

[22] Morozov, D. S., Yefimenko, A. A., Nikitchuk, T. M., Kolomiiets, R. O., & Semerikov, S. O. (2024). The Sweet Taste of IoT Deception: An Adaptive Honeypot Framework for Design and Evaluation. Journal of Edge Computing, 3(2), 207.

[23] Hodlevskyi, Y., & Vakulenko, O. (2024). Trust Management in IoT Networks: A Deep Learning Approach. IEEE Internet of Things Journal, 11(4), 3456–3467.