Autonomous Security Validation For Embedded Trust Chains In Connected Devices
DOI:
https://doi.org/10.29284/1th3da51Keywords:
Internet of Things, IoT Security, Trust Validation, Intrusion Detection, Machine Learning.Abstract
The rapid expansion of Internet of Things (IoT) technologies has enabled large-scale connectivity among smart devices across domains such as smart homes, industrial automation, healthcare systems, and intelligent monitoring infrastructures. However, the growing number of interconnected devices also introduces significant security challenges due to heterogeneous architectures, limited computational capabilities, and increasing exposure to cyber threats. Malicious activities targeting IoT devices can disrupt communication processes, compromise embedded trust relationships, and threaten the reliability of connected systems. Therefore, effective security validation mechanisms are required to monitor device communication behavior and detect potential compromises within IoT networks. This study proposes an autonomous security validation framework designed to analyze network communication traffic generated by heterogeneous IoT devices and identify malicious activities affecting embedded trust chains. The proposed approach utilizes a data-driven experimental methodology combined with supervised machine learning techniques to learn behavioral patterns associated with both legitimate device communication and multiple cyberattack categories.The framework analyzes communication records generated by several IoT devices and performs multi-class attack classification to detect threats such as backdoor attacks, distributed denial-of-service attacks, injection attacks, password exploitation attempts, ransomware activities, scanning operations, and cross-site scripting attacks. Experimental evaluation demonstrates that the proposed model achieves strong classification performance with high accuracy, precision, recall, and F1-score, indicating reliable detection of malicious device behavior. The results highlight the effectiveness of behavioral traffic analysis and machine learning techniques for strengthening trust validation and improving security monitoring in connected IoT environments.
References
1. Alaba, F. A., Othman, M., Hashem, I. A. T., & Alotaibi, F. (2017). Internet of Things security: A survey. Journal of network and computer applications, 88, 10-28.
2. Al-Fuqaha, A., Guizani, M., Mohammadi, M., Aledhari, M., & Ayyash, M. (2015). Internet of things: A survey on enabling technologies, protocols, and applications. IEEE communications surveys & tutorials, 17(4), 2347-2376.
3. Amanullah, M. A., Habeeb, R. A. A., Nasaruddin, F. H., Gani, A., Ahmed, E., Nainar, A. S. M., ... & Imran, M. (2020). Deep learning and big data technologies for IoT security. Computer Communications, 151, 495-517.
4. Ammar, M., Russello, G., & Crispo, B. (2018). Internet of Things: A survey on the security of IoT frameworks. Journal of information security and Applications, 38, 8-27.
5. Boyes, H., Hallaq, B., Cunningham, J., & Watson, T. (2018). The industrial internet of things (IIoT): An analysis framework. Computers in industry, 101, 1-12.
6. Conti, M., Dehghantanha, A., Franke, K., & Watson, S. (2018). Internet of Things security and forensics: Challenges and opportunities. Future Generation Computer Systems, 78, 544-546.
7. Dhelim, S., Aung, N., Kechadi, M. T., Ning, H., Chen, L., & Lakas, A. (2022). Trust2Vec: Large-scale IoT trust management system based on signed network embeddings. IEEE Internet of Things Journal, 10(1), 553-562.
8. Doshi, R., Apthorpe, N., & Feamster, N. (2018, May). Machine learning ddos detection for consumer internet of things devices. In 2018 IEEE security and privacy workshops (SPW) (pp. 29-35). IEEE.
9. HaddadPajouh, H., Dehghantanha, A., Khayami, R., & Choo, K. K. R. (2018). A deep recurrent neural network based approach for internet of things malware threat hunting. Future Generation Computer Systems, 85, 88-96.
10. Hasan, M., Islam, M. M., Zarif, M. I. I., & Hashem, M. M. A. (2019). Attack and anomaly detection in IoT sensors in IoT sites using machine learning approaches. Internet of Things, 7, 100059.
11. Iwendi, C., Rehman, S. U., Javed, A. R., Khan, S., & Srivastava, G. (2021). Sustainable security for the internet of things using artificial intelligence architectures. ACM Transactions on Internet Technology (TOIT), 21(3), 1-22.
12. Khan, M. A., & Salah, K. (2018). IoT security: Review, blockchain solutions, and open challenges. Future generation computer systems, 82, 395-411.
13. Khraisat, A., Gondal, I., Vamplew, P., & Kamruzzaman, J. (2019). Survey of intrusion detection systems: techniques, datasets and challenges. Cybersecurity, 2(1), 1-22.
14. Lim, K., & Prum, S. (2025, June). Machine Learning Approaches for Anomaly Detection in IoT Networks: A Survey. In 2025 International Conference on Software, Knowledge, Information Management & Applications (SKIMA) (pp. 1-6). IEEE.
15. Lin, H., & Bergmann, N. W. (2016). IoT privacy and security challenges for smart home environments. Information, 7(3), 44.
16. Lu, Y., & Da Xu, L. (2018). Internet of Things (IoT) cybersecurity research: A review of current research topics. IEEE Internet of Things Journal, 6(2), 2103-2115.
17. Meneghello, F., Calore, M., Zucchetto, D., Polese, M., & Zanella, A. (2019). IoT: Internet of threats? A survey of practical security vulnerabilities in real IoT devices. IEEE Internet of Things Journal, 6(5), 8182-8201.
18. Moosavi, S. R., Gia, T. N., Rahmani, A. M., Nigussie, E., Virtanen, S., Isoaho, J., & Tenhunen, H. (2015). SEA: a secure and efficient authentication and authorization architecture for IoT-based healthcare using smart gateways. Procedia Computer Science, 52, 452-459.
19. Mosenia, A., & Jha, N. K. (2016). A comprehensive study of security of internet-of-things. IEEE Transactions on emerging topics in computing, 5(4), 586-602.
20. Moustafa, N., & Slay, J. (2015, November). UNSW-NB15: a comprehensive data set for network intrusion detection systems (UNSW-NB15 network data set). In 2015 military communications and information systems conference (MilCIS) (pp. 1-6). Ieee.
21. Muna, A. H., Moustafa, N., & Sitnikova, E. (2018). Identification of malicious activities in industrial internet of things based on deep learning models. Journal of information security and applications, 41, 1-11.
22. Okporokpo, O., Olajide, F., Ajienka, N., & Ma, X. (2023). Trust-based approaches towards enhancing IoT security: a systematic literature review. arXiv preprint arXiv:2311.11705.
23. Rose, J. R., Swann, M., Bendiab, G., Shiaeles, S., & Kolokotronis, N. (2021, June). Intrusion detection using network traffic profiling and machine learning for IoT. In 2021 IEEE 7th International Conference on Network Softwarization (NetSoft) (pp. 409-415). IEEE.
24. Rose, K., Eldridge, S., & Chapin, L. (2015). The internet of things: An overview. The internet society (ISOC), 80(15), 1-53.
25. Sajid, A., Abbas, H., & Saleem, K. (2016). Cloud-assisted IoT-based SCADA systems security: A review of the state of the art and future challenges. Ieee Access, 4, 1375-1384.
26. Mehta, M.J.B., 2026. AI-DRIVEN TEST ENGINEERING FOR CLOUD-NATIVE SYSTEMS. International Journal of Data Science and IoT Management System, 5(1).
27. Sedjelmaci, H., Senouci, S. M., & Taleb, T. (2017). An accurate security game for low-resource IoT devices. IEEE Transactions on Vehicular Technology, 66(10), 9381-9393.
28. Sfar, A. R., Natalizio, E., Challal, Y., & Chtourou, Z. (2018). A roadmap for security challenges in the Internet of Things. Digital Communications and Networks, 4(2), 118-137.
29. Sicari, S., Rizzardi, A., Grieco, L. A., & Coen-Porisini, A. (2015). Security, privacy and trust in Internet of Things: The road ahead. Computer networks, 76, 146-164.
30. Soula, M., Mbarek, B., Meddeb, A., & Pitner, T. (2023, March). A survey of intrusion detection-based trust management approaches in IoT networks. In International Conference on Advanced Information Networking and Applications (pp. 504-517). Cham: Springer International Publishing.
31. Sun, X., & Ansari, N. (2016). EdgeIoT: Mobile edge computing for the Internet of Things. IEEE Communications Magazine, 54(12), 22-29.
32. UNSW-NB15: A comprehensive data set for network intrusion detection systems (UNSW-NB15 network data set). 2015 Military Communications and Information Systems Conference (MilCIS), 1–6. IEEE. https://doi.org/10.1109/MilCIS.2015.7348942
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This work is licensed under a Creative Commons Attribution 4.0 International License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.
