Enhancing Smart Home Security Using Adaptive Access Control with Blockchain and Machine Learning
DOI:
https://doi.org/10.11113/ijic.v16n1-2.697Keywords:
Smart Home Security, Blockchain, Machine Learning, IoT SecurityAbstract
Smart homes, equipped with interconnected IoT devices such as locks, cameras, and sensors, face critical security challenges due to the limitations of static access control mechanisms like Role-Based Access Control (RBAC) and Attribute-Based Access Control (ABAC), which lack adaptability to dynamic, multi-user environments and evolving threats. To address this problem, this research introduces a hybrid Blockchain–Machine Learning (ML) framework that ensures secure, adaptive, and context-aware access control for smart home ecosystems. The proposed system integrates IoT devices with ML algorithms, including Support Vector Machines (SVM) and Neural Networks, to predict user behaviours and dynamically adjust access permissions in real time, while Blockchain ensures immutable, decentralized, and tamper-proof logging of access events. The methodology employed a mixed approach, beginning with an extensive literature review to identify shortcomings in existing static models, followed by system design using smart contracts, caching strategies to reduce latency, and a user perception survey involving 25 participants to validate acceptance and usability. Results demonstrated high user trust and readiness to adopt the proposed system, with 96% of respondents favouring Blockchain-ML-enabled dynamic access control over conventional methods despite concerns about privacy risks, costs, and implementation complexity. This work contributes to society by offering a scalable and intelligent smart home security solution that enhances trust, improves user experience, and strengthens resilience against cyber threats, ultimately supporting safer and smarter living environments.
References
Aldahmani, A., Ouni, B., Lestable, T., & Debbah, M. (2023). Cyber-security of embedded IoTs in smart homes: Challenges, requirements, countermeasures, and trends. IEEE Open Journal of Vehicular Technology, 4, 281–292.
Algarni, S., Eassa, F., Almarhabi, K., Almalaise, A., Albassam, E., Alsubhi, K., & Yamin, M. (2021). Blockchain-based secured access control in an IoT system. Applied Sciences, 11(4), 1772.
Ameer, S., Benson, J., & Sandhu, R. (2023). Hybrid approaches ABAC and RBAC toward secure access control in smart home IoT. IEEE Transactions on Dependable and Secure Computing, 20(5), 4032–4051.
Bagaa, M., Taleb, T., Bernabe, J. B., & Skarmeta, A. (2020). A machine learning security framework for IoT systems. IEEE Access, 8, 114066–114077.
Bhatt, S., Pham, T. K., Gupta, M., Benson, J., Park, J., & Sandhu, R. (2021). Attribute-based access control for AWS Internet of Things and secure industries of the future. IEEE Access, 9, 107200–107223.
Boustia, N., & Mokhtari, A. (2012). A dynamic access control model. Applied Intelligence, 36(1), 190–207.
Ding, S., Cao, J., Li, C., Fan, K., & Li, H. (2019). A novel attribute-based access control scheme using blockchain for IoT. IEEE Access, 7, 38431–38441.
Di Francesco Maesa, D., Mori, P., & Ricci, L. (2017). Blockchain based access control. In L. Y. Chen & H. P. Reiser (Eds.), Distributed Applications and Interoperable Systems (Vol. 10320, pp. 206–220). Springer International Publishing.
Geetha, R., & Thilagam, T. (2021). A review on the effectiveness of machine learning and deep learning algorithms for cyber security. Archives of Computational Methods in Engineering, 28(4), 2861–2879.
Hashmi, I. F., Iqbal, Z., Munir, E., Kryvinska, N., Ivanochko, I., & Sampedro, G. A. (2024). SAAC secure access control management framework for multi-user smart home systems. IEEE Access, 12, 133339–133355.
Houda, Z. A. E., Beaugeard, J., Sauvtre, Q., & Khoukhi, L. (2023). Towards a secure and scalable access control system using blockchain. In 2023 IEEE International Conference on Blockchain and Cryptocurrency (ICBC) (pp. 1–8).
Hussain, H. A., Mansor, Z., Shukur, Z., & Jafar, U. (2024). Cost-optimized dynamic access control policy using blockchain and machine learning for enhanced security in IoT smart homes. ITM Web of Conferences, 63, 01009.
Alam, H., & Tomai, E. (2023). Security attacks and countermeasures in smart homes. International Journal on Cybernetics & Informatics, 12(2), 109–119.
Lee, Y., Rathore, S., Park, J. H., & Park, J. H. (2020). A blockchain-based smart home gateway architecture for preventing data forgery. Human-Centric Computing and Information Sciences, 10(1), 9.
Zhao, L., Liu, S., Li, J., & Xu, H. (2010). A dynamic access control model based on trust. In 2010 The 2nd Conference on Environmental Science and Information Application Technology (pp. 548–551).
Mahlous, A. R. (2023). Threat model and risk management for a smart home IoT system. Informatica, 47(1), 1–7.
Mohammad, Z. N., Farha, F., Abuassba, A. O. M., Yang, S., & Zhou, F. (2021). Access control and authorization in smart homes: A survey. Tsinghua Science and Technology, 26(6), 906–917.
Nassif, A. B., Talib, M. A., Nasir, Q., Albadani, H., & Dakalbab, F. M. (2021). Machine learning for cloud security: A systematic review. IEEE Access, 9, 20717–20735.
Nemec Zlatolas, L., Feher, N., & Hlbl, M. (2022). Security perception of IoT devices in smart homes. Journal of Cybersecurity and Privacy, 2(1), 65–73.
Park, M., Oh, H., & Lee, K. (2019). Security risk measurement for information leakage in IoT-based smart homes from a situational awareness perspective. Sensors, 19(9), 2148.
Rajput, S., Talpur, N., Boudville, R., Abro, G. M. E., Talpur, B., & Zahid, F. (2024). Modernizing home protection: An IoT-driven approach with smart lock and Android application. In 2024 IEEE 14th International Conference on Control System, Computing and Engineering (ICCSCE) (pp. 82–87).
Shaukat, K., Luo, S., Varadharajan, V., Hameed, I. A., & Xu, M. (2020). A survey on machine learning techniques for cyber security in the last decade. IEEE Access, 8, 222310–222354.
Stoyanova, M., Nikoloudakis, Y., Panagiotakis, S., Pallis, E., & Markakis, E. K. (2020). A survey on the Internet of Things (IoT) forensics: Challenges, approaches, and open issues. IEEE Communications Surveys & Tutorials, 22(2), 1191–1221.
Touqeer, H., Zaman, S., Amin, R., Hussain, M., Al-Turjman, F., & Bilal, M. (2021). Smart home security: Challenges, issues and solutions at different IoT layers. The Journal of Supercomputing, 77(12), 14053–14089.
Uddin, M., Islam, S., & Al-Nemrat, A. (2019). A dynamic access control model using authorising workflow and task-role-based access control. IEEE Access, 7, 166676–166689.
Vardakis, G., Hatzivasilis, G., Koutsaki, E., & Papadakis, N. (2024). Review of smart-home security using the Internet of Things. Electronics, 13(16), 3343.
Vojković, G., Milenković, M., & Katulić, T. (2020). IoT and smart home data breach risks from the perspective of data protection and information security law. Business Systems Research Journal, 11(3), 167–185.
Wazid, M., Das, A. K., Chamola, V., & Park, Y. (2022). Uniting cyber security and machine learning: Advantages, challenges and future research. ICT Express, 8(3), 313–321.
Yuan, B., Wan, J., Wu, Y.-H., Zou, D.-Q., & Jin, H. (2023). On the security of smart home systems: A survey. Journal of Computer Science and Technology, 38(2), 228–247.
Ahmed, M. R., & Rahman, M. O. (2024). An enhanced secure user authentication and authorized scheme for smart home management. International Journal of Advanced Computer Science and Applications, 15(6), 58–67.
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