A Semantic Ontology-Based Model by Ensemble Learning For Secure Attribute-Based Encryption in Fog-Enabled Smart Homes
محورهای موضوعی : پردازش چند رسانه ای، سیستمهای ارتباطی، سیستمهای هوشمند
Ronita Rezapour
1
,
Parvaneh Asghari
2
*
,
Hamid Haj Seyyed Javadi
3
,
Shamsollah Ghanbari
4
1 - PhD Student, Department of Computer Engineering, Q.C., Islamic Azad University, Qom, Iran
2 - Assistant Professor, Department of Computer Engineering,CT.C, Islamic Azad University, Tehran, Iran
3 - Professor, Department of Computer Engineering, Shahed University, Tehran, Iran
4 - Assistant Professor, Department of Computer Engineering, ASH.C., Islamic Azad University, Ashtian, Iran
کلید واژه: رمز نگاری مبتنی بر ویژگی, یادگیری جمعی, شبکه عصبی عمیق, آنتولوژی, ساختمان های هوشمند مبتنی بر محاسبات مه,
چکیده مقاله :
|
Fog computing empowers resource-limited applications by bringing cloud computing close to the network periphery, effectively limiting latency, improving efficiency, and assuring better resource management. Security challenges, however, restrict widespread adoption in practice, necessitating cryptographic mechanisms with low computational overheads. One well-known approach for data sharing in a secure way is Ciphertext-Policy Attribute-Based Encryption (CP-ABE), which provides a way of access control based on attributes. However, the high execution time and storage requirements of CP-ABE, due to the diversity of attributes in secret keys and access structures, limit its practicality in resource-constrained environments. In response to these problems, this paper presents a hybrid semantic model consisting of an outsourced CP-ABE with attribute revocation and an optimized AES algorithm based on ensemble learning. The model employs classifiers such as GMDH, KNN, and SVM to identify attributes relevant to CP-ABE. Additionally, the Dragonfly optimization algorithm and ontology-based semantic techniques enhance the efficiency of feature selection. With experimental analysis on five smart building datasets, the prediction performance of this model outperforms existing methods. The times of encryption, decryption, and attribute revocation decreased to 2.99 ms, 2.86 ms, and 18.6 ms. The growth of storage for secret keys and access structures was reduced to 13.56 KB and 10.4 KB, which made its use more efficient and secure. Overall, the results indicate that the model improves data security and minimizes computational overhead, leading to a more feasible implementation of CP-ABE for fog computing scenarios. |
|
Fog computing empowers resource-limited applications by bringing cloud computing close to the network periphery, effectively limiting latency, improving efficiency, and assuring better resource management. Security challenges, however, restrict widespread adoption in practice, necessitating cryptographic mechanisms with low computational overheads. One well-known approach for data sharing in a secure way is Ciphertext-Policy Attribute-Based Encryption (CP-ABE), which provides a way of access control based on attributes. However, the high execution time and storage requirements of CP-ABE, due to the diversity of attributes in secret keys and access structures, limit its practicality in resource-constrained environments. In response to these problems, this paper presents a hybrid semantic model consisting of an outsourced CP-ABE with attribute revocation and an optimized AES algorithm based on ensemble learning. The model employs classifiers such as GMDH, KNN, and SVM to identify attributes relevant to CP-ABE. Additionally, the Dragonfly optimization algorithm and ontology-based semantic techniques enhance the efficiency of feature selection. With experimental analysis on five smart building datasets, the prediction performance of this model outperforms existing methods. The times of encryption, decryption, and attribute revocation decreased to 2.99 ms, 2.86 ms, and 18.6 ms. The growth of storage for secret keys and access structures was reduced to 13.56 KB and 10.4 KB, which made its use more efficient and secure. Overall, the results indicate that the model improves data security and minimizes computational overhead, leading to a more feasible implementation of CP-ABE for fog computing scenarios. |
1. Miraz, M.H., Ali, M., Excell, P.S. and Picking, R., 2015. A review on Internet of Things (IoT), Internet of everything (IoE) and Internet of nano things (IoNT). 2015 Internet Technologies and Applications (ITA), pp.219-224.
2. Rasori, M., La Manna, M., Perazzo, P. and Dini, G., 2022. A survey on attribute-based encryption schemes suitable for the internet of things. IEEE Internet of Things Journal, 9(11), pp.8269-8290.
3. Li, H. and Jing, T., 2020. A ciphertext-policy attribute-based encryption scheme with public verification for an IoT-fog-cloud architecture. Procedia Computer Science, 174, pp.243-251.
4. Sethi, K., Pradhan, A. and Bera, P., 2021. PMTER-ABE: a practical multi-authority CP-ABE with traceability, revocation and outsourcing decryption for secure access control in cloud systems. Cluster Computing, 24, pp.1525-1550.
5. Khalid, T., Abbasi, M.A.K., Zuraiz, M., Khan, A.N., Ali, M., Ahmad, R.W., Rodrigues, J.J. and Aslam, M., 2021. A survey on privacy and access control schemes in fog computing. International Journal of Communication Systems, 34(2), p.e4181.
6. Asghari, P., Rahmani, A.M. and Javadi, H.H.S., 2019. Internet of Things applications: A systematic review. Computer Networks, 148, pp.241-261.
7. Sarma, R., Kumar, C. and Barbhuiya, F.A., 2022. MACFI: A multi-authority access control scheme with efficient ciphertext and secret key size for fog-enhanced IoT. Journal of Systems Architecture, 123, p.102347.
8. Wen, M., Chen, S., Lu, R., Li, B. and Chen, S., 2019. Security and efficiency enhanced revocable access control for fog-based smart grid system. IEEE Access, 7, pp.137968-137981.
9. Zhao, J., Zeng, P. and Choo, K.K.R., 2021. An efficient access control scheme with outsourcing and attribute revocation for fog-enabled E-health. IEEE Access, 9, pp.13789-13799.
10. Kazemi, M., Ghanbari, S. and Kazemi, M., 2020. Divisible load framework and close form for scheduling in fog computing systems. In Recent Advances on Soft Computing and Data Mining: Proceedings of the Fourth International Conference on Soft Computing and Data Mining (SCDM 2020), Melaka, Malaysia, January 22– 23, 2020 (pp. 323-333). Springer International Publishing.
11. Saidi, A., Nouali, O. and Amira, A., 2022. SHARE-ABE: an efficient and secure data sharing framework based on ciphertext-policy attribute-based encryption and Fog computing. Cluster Computing, 25(1), pp.167-185.
12. Luo, F., Al-Kuwari, S., Wang, F. and Chen, K., 2021. Attribute-based proxy re-encryption from standard lattices. Theoretical Computer Science, 865, pp.52-62.
13. Wu, Z., Shi, R.H., Li, K. and Yang, Y., 2022. Attribute-based data access control scheme with secure revocation in fog computing for smart grid. Cluster Computing, 25(6), pp.3899-3913.
14. Xin, X., Yang, Q. and Li, F., 2020. Quantum public-key signature scheme based on asymmetric quantum encryption with trapdoor information. Quantum Information Processing, 19(8), p.233.
15. Tu, S., Waqas, M., Huang, F., Abbas, G. and Abbas, Z.H., 2021. A revocable and outsourced multi-authority attribute-based encryption scheme in fog computing. Computer Networks, 195, p.108196.
16. Sarma, R., Kumar, C. and Barbhuiya, F.A., 2022. MACFI: A multi-authority access control scheme with efficient ciphertext and secret key size for fog-enhanced IoT. Journal of Systems Architecture, 123, p.102347.
17. Fu, X., Ding, Y., Li, H., Ning, J., Wu, T. and Li, F., 2022. A survey of lattice based expressive attribute based encryption. Computer Science Review, 43, p.100438.
18. Dixit, S., Joshi, K.P., Choi, S.G. and Elluri, L., 2022, May. Semantically rich access control in cloud ehr systems based on ma-abe. In 2022 IEEE 8th intl conference on big data security on cloud (BigDataSecurity), IEEE intl conference on high performance and smart computing,(HPSC) and IEEE intl conference on intelligent data and security (IDS) (pp. 1-10). IEEE.
19. Maleki, N., Zeinali, Y. and Niaki, S.T.A., 2021. A k-NN method for lung cancer prognosis with the use of a genetic algorithm for feature selection. Expert Systems with Applications, 164, p.113981.
20. Hazman, C., Benkirane, S., Guezzaz, A., Azrour, M. and Abdedaime, M., 2022, November. Intrusion detection framework for IoT-based smart environments security. In The International Conference on Artificial Intelligence and Smart Environment (pp. 546-552). Cham: Springer International Publishing.
21. Panthi, M. and Das, T.K., 2022. Intelligent intrusion detection scheme for smart power-grid using optimized ensemble learning on selected features. International Journal of Critical Infrastructure Protection, 39, p.100567.
22. Rahman, H. and Hussain, M.I., 2021. A light-weight dynamic ontology for Internet of Things using machine learning technique. ICT Express, 7(3), pp.355-360.
23. Saba, D., Sahli, Y. and Hadidi, A., 2021. An ontology based energy management for smart home. Sustainable Computing: Informatics and Systems, 31, p.100591.
24. Khan, P.W. and Byun, Y.C., 2020. Genetic algorithm based optimized feature engineering and hybrid machine learning for effective energy consumption prediction. Ieee Access, 8, pp.196274-196286.
25. Tasci, E., 2020. Voting combinations-based ensemble of fine-tuned convolutional neural networks for food image recognition. Multimedia Tools and Applications, 79(41), pp.30397-30418.
26. Jethanandani, M., Sharma, A., Perumal, T. and Chang, J.R., 2020. Multi-label classification based ensemble learning for human activity recognition in smart home. Internet of Things, 12, p.100324.
27. Beimel, A., 1996. Secure schemes for secret sharing and key distribution. PhD thesis, Israel Institute of Technology, Technion.
28. Waters, B., 2011, March. Ciphertext-policy attribute-based encryption: An expressive, efficient, and provably secure realization. In International workshop on public key cryptography (pp. 53-70). Berlin, Heidelberg: Springer Berlin Heidelberg.
29. Xia, Q., Gao, J., Obiri, I.A., Asamoah, K.O. and Worae, D.A., 2024. Selective Secure ABE Schemes Based on Prime Order Group.
30. Hosseini, S. and Seilani, H., 2021. Anomaly process detection using negative selection algorithm and classification techniques. Evolving Systems, 12(3), pp.769-778.
31. Borlea, I.D., Precup, R.E. and Borlea, A.B., 2022. Improvement of K-means cluster quality by post processing resulted clusters. Procedia Computer Science, 199, pp.63-70.
32. Abualigah, L., 2020. Multi-verse optimizer algorithm: a comprehensive survey of its results, variants, and applications. Neural Computing and Applications, 32(16), pp.12381-12401.
33. Emambocus, B.A.S., Jasser, M.B., Mustapha, A. and Amphawan, A., 2021. Dragonfly algorithm and its hybrids: A survey on performance, objectives and applications. Sensors, 21(22), p.7542.
34. Mirjalili, S., 2016. Dragonfly algorithm: a new meta-heuristic optimization technique for solving single-objective, discrete, and multi-objective problems. Neural computing and applications, 27, pp.1053-1073.
35. Alshinwan, M., Abualigah, L., Shehab, M., Elaziz, M.A., Khasawneh, A.M., Alabool, H. and Hamad, H.A., 2021. Dragonfly algorithm: a comprehensive survey of its results, variants, and applications. Multimedia Tools and Applications, 80, pp.14979-15016.
36. Babcock, S., Beverley, J., Cowell, L.G. and Smith, B., 2021. The infectious disease ontology in the age of COVID-19. Journal of biomedical semantics, 12, pp.1-20.
37. Zhou, D., Zhou, B., Zheng, Z., Kostylev, E.V., Cheng, G., Jimenez-Ruiz, E., Soylu, A. and Kharlamov, E., 2022, May. Enhancing knowledge graph generation with ontology reshaping–Bosch case. In European Semantic Web Conference (pp. 299-302). Cham: Springer International Publishing.
38. Ma, R., Li, Q., Zhang, B., Huang, H. and Yang, C., 2024. An ontology-driven method for urban building energy modeling. Sustainable Cities and Society, 106, p.105394.
39. Haykin, S., 1998. Neural networks: a comprehensive foundation. Prentice Hall PTR.
40. Farlow, S.J., 1984. Self-organizing Methods in Modeling (Statistics: Textbooks and Monographs, vol. 54).
41. Nariman-Zadeh, N., Darvizeh, A. and Ahmad-Zadeh, G.R., 2003. Hybrid genetic design of GMDH-type neural networks using singular value decomposition for modelling and prediction of the explosive cutting process. Proceedings of the Institution of Mechanical Engineers, Part B: Journal of Engineering Manufacture, 217(6), pp.779-790.
42. Li, D., Armaghani, D.J., Zhou, J., Lai, S.H. and Hasanipanah, M., 2020. A GMDH predictive model to predict rock material strength using three non-destructive tests. Journal of Nondestructive Evaluation, 39, pp.1-14.
43. Li, D., Moghaddam, M.R., Monjezi, M., Jahed Armaghani, D. and Mehrdanesh, A., 2020. Development of a group method of data handling technique to forecast iron ore price. Applied Sciences, 10(7), p.2364.
44. Koopialipoor, M., Nikouei, S.S., Marto, A., Fahimifar, A., Jahed Armaghani, D. and Mohamad, E.T., 2019. Predicting tunnel boring machine performance through a new model based on the group method of data handling. Bulletin of Engineering Geology and the Environment, 78, pp.3799-3813.
45. Armaghani, D.J., Momeni, E. and Asteris, P.G., 2020. Application of group method of data handling technique in assessing deformation of rock mass. 1, 1(1), p.001.
46. Wazirali, R., 2020. An improved intrusion detection system based on KNN hyperparameter tuning and cross-validation. Arabian Journal for Science and Engineering, 45(12), pp.10859-10873.
47. Ahakonye, L.A.C., Nwakanma, C.I., Lee, J.M. and Kim, D.S., 2021. Efficient classification of enciphered SCADA network traffic in smart factory using decision tree algorithm. IEEE Access, 9, pp.154892-154901.
48. Deng, Z., Zhu, X., Cheng, D., Zong, M. and Zhang, S., 2016. Efficient kNN classification algorithm for big data. Neurocomputing, 195, pp.143-148.
49. Izmailov, R., Vapnik, V. and Vashist, A., 2013, August. Multidimensional splines with infinite number of knots as SVM kernels. In The 2013 International Joint Conference on Neural Networks (IJCNN) (pp. 1-7). IEEE.
50. Kotsiantis, S.B., Zaharakis, I. and Pintelas, P., 2007. Supervised machine learning: A review of classification techniques. Emerging artificial intelligence applications in computer engineering, 160(1), pp.3-24.
51. Mustaqeem, M. and Saqib, M., 2021. Principal component based support vector machine (PC-SVM): a hybrid technique for software defect detection. Cluster Computing, 24(3), pp.2581-2595.
52. Tuttle, J.F., Blackburn, L.D. and Powell, K.M., 2020. On-line classification of coal combustion quality using nonlinear SVM for improved neural network NOx emission rate prediction. Computers & chemical engineering, 141, p.106990.
53. Zang, C. and Ma, Y., 2012. Ensemble machine learning: Methods and application. Springer publication, DOI, 10, pp.978-1.
54. Sun, X. and Qourbani, A., 2023. Combining ensemble classification and integrated filter-evolutionary search for breast cancer diagnosis. Journal of Cancer Research and Clinical Oncology, 149(12), pp.10753-10769.
55. Tripathi, K., Khan, F.A., Khanday, A.M.U.D. and Nisa, K.U., 2023. The classification of medical and botanical data through majority voting using artificial neural network. International Journal of Information Technology, 15(6), pp.3271-3283.
56. Liang, X., Cao, Z., Lin, H. and Shao, J., 2009, March. Attribute based proxy re-encryption with delegating capabilities. In Proceedings of the 4th international symposium on information, computer, and communications security (pp. 276-286).
57. https://www.kaggle.com/code/offmann/smart-home-dataset/data.
58. https://www.kaggle.com/datasets/ranakrc/smart-building-system.
59. https://www.kaggle.com/datasets/claytonmiller/open-smart-home-iotieqenergy-data?resource=download&select=Measurements.
60. https://www.kaggle.com/datasets/claytonmiller/cubems-smart-building-energy-and-iaq-data.
61. https://www.kaggle.com/datasets/claytonmiller/open-smart-home-iotieqenergy-data
62. Melamed, I.D., Green, R. and Turian, J., 2003. Precision and recall of machine translation. In Companion volume of the proceedings of HLT-NAACL 2003-short papers (pp. 61-63).