1
Research Scholar, Amity University, Gwalior, M.P, India
(abhishek.kumar13@s.amity.edu)
2
Associate Professor, Amity University, Gwalior, M.P, India
(sjgoyal@gwa.amity.edu)
3
Assistant Professor, G.N.S University, Sasaram, Bihar, India
(sumit170787@gmail.com)
4
Research Scholar, Amity University, Gwalior, M.P, India
(hitesh.sharma2@s.amity.edu)
Abstract :
The rapid adoption of the Internet of Things throughout healthcare and smart city construction has led to a rise in networked devices and security issues. This work suggests new techniques to improve IoT safety and maximise computing resources. We develop a complete security architecture integrating lightweight cryptography, blockchain, machine learning anomaly detection, and federated learning. We did so because we know that traditional security measures are inadequate for the Internet of Things. The lightweight cryptographic algorithm (LCA) provides efficient encryption and decryption, making it ideal for low-resource Internet of Things devices. Twenty processes comprise the LCA design. These operations include key generation, data encryption, digital signatures, and integrity checking. These procedures secure IoT data transfers. ADML detects anomalies in encrypted Internet of Things data using machine learning. This approach may identify security issues better. To keep up with data trends, this method extracts features, trains models, and updates them. Blockchain-based data integrity (BDI) is the third element. Blockchain ensures that Internet of Things data is reliable and full. BDI developed an immutable ledger solution to increase IoT data security and dependability. This data integrity system generates blocks, hashes, confirms blocks, and updates the blockchain. Fourth, FLIoT (Federated Learning for the Internet of Things) emphasises data privacy and collaborative model training across IoT devices. Foundation for the Internet of Things (FIoT) protocols and standards aim to increase IoT devices' collective intelligence while safeguarding users' privacy. It includes local model training, model aggregation, and the latest global model distribution. Our work also uses Secure Multi-party Computation (SMC) to analyse data more thoroughly and continuously, addressing online transaction cybersecurity issues. The framework outperforms the current state of the art in memory use, energy consumption, anomaly detection accuracy and precision, and encryption and decryption time. The "Hybrid Fusion Framework" combines lightweight cryptographic algorithms with federated learning, machine learning, blockchain technology, and other similar technologies to provide an effective, adaptable, and affordable IoT security solution.
Keywords :
Blockchain Technology; Data Integrity; Edge Computing; Encryption/Decryption; Federated Learning , IoT Security; Lightweight Cryptographic Algorithms; Scalability; Zero Trust Architecture.
References :
[1] Z. Huang and Y. Qin, "Malicious mining web page detection and forensics based on multi-feature recognition," Netinfo Security, vol. 21, no. 7, pp. 87–94, 2021.
[2] X. Yu, R. Ge, and F. Li, "Research on blockchain-based identity authentication scheme in social networks," in Proceedings of the International Conference on Machine Learning for Cyber Security, Springer, Berlin, Germany, 2020.
[3] C. Lee, S. Maharjan, K. Ko, J. Woo, and J. Wk Hong, "Machine Learning Based Bitcoin Address Classification," in Proceedings of the International Conference on Blockchain and Trustworthy Systems, Springer, Singapore, 2020.
[4] D. Pathak and R. Kashyap, "Neural correlate-based E-learning validation and classification using convolutional and Long Short-Term Memory networks," Traitement du Signal, vol. 40, no. 4, pp. 1457-1467, 2023. [Online]. Available: https://doi.org/10.18280/ts.400414
[5] R. Kashyap, "Stochastic Dilated Residual Ghost Model for Breast Cancer Detection," J Digit Imaging, vol. 36, pp. 562–573, 2023. [Online]. Available: https://doi.org/10.1007/s10278-022-00739-z
[6] D. Bavkar, R. Kashyap, and V. Khairnar, "Deep Hybrid Model with Trained Weights for Multimodal Sarcasm Detection," in Inventive Communication and Computational Technologies, G. Ranganathan, G. A. Papakostas, and Á. Rocha, Eds. Singapore: Springer, 2023, vol. 757, Lecture Notes in Networks and Systems. [Online]. Available: https://doi.org/10.1007/978-981-99-5166-6_13
[7] P. Singh, M. Masud, M. S. Hossain, and A. Kaur, "Blockchain and homomorphic encryption-based privacy-preserving data aggregation model in smart grid," Computers & Electrical Engineering, vol. 93, 2021.
[8] Y. Fang, C. Huang, L. Liu, and M. Xue, "Research on malicious JavaScript detection technology based on LSTM," IEEE Access, vol. 6, pp. 59118–59125, 2018.
[9] M.-H. Wu, Y.-J. Lai, Y.-L. Hwang, T.-C. Chang, and F.-H. Hsu, "MinerGuard: A Solution to Detect Browser-Based Cryptocurrency Mining through Machine Learning," Applied Sciences, vol. 12, no. 19, p. 9838, 2022.
[10] R. Tahir, S. Durrani, F. Ahmed, H. Saeed, F. Zaffar, and S. Ilyas, "The browsers strike back: countering cryptojacking and parasitic miners on the web," in Proceedings of the IEEE Conference on Computer Communications, pp. 703–711, Paris, France, August 2019.
[11] H. Geng, Z. Yang, and S. Yang, "How you get shot in the back: a systematical study about cryptojacking in the real world," in Proceedings of the 2018 ACM SIGSAC Conference on Computer and Communications Security, pp. 1701–1713, Toronto, Canada, October 2018.
[12] J. G. Kotwal, R. Kashyap, and P. M. Shafi, "Artificial Driving based EfficientNet for Automatic Plant Leaf Disease Classification," Multimed Tools Appl, 2023. [Online]. Available: https://doi.org/10.1007/s11042-023-16882-w
[13] V. Roy et al., “Detection of sleep apnea through heart rate signal using Convolutional Neural Network,” International Journal of Pharmaceutical Research, vol. 12, no. 4, pp. 4829-4836, Oct-Dec 2020.
[14] R. Kashyap, "Machine Learning, Data Mining for IoT-Based Systems," in Research Anthology on Machine Learning Techniques, Methods, and Applications, Information Resources Management Association, Ed. IGI Global, 2022, pp. 447-471. [Online]. Available: https://doi.org/10.4018/978-1-6684-6291-1.ch025
[15] V. G. Le, H. T. Nguyen, and D. N. Lu, "A solution for automatically malicious web shell and web application vulnerability detection," in Proceedings of the International Conference on Computational Collective Intelligence, Springer International Publishing, Berlin, Germany, 2016.
[16] J. Fu, L. Lai, and Y. Wang, "CNN-based web shell file detection," Journal of Zhengzhou University (Medical Science), vol. 51, no. 2, pp. 4–11, 2019.
[17] E. Heilman, A. Kendler, A. Zohar, and S. Goldberg, "Eclipse attacks on bitcoin's peer-to-peer network," in Proceedings of the 24th USENIX Conference on Security Symposium, USENIX Association, 2015.
[18] H. P. Sahu and R. Kashyap, "FINE_DENSEIGANET: Automatic medical image classification in chest CT scan using Hybrid Deep Learning Framework," International Journal of Image and Graphics [Preprint], 2023. [Online]. Available: https://doi.org/10.1142/s0219467825500044
[19] S. Stalin, V. Roy, P. K. Shukla, A. Zaguia, M. M. Khan, P. K. Shukla, A. Jain, "A Machine Learning-Based Big EEG Data Artifact Detection and Wavelet-Based Removal: An Empirical Approach," Mathematical Problems in Engineering, vol. 2021, Article ID 2942808, 11 pages, 2021. [Online]. Available: https://doi.org/10.1155/2021/2942808
[20] M. Zhang, Y. Cao, K. Jiang et al., "Proactive measures to prevent conveyor belt failures: deep learning-based faster foreign object detection," Engineering Failure Analysis, vol. 141, Article ID 106653, 2022.
[21] J. Wang, Q. Liu, and M. Dai, "Belt vision localization algorithm based on machine vision and belt conveyor deviation detection," in Proceedings of the 2019 34rd Youth Academic Annual Conference of Chinese Association of Automation (YAC), pp. 269–273, Jinzhou, China, June 2019.
| Style | # |
|---|---|
| MLA | Abhishek Kumar, Samta Jain Goyal , Sumit Kumar, Hitesh Kumar Sharma. "Hybrid Fusion of Lightweight Security Frameworks Using Data Mining Approach in IoT." Fusion: Practice and Applications, Vol. 14, No. 2, 2024 ,PP. 244-260 (Doi : https://doi.org/10.54216/FPA.140220) |
| APA | Abhishek Kumar, Samta Jain Goyal , Sumit Kumar, Hitesh Kumar Sharma. (2024). Hybrid Fusion of Lightweight Security Frameworks Using Data Mining Approach in IoT. Journal of Fusion: Practice and Applications, 14 ( 2 ), 244-260 (Doi : https://doi.org/10.54216/FPA.140220) |
| Chicago | Abhishek Kumar, Samta Jain Goyal , Sumit Kumar, Hitesh Kumar Sharma. "Hybrid Fusion of Lightweight Security Frameworks Using Data Mining Approach in IoT." Journal of Fusion: Practice and Applications, 14 no. 2 (2024): 244-260 (Doi : https://doi.org/10.54216/FPA.140220) |
| Harvard | Abhishek Kumar, Samta Jain Goyal , Sumit Kumar, Hitesh Kumar Sharma. (2024). Hybrid Fusion of Lightweight Security Frameworks Using Data Mining Approach in IoT. Journal of Fusion: Practice and Applications, 14 ( 2 ), 244-260 (Doi : https://doi.org/10.54216/FPA.140220) |
| Vancouver | Abhishek Kumar, Samta Jain Goyal , Sumit Kumar, Hitesh Kumar Sharma. Hybrid Fusion of Lightweight Security Frameworks Using Data Mining Approach in IoT. Journal of Fusion: Practice and Applications, (2024); 14 ( 2 ): 244-260 (Doi : https://doi.org/10.54216/FPA.140220) |
| IEEE | Abhishek Kumar, Samta Jain Goyal, Sumit Kumar, Hitesh Kumar Sharma, Hybrid Fusion of Lightweight Security Frameworks Using Data Mining Approach in IoT, Journal of Fusion: Practice and Applications, Vol. 14 , No. 2 , (2024) : 244-260 (Doi : https://doi.org/10.54216/FPA.140220) |