Volume 7 , Issue 2 , PP: 08-24, 2023 | Cite this article as | XML | Html | PDF | Full Length Article
Vishwesh Nagamalla 1 * , J.Raj karkee 2 , Ravi Kumar Sanapala 3
Doi: https://doi.org/10.54216/IJWAC.070201
This paper introduces a comprehensive framework for industrial Internet of Things (IoT) cybersecurity, integrating multiple algorithms to enhance threat intelligence. The proposed framework encompasses five key algorithms, each addressing specific aspects of data preprocessing, time series analysis, predictive analytics, and behavioral machine learning. The Data Preprocessing and Integration algorithm refines raw IoT data through a meticulous 20-step process, ensuring high-quality input for subsequent analyses. The Time Series Analysis algorithm delves into temporal patterns, while the Random Forest algorithm focuses on predictive analytics for proactive threat detection. The LSTM Ensemble algorithm extends the analysis into behavioral machine learning, capturing temporal dependencies and detecting anomalies. The Weighted Average Ensemble combines outputs from predictive analytics and behavioral models, leveraging their correlation for enhanced threat intelligence. An ablation study dissects the individual contributions of each algorithmic component, shedding light on their specific impacts. The results highlight the significance of each step, guiding optimizations for improved performance. The proposed framework outperforms existing methods in various performance metrics, showcasing its potential as a robust solution for proactive threat intelligence in complex industrial environments. This framework stands at the forefront of industrial IoT cybersecurity, offering a holistic and adaptive approach to address evolving threats. The ablation study enhances the transparency and understanding of the framework, contributing to its continuous refinement and effectiveness in safeguarding critical industrial systems.
algorithm, analysis , anomaly detection , behavioral machine learning, cybersecurity , data preprocessing , ensemble learning, industrial IoT , integration, LSTM , machine learning , predictive analytics , random forest , temporal features , time series analysis , weighted average ensemble .
[1] J. Pei, Z. Yu, J. Li, M. A. Jan, and K. Lakshmanna, "TKAGFL: a federated communication framework under data heterogeneity," IEEE Transactions on Network Science and Engineering, 2022. [Online]. Available: Publisher Site | Google Scholar
[2] N. Gundluru, D. S. Rajput, K. Lakshmanna et al., "Enhancement of detection of diabetic retinopathy using Harris hawks optimization with deep learning model," Computational Intelligence and Neuroscience, vol. 2022, 13 pages, 2022. [Online]. Available: Publisher Site | Google Scholar
[3] R. Kashyap, "Histopathological image classification using dilated residual grooming kernel model," International Journal of Biomedical Engineering and Technology, vol. 41, no. 3, p. 272, 2023. [Online]. Available: https://doi.org/10.1504/ijbet.2023.129819
[4] J. Kotwal, Dr. R. Kashyap, and Dr. S. Pathan, "Agricultural plant diseases identification: From traditional approach to deep learning," Materials Today: Proceedings, vol. 80, pp. 344–356, 2023. [Online]. Available: https://doi.org/10.1016/j.matpr.2023.02.370
[5] Edwin Ramirez-Asis, Romel Percy Melgarejo Bolivar, Leonid Alemán Gonzales, Sushovan Chaudhury, Ramgopal Kashyap, Walaa F. Alsanie, G. K. Viju, "A Lightweight Hybrid Dilated Ghost Model-Based Approach for the Prognosis of Breast Cancer," Computational Intelligence and Neuroscience, vol. 2022, Article ID 9325452, 10 pages, 2022. [Online]. Available: https://doi.org/10.1155/2022/9325452
[6] N. G. Rezk, E. E. D. Hemdan, A. F. Attia, A. el-Sayed, and M. A. el-Rashidy, "An efficient IoT based smart farming system using machine learning algorithms," Multimedia Tools and Applications, vol. 80, no. 1, pp. 773–797, 2021. [Online]. Available: Publisher Site | Google Scholar
[7] A. Araby, M. M. Abd Elhameed, N. M. Magdy, N. Abdelaal, Y. T. Abd Allah, and M. S. Darweesh, "Intelligent IoT monitoring system for agriculture with predictive analysis," in 2019 8th International Conference on Modern Circuits and Systems Technologies (MOCAST), pp. 1–4, 2019. [Online]. Available: Publisher Site | Google Scholar
[8] A. Tageldin, D. Adly, H. Mostafa, and H. S. Mohammed, "Applying machine learning technology in the prediction of crop infestation with cotton leafworm in greenhouse," bioRxiv, 2020. [Online]. Available: Publisher Site | Google Scholar
[9] A. A. S. Aliar, J. Yesudhasan, M. Alagarsamy, K. Anbalagan, J. Sakkarai, and K. Suriyan, "A comprehensive analysis on IoT-based intelligent farming solutions using machine learning algorithms," Bulletin of Electrical Engineering and Informatics, vol. 11, no. 3, pp. 1550–1557, 2022. [Online]. Available: Publisher Site | Google Scholar
[10] P. Sethy, S. Behera, C. Pandey, and S. Narayanand, "Intelligent paddy field monitoring system using deep learning and IoT," Concurrent Engineering Research and Applications, 2020. [Online]. Available: Google Scholar
[11] N. Kaushik, S. Narad, A. Mohature, and P. Sakpal, "Predictive analysis of IoT-based digital agriculture system using machine learning," International Journal of Engineering Science and Computing, vol. 9, 2019. [Online]. Available: Google Scholar
[12] K. N.-E.-A. Siddiquee, M. Islam, N. Singh et al., "Development of algorithms for an IoT-based smart agriculture monitoring system," Wireless Communications and Mobile Computing, vol. 2022, Article ID 7372053, 16 pages, 2022. [Online]. Available: Publisher Site | Google Scholar
[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 et al., "Glaucoma detection and classification using improved U-Net Deep Learning Model," Healthcare, vol. 10, no. 12, p. 2497, 2022. [Online]. Available: https://doi.org/10.3390/healthcare10122497
[15] Vinodkumar Mohanakurup, Syam Machinathu Parambil Gangadharan, Pallavi Goel, Devvret Verma, Sameer Alshehri, Ramgopal Kashyap, Baitullah Malakhil, "Breast Cancer Detection on Histopathological Images Using a Composite Dilated Backbone Network," Computational Intelligence and Neuroscience, vol. 2022, Article ID 8517706, 10 pages, 2022. [Online]. Available: https://doi.org/10.1155/2022/8517706
[16] G. S. Gaba, M. Hedabou, P. Kumar, A. Braeken, M. Liyanage, and M. Alazab, "Zero knowledge proofs based authenticated key agreement protocol for sustainable healthcare," Sustainable Cities and Society Journal, vol. 80, p. 103766, 2022. [Online]. Available: Publisher Site | Google Scholar
[17] M. Hedabou, "Cloud key management based on verifiable secret sharing," in 15th International Conference on Network and System Security, pp. 289–303, 2021. [Online]. Available: Publisher Site | Google Scholar
[18] E. M. Amhoud, G. R. B. Othman, L. Bigot et al., "Experimental demonstration of space-time coding for MDL mitigation in few-mode fiber transmission systems," in 2017 European Conference on Optical Communication (ECOC), pp. 1–3, 2017. [Online]. Available: Google Scholar
[19] E. M. Amhoud, G. Rekaya-Ben Othman, and Y. Jaouën, "Capacity enhancement of few-mode fiber transmission systems impaired by mode-dependent loss," Applied Sciences, vol. 8, no. 3, p. 326, 2018. [Online]. Available: Publisher Site | Google Scholar
[20] R. Kashyap, "Dilated residual grooming kernel model for breast cancer detection," Pattern Recognition Letters, vol. 159, pp. 157–164, 2022. [Online]. Available: https://doi.org/10.1016/j.patrec.2022.04.037
[21] 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
[22] O. Alkhazragi, X. Sun, V. Zuba et al., "Spectrally resolved characterization of thermally induced underwater turbulence using a broadband white-light interrogator," IEEE Photonics Journal, vol. 11, no. 5, pp. 1–9. [Online]. Available: Publisher Site | Google Scholar
R. Kaur, K. Havish, T. K. Dutt, and G. M. Reddy, "Agrocompanion: an intelligent farming approach based on IoT and machine learning," International Journal of Innovative Technology and Exploring Engineering (IJITEE), vol. 9, no. 12, pp. 254–262, 2020. [Online]. Available: Publisher
