Department of Computer Science and Engineering, Vel Tech Rangarajan Dr. Sagunthala R & D Institute of Science and Technology, Chennai, Tamil Nadu, India.
The global energy market is migrating toward sustainable renewable energy sources (RES), with solar energy (SE) being the most significant due to its abundance and reliability. Photovoltaic (PV) converts SE into electricity, relying on data integrity and security. However, digitized data has cybersecurity vulnerabilities, including data breaches and attacks. Traditional security systems can provide essential protection but fail to address PV's dynamic and distributed nature, leading to gaps in defense against evolving cyber threats. The study proposes an endogenous security model for improving data transmission and storage within PV. It uses a Verification Feedback Mechanism (VFM) to integrate routing methods, compute efficient data paths, schedule them periodically, and verify their integrity. The model also incorporates cryptographic key infrastructure and key management protocols to ensure secure data transmission and management. This approach addresses challenges in data forging and ensures the integrity of the network's components. The study compared two methods and found one model superior in communication integrity and system adaptability. It achieved latency statistics below 20 ms and maintained Network Throughput (NT) at 9.2 Gbps even when attacked, demonstrating its effectiveness in securing PV from multiple cyberattacks.
Keywords
Photovoltaic Systems, Renewable Energy Sources, Solar Energy, Cyber-Physical Security, Data Transmission Integrity Rates.
C. Breyer et al., “On the History and Future of 100% Renewable Energy Systems Research,” IEEE Access, vol. 10, pp. 78176–78218, 2022, doi: 10.1109/access.2022.3193402.
S. Panneerselvam, S. K. Thangavel, V. S. Ponnam, and S. Sengan, “Federated learning based fire detection method using local MobileNet,” Scientific Reports, vol. 14, no. 1, Dec. 2024, doi: 10.1038/s41598-024-82001-w.
A. Alshahrani, S. Omer, Y. Su, E. Mohamed, and S. Alotaibi, “The Technical Challenges Facing the Integration of Small-Scale and Large-scale PV Systems into the Grid: A Critical Review,” Electronics, vol. 8, no. 12, p. 1443, Dec. 2019, doi: 10.3390/electronics8121443.
D. P. F. Möller, “Cybersecurity in Digital Transformation,” Guide to Cybersecurity in Digital Transformation, pp. 1–70, 2023, doi: 10.1007/978-3-031-26845-8_1.
Mahalakshmi, R. L. Kumar, K. S. Ranjini, S. Sindhu, and R. Udhayakumar, “Efficient authenticated key establishment protocol for telecare medicine information systems,” Industrial, Mechanical And Electrical Engineering, vol. 2676, p. 020006, 2022, doi: 10.1063/5.0117522.
S. Saeed, S. A. Altamimi, N. A. Alkayyal, E. Alshehri, and D. A. Alabbad, “Digital Transformation and Cybersecurity Challenges for Businesses Resilience: Issues and Recommendations,” Sensors, vol. 23, no. 15, p. 6666, Jul. 2023, doi: 10.3390/s23156666.
J. Ye et al., “A Review of Cyber–Physical Security for Photovoltaic Systems,” IEEE Journal of Emerging and Selected Topics in Power Electronics, vol. 10, no. 4, pp. 4879–4901, Aug. 2022, doi: 10.1109/jestpe.2021.3111728.
“An E-Commerce Based Personalized Health Product Recommendation System Using CNN-Bi-LSTM Model,” International Journal of Intelligent Engineering and Systems, vol. 16, no. 6, pp. 398–410, Dec. 2023, doi: 10.22266/ijies2023.1231.33.
A. C. S. Robert Vincent and S. Sengan, “Effective clinical decision support implementation using a multi filter and wrapper optimisation model for Internet of Things based healthcare data,” Scientific Reports, vol. 14, no. 1, Sep. 2024, doi: 10.1038/s41598-024-71726-3.
R. Lokeshkumar, O. Mishra, and S. Kalra, “Social media data analysis to predict mental state of users using machine learning techniques,” Journal of Education and Health Promotion, vol. 10, no. 1, p. 301, 2021, doi: 10.4103/jehp.jehp_446_20.
G. Heilscher et al., “Integration of Photovoltaic Systems into Smart Grids Demonstration of Solar-, Storage and E-Mobility Applications within a Secure Energy Information Network in Germany,” 2019 IEEE 46th Photovoltaic Specialists Conference (PVSC), pp. 1541–1548, Jun. 2019, doi: 10.1109/pvsc40753.2019.8980532.
U. Chadha et al., “Powder Bed Fusion via Machine Learning-Enabled Approaches,” Complexity, vol. 2023, pp. 1–25, Apr. 2023, doi: 10.1155/2023/9481790.
R. K. Poluru and R. Lokeshkumar, “Meta-Heuristic MOALO Algorithm for Energy-Aware Clustering in the Internet of Things,” International Journal of Swarm Intelligence Research, vol. 12, no. 2, pp. 74–93, Apr. 2021, doi: 10.4018/ijsir.2021040105.
B. R. R. Reddy and R. L. Kumar, “A Fusion Model for Personalized Adaptive Multi-Product Recommendation System Using Transfer Learning and Bi-GRU,” Computers, Materials & Continua, vol. 81, no. 3, pp. 4081–4107, 2024, doi: 10.32604/cmc.2024.057071.
S. Kunjiappan, L. K. Ramasamy, S. Kannan, P. Pavadai, P. Theivendren, and P. Palanisamy, “Optimization of ultrasound-aided extraction of bioactive ingredients from Vitis vinifera seeds using RSM and ANFIS modeling with machine learning algorithm,” Scientific Reports, vol. 14, no. 1, Jan. 2024, doi: 10.1038/s41598-023-49839-y.
N. Krishnadoss and L. Kumar Ramasamy, “A study on high dimensional big data using predictive data analytics model,” Indonesian Journal of Electrical Engineering and Computer Science, vol. 30, no. 1, p. 174, Apr. 2023, doi: 10.11591/ijeecs.v30.i1.pp174-182.
A. L. Karn et al., “Fuzzy and SVM Based Classification Model to Classify Spectral Objects in Sloan Digital Sky,” IEEE Access, vol. 10, pp. 101276–101291, 2022, doi: 10.1109/access.2022.3207480.
N. Krishnadoss and L. K. Ramasamy, “Crop yield prediction with environmental and chemical variables using optimized ensemble predictive model in machine learning,” Environmental Research Communications, vol. 6, no. 10, p. 101001, Oct. 2024, doi: 10.1088/2515-7620/ad7e81.
P. Krishnamoorthy et al., “Effective Scheduling of Multi-Load Automated Guided Vehicle in Spinning Mill: A Case Study,” IEEE Access, vol. 11, pp. 9389–9402, 2023, doi: 10.1109/access.2023.3236843.
P. Selvam et al., “A Transformer-Based Framework for Scene Text Recognition,” IEEE Access, vol. 10, pp. 100895–100910, 2022, doi: 10.1109/access.2022.3207469.
CRediT Author Statement
The authors confirm contribution to the paper as follows:
Conceptualization: Brahmadesam Viswanathan Krishna, Sathvik Bagam, Jayasri R, Krishnaveni N, Prasath R and Tanweer Alam;
Methodology: Brahmadesam Viswanathan Krishna and Sathvik Bagam;
Software: Jayasri R, Krishnaveni N, Prasath R and Tanweer Alam;
Data Curation: Brahmadesam Viswanathan Krishna and Sathvik Bagam;
Writing- Original Draft Preparation: Brahmadesam Viswanathan Krishna, Sathvik Bagam, Jayasri R, Krishnaveni N, Prasath R and Tanweer Alam;
Visualization: Jayasri R, Krishnaveni N, Prasath R and Tanweer Alam;
Investigation: Brahmadesam Viswanathan Krishna and Sathvik Bagam;
Supervision: Viswanathan Krishna, Sathvik Bagam;
Validation: Brahmadesam Viswanathan Krishna, Sathvik Bagam;
Writing- Reviewing and Editing: Brahmadesam Viswanathan Krishna, Sathvik Bagam, Jayasri R, Krishnaveni N, Prasath R and Tanweer Alam;
All authors reviewed the results and approved the final version of the manuscript.
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Brahmadesam Viswanathan Krishna
Department of Computer Science and Engineering, KCG College of Technology, Chennai, Tamil Nadu, India.
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Cite this article
Brahmadesam Viswanathan Krishna, Sathvik Bagam, Jayasri R, Krishnaveni N, Prasath R and Tanweer Alam, “Implementation and Enhancement of Endogenous Security Mechanisms in Photovoltaic Data Storage and Transmission”, Journal of Machine and Computing, pp. 968-983, April 2025, doi: 10.53759/7669/jmc202505077.
