A decentralized framework for enhancing security in power systems through blockchain technology and trading system

ABSTRACT

The primary objective of implementing secure smart power networks is to reduce the risk of data privacy breaches, including adversarial data poisoning and inference attacks. This study presents a novel approach, namely the Blockchain-based Power System Security Model (BC-PSSM), to augment security measures within power systems. The proposed method utilizes blockchain technology to enhance the security of data storage and verification and the mechanisms for data signing and authentication. It employs decentralized detection to improve the effectiveness and precision of intrusion detection with a Support Vector Machine (SVM). The BC-PSSM method exhibits promising outcomes in multiple facets of power system security detection, as evidenced by thorough simulations and evaluations. The simulation results demonstrate a significant decrease in computational burden and energy consumption by maintaining a high energy output level. The BC-PSSM algorithm performs remarkably in detection and false positive rates, resulting in heightened security and enhanced efficiency. The results also illustrate the positive impact of BC-PSSM in enhancing the security of power systems, offering robust protection against cybersecurity threats. BC-PSSM aims to enhance the security of power systems, thereby promoting a more robust and dependable energy infrastructure. This initiative plays a crucial role in protecting essential assets and mitigating the potential threats posed by cyber-attacks. The BC-PSSM algorithm enhanced efficiency to 92.15% and boosted overall security to 96.34%. It achieved this by achieving an amazing identification rate of 96.75% and an extraordinarily low false positive rate of 1.82%.

KEYWORDS:

• Power systems
• security
• blockchain
• intrusion detection
• support vector machine

Disclosure statement

No potential conflict of interest was reported by the author(s).

Author contribution declaration

The authors confirm their contributions to the paper as follows:

TK conceived the study, developed the theory and performed the computations and in-charge of overall direction and planning.

DD developed the theoretical formalism toward Energy trading system and Electricity market and performed the analytic calculations.

GR and TK developed the model code, performed the simulation study and obtained the results.

VR contributed in developing model code, preparing the article and supervised the findings of this work.

All authors reviewed the results and approved the final version of the manuscript.

Additional information

Funding

The author(s) received no financial support for the research, authorship, and/or publication of this article.

Notes on contributors

V. Thiruppathy Kesavan

Thiruppathy Kesavan, V. completed his M.E. and Ph.D. in the field of Computer Science and Engineering from Anna University, Chennai, India, and Kalasalingam University, Krishnankoil, India, respectively. He has more than 20 years of teaching experience since 2003. He has worked in various positions at reputed institutes such as Kalasalingam University, Tamil Nadu, Madanapalle Institute of Technology and Science, Madanapalle, Andhra Pradesh, and GMR Institute of Technology, Andhra Pradesh. Presently, he is working as Professor and Head, Information Technology at Dhanalakshmi Srinivasan Engineering College, Tamil Nadu, India. His areas of interest include Internet of Things, Wireless Sensor Networks, Computer Networks, Network Security, System Software, and Microprocessors. He has published more than 30 research articles, including in International journals, Book chapters, and International and National conferences.

D. Danalakshmi

Danalakshmi, D. has received her B.E. degree in Electrical and Electronics Engineering from Thiagarajar College of Engineering, Madurai, affiliated to Madurai Kamaraj University, India in 2003 and M.E. degree in Power Systems Engineering from Thiagarajar College of Engineering, Madurai, affiliated to Anna University, Chennai, Tamil Nadu, India, in 2006. She has completed her Ph.D. degree in Kalasalingam University in September 2017. He has worked in various positions at reputed institutes such as Kalasalingam University, Tamil Nadu, Madanapalle Institute of Technology and Science, Madanapalle, Andhra Pradesh, and GMR Institute of Technology, Andhra Pradesh. Presently, she is working as Associate Professor in the Department of Electrical & Electronics Engineering at Dhanalakshmi Srinivasan Engineering College, Tamil Nadu, India. She is actively involved in research for the past ten years and published more than 25 research articles, including in International journals, book chapters, and International and National conferences. She is presently working in the area of Power System Optimization, Renewable energy, Power system security and Smart Grid.

R. Gopi

Dr. Gopi R. is a Professor in the Department of Computer Science and Engineering at Dhanalakshmi Srinivasan Engineering College, Perambalur. He earned his Ph.D. degree in 2019 from St. Peter’s Institute of Higher Education and Research. He completed his Master of Technology (M.Tech.) degree in 2012 from Veltech University, Chennai, and obtained his Bachelor’s Degree (B.E.) in 2006 from Sona College of Technology. He has published more than 25 papers in SCI journals, holds 5 patents, and had one patent granted in 2023. His research areas include AI Techniques, Cloud Computing, Machine Learning, Big Data, and Vehicular Adhoc Networks (VANET).

R. Venkatesan

Venkatesan R. (Rudhrakoti Venkatesan) received a Bachelor of Engineering degree in Computer Science and Engineering from Park College of Engineering and Technology, Coimbatore in 2004, followed by a Master of Engineering in Computer Science and Engineering from Sona College of Technology, Salem in 2007. He completed his doctoral degree at Vellore Institute of Technology, deemed to be University, Vellore in 2021. With over 18 years of teaching experience and 10 years of research experience, his current research focuses on satellite image processing and neural networks. Currently, he holds the position of Assistant Professor Grade III in the School of Computing at SASTRA (Deemed to be University), Thanjavur.