Aadish Jain,
Abhinav Kumar,
Gaurav Joshi,
Surya Pratap Singh,
Dheeraj Verma,
Praveen Kumar Agrawal,
- Student Department of Electrical Engineering, Malaviya National Institute of Technology, Jaipur Rajasthan India
- Student Department of Electrical Engineering, Malaviya National Institute of Technology, Jaipur Rajasthan India
- Student Department of Electrical Engineering, Malaviya National Institute of Technology, Jaipur Rajasthan India
- Student Department of Electrical Engineering, Malaviya National Institute of Technology, Jaipur Rajasthan India
- Research Scholar Department of Electrical Engineering, Malaviya National Institute of Technology, Jaipur Rajasthan India
- Associate Professor Department of Electrical Engineering, Malaviya National Institute of Technology, Jaipur Rajasthan India
Abstract
The integration of Information and Communication Technology (ICT) with traditional electric grids has led to the development of smart grids. However, this integration has also increased the risk of anomalies, such as cyber-attacks, metering fraud, electricity theft etc. False Data Injection Attacks are a class of cyber-attacks against power grid monitoring systems, where adversaries can inject false data to manipulate the grid’s operation. Metering frauds pertain to malicious customers com- promising their smart meters to report false readings to achieve financial gains illegally. As a result, there is a growing need for effective detection mechanisms to identify and mitigate anomalies in smart grids. One approach to detecting anomalies in smart grids is through the use of machine learning algorithms. These algorithms leverage the data generated by smart grid systems to identify anomalies that may indicate the presence of false data injection attacks. The detection of false data injection attacks in smart grids is a critical area of research, given the increasing risk of cyber-attacks in these systems. Machine learning algorithms, transfer learning, and deep learning-based methods have been proposed as effective mechanisms for detecting and mitigating anomalies in smart grids, ultimately contributing to the security and reliability of smart grid systems.
Keywords: Anomalies, Machine Learning, Information and Communication Technology, False Data Injection Attacks.
[This article belongs to Trends in Electrical Engineering(tee)]
References
[1] M. Hentea, “A perspective on research initiatives in cybersecurity engineering for future smartgrids,” in 2022 IEEE International Conference on Electro Information Technology (eIT). IEEE, 2022.
[2] P. Ganguly, M. Nasipuri, and S. Dutta, “Challenges of the existing security measures deployed in the smart grid framework,” in 2019 IEEE 7th International Conference on Smart Energy Grid Engineering (SEGE). IEEE, 2019.
[3] T. Dayaratne et al., “False data injection attack detection for secure distributed demand response in smart grids,” in 2022 52nd Annual IEEE/IFIP International Conference on Dependable Systems and Networks (DSN). IEEE, 2022.
[4] S. K. Venkatachary, J. Prasad, and R. Samikannu, “Cybersecurity and cyber terrorism-in energy sector–a review,” Journal of Cyber Security Technology, vol. 2, no. 3-4, pp. 111–130, 2018.
[5] S. A. Yadav et al., “A review of possibilities and solutions of cyber- attacks in smart grids,” in 2016 International Conference on Innovation and Challenges in Cyber Security (ICICCS-INBUSH). IEEE, 2016.
[6] R. Gupta, “A survey on machine learning approaches and its techniques,” in 2020 IEEE International Students’ Conference on Electrical, Electronics and Computer Science (SCEECS). IEEE, 2020.
[7] A. Dawoud, S. Shahristani, and C. Raun, “Deep learning for network anomalies detection,” in 2018 International Conference on Machine Learning and Data Engineering (iCMLDE). IEEE, 2018.
[8] R. K. Dwivedi, A. K. Rai, and R. Kumar, “Outlier detection in wireless sensor networks using machine learning techniques: a survey,” in 2020 International Conference on Electrical and Electronics Engineering (ICE3). IEEE, 2020.
[9] F. Fathnia, F. Fathnia, and D. M. H. Javidi, “Detection of anomalies in smart meter data: A density-based approach,” in 2017 Smart Grid Conference (SGC). IEEE, 2017.
[10] A. Sharma, A. Kaur, and A. Semwal, “Supervised and unsupervised prediction application of machine learning,” in 2022 International Conference on Cyber Resilience (ICCR). IEEE, 2022.
[11] K. R. Dalal, “Analysing the role of supervised and unsupervised machine learning in iot,” in 2020 International Conference on Electronics and Sustainable Communication Systems (ICESC). IEEE, 2020.
[12] Z.-M. Wang, G.-H. Song, and C. Gao, “An isolation-based distributed outlier detection framework using nearest neighbor ensembles for wire- less sensor networks,” IEEE Access, vol. 7, pp. 96 319–96 333, 2019.
[13] M. A. Kabir and X. Luo, “Unsupervised learning for network flow based anomaly detection in the era of deep learning,” in 2020 IEEE Sixth International Conference on Big Data Computing Service and Applications (BigDataService). IEEE, 2020.
[14] R. K. Chahar and A. S. Singh, “Using machine learning techniques for outlier detection application,” in 2022 4th International Conference on Advances in Computing, Communication Control and Networking (ICAC3N). IEEE, 2022.
[15] C. Ordoudis et al., “An updated version of the ieee rts 24-bus system for electricity market and power system operation studies,” Technical University of Denmark, Tech. Rep. 13, 2016.
[16] Y. Yuan, Z. Li, and K. Ren, “Modeling load redistribution attacks in power systems,” IEEE Transactions on Smart Grid, vol. 2, no. 2, pp. 382–390, 2011.
[17] daython3. Anomaly detection using pyod python library. medium.com/@daython3/anomaly-detection-using-pyod-85b22ef70c00. Accessed: 5 May 2024.
[18] M. Kumar et al., “A conceptual introduction of machine learning algorithms,” in 2023 1st International Conference on Intelligent Computing and Research Trends (ICRT). IEEE, 2023.
[19] A. Talati et al., “Cyber-attack detection in smart grids using machine learning approach,” in 2023 7th International Conference on Computer Applications in Electrical Engineering-Recent Advances (CERA). IEEE, 2023. [20] Prayas Energy. Electricity load patterns. energy.prayaspune.org/our-work/article-and-blog/electricity-load-patterns. Accessed: 2024. [21] W. Jing, “Outlier detection in software system based on machine learning,” in 2020 International Conference on Robots & Intelligent System (ICRIS). IEEE, 2020. [22] N. Nesa, T. Ghosh, and I. Banerjee, “Non-parametric sequence-based learning approach for outlier detection in iot,” Future Generation Com- puter Systems, vol. 82, pp. 412–421, 2018. [23] M. Xie et al., “Scalable hypergrid k-nn-based online anomaly detection in wireless sensor networks,” IEEE Transactions on Parallel and Distributed Systems, vol. 24, no. 8, pp. 1661–1670, 2012. [24] S. Mehata, L. Linus, and L. Vinayakvitthal, “Real time data plotting tool using open source platform like raspberry pi and python,” in 2019 Global Conference for Advancement in Technology (GCAT). IEEE, 2019.
Trends in Electrical Engineering
| Volume | 14 |
| Issue | 02 |
| Received | June 29, 2024 |
| Accepted | July 22, 2024 |
| Published | August 16, 2024 |