Authors : Sushma; Amanulla; Javid Akthar

Volume/Issue : Volume 9 - 2024, Issue 8 - August

Google Scholar : https://tinyurl.com/khzynuxw

Scribd : https://tinyurl.com/y3hwx2yb

DOI : https://doi.org/10.38124/ijisrt/IJISRT24AUG1475

Note : A published paper may take 4-5 working days from the publication date to appear in PlumX Metrics, Semantic Scholar, and ResearchGate.

Abstract : As global reliance on power systems grows due to increasing energy demands and modern consumption patterns, maintaining the stability and reliability of the power grid has become crucial. Power systems are complex and nonlinear, and their operations are continuously evolving, making it difficult and expensive to ensure stability. Traditionally, power systems are designed to handle a single outage at a time. However, recent years have seen several significant blackouts, each originating from a single failure, which have been extensively reported. These reports are vital for mitigating operational risks by strengthening systems against identified high-risk scenarios. While extensive research has been conducted on these blackouts, cyber- attacks introduce a new dimension of risk. The advent of Phasor Measurement Units (PMUs) has enabled centralized monitoring of power system data, allowing for more effective fault and cyber-attack detection.This paper proposes a machine learning-based approach to detecting cyber-attacks using PMU data. Given the complexity and volume of power system data, traditional mathematical and statistical methods are challenging to implement. Instead, a Support Vector Classification (SVC) algorithm is used for binary classification, distinguishing between 'attack' and 'normal' states. The algorithm is trained on PMU data and evaluated using metrics such as the AUC-ROC curve and confusion matrix, achieving an 82% AUC- ROC score, demonstrating its effectiveness in identifying cyber- attacks.

Keywords : Cyber Attack; Support Vector Machine; AUC- ROC; Support Vector Classification.

References :

1. internet.http://www.wired.com/threatlevel/2013/12/bgp-hijacking-belarus-iceland/. Accessed: 2022-12-15.
2. Q. Sun, L. Shi, Y. Ni, D. Si, and J. Zhu, ‘‘An enhanced cascading failure model integrating data mining technique,’’ Protection Control Mod. Power Syst., vol. 2, no. 1, pp. 209–219, Jan. 2017.
3. R. Vijayanand, D. Devaraj, B. Kannapiran, and K. Kartheeban, ‘‘Bit masking based secure data aggregation technique for Advanced Metering Infrastructure in Smart Grid system,’’ in Proc. Int. Conf. Comput. Commun. Inform., Jan. 2016, pp. 45–54.
4. T. J. Overbye, Z. Mao, K. S. Shetye, and J. D. Weber, ‘‘An interactive, extensible environment for power system simulation on the PMU time frame with a cyber security application,’’ in Proc. IEEE Power Energy Conf., Feb. 2017, pp. 1–6.
5. Z. Mao, T. Xu, and T. J. Overbye, ‘‘Real-time detection of malicious PMU data,’’ in Proc. Int. Conf. Intell. Syst. Appl. Power Syst., Sep. 2017, pp. 121–128.
6. https://www.kaggle.com/bachirbarika/power-system
7. S. Wang, M. Roger, J. Sarrazin et al., “Hyperparameter optimization of two-hidden-layer neural networks for power amplifiers behavioral modeling using genetic algorithms,” IEEE Microwave and Wireless Components Letters, vol. 29, no. 12, pp. 802-805, Dec. 2019
8. S. Ahmed, Y. Lee, S. Hyun et al., “Unsupervised machine learningbased detection of covert data integrity assault in smart grid networks utilizing isolation forest,” IEEE Transactions on Information Forensics and Security, vol. 14, no. 10, pp. 2765-2777, Oct. 2019.
9. J. Wang, D. Shi, Y. Li et al., “Distributed framework for detecting PMU data manipulation attacks with deep autoencoders,” IEEE Trans‐ actions on Smart Grid, vol. 10, no. 4, pp. 4401-4410, Jul. 2019.
10. M. Aboelwafa, K. Seddik, M. Eldefrawy et al., “A machine-learningbased technique for false data injection attacks detection in industrial IoT,” IEEE Internet of Things Journal, vol. 7, no. 9, pp. 8462-8471, Sept. 2020.
11. K. Lu, G. Zeng, X. Luo et al., “Evolutionary deep belief network for cyber-attack detection in industrial automation and control system,” IEEE Transactions on Industrial Informatics, vol. 17, no. 11, pp. 7618- 7627,Nov. 2021
12. I. Sohn, “Deep belief network based intrusion detection techniques: a survey,” Expert Systems with Applications, vol. 167, pp. 1-9, Apr. 2021.
13. Y. Zhang, J. Wang and B. Chen, “Detecting false data injection at‐ tacks in smart grids: A semi-supervised deep learning approach,” IEEE Transactions on Smart Grid, vol. 12, no. 1, pp. 623-634, Jan. 2021.
14. M. Farajzadeh-Zanjani, E. Hallaji, R. Razavi-Far et al., “Adversarial semi-supervised learning for diagnosing faults and attacks in power grids,” IEEE Transactions on Smart Grid, vol. 12, no. 4, pp. 3468- 3478, Jul. 2021.
15. M. Abdel-Basset, H. Hawash, R. Chakrabortty et al., “Semi-super‐ vised spatiotemporal deep learning for intrusions detection in IoT net‐ works,” IEEE Internet of Things Journal, vol. 8, no. 15, pp. 12251- 12265, Aug. 2021
16. T. Zheng, Y. Liu, Y. Yan et al., “RSSPN: robust semi-supervised proto‐ typical network for fault root cause classification in power distribution systems,” IEEE Transactions on Power Delivery, Nov. 2021. DOI: