Journal of Green Energy Research and Innovation
Quarterly

Illegal Miner Detection based on Dynamic Mode Decomposition and Unsupervised Machine Learning Algorithms

Document Type : Research article

Authors

Alireza Simorgh 1
Khosro Khandani 1
Maryam Amiri 2

1 Department of Electrical Engineering, Faculty of Engineering, Arak University, Arak, 38156-8-8349, Iran.‎

2 Department of Computer Engineering, Faculty of Engineering, Arak University, Arak, 38156-8-8349, Iran.‎

https://doi.org/10.61186/jgeri.2.2.79
Abstract
Since the most important issue in the production of digital currencies is energy consumption, the ‎use of illegal electricity in mining farms has become very popular. Illegal mining is particularly ‎important in countries such as Iran where the price of electrical energy is extremely low. This issue ‎has caused numerous problems such as frequent blackouts, large losses for industries and even ‎daily power cuts in several large cities. Previous machine learning approaches for miner detection ‎are mostly supervised methods which rely on labeled data. Due to the fact that the number of ‎labeled data is very limited in reality, we propose unsupervised methods in this paper. A real data ‎set from Markazi Province Distribution Company in Iran has been employed to produce the results. ‎The classification process consists of two stages: in the first stage, Dynamic Mode Decomposition ‎‎(DMD) has been used to extract new features which compose the set of features along with certain ‎factors from the Advanced Metering Infrastructure (AMI). These features are selected for 58 ‎subscribers with positive and negative labels. In the second stage, a number of unsupervised models ‎are built from the results of the first stage. The highest accuracy of classification obtained is 74% ‎from unsupervised algorithms and 85% for supervised algorithms, which is very significant ‎considering the fact that unsupervised algorithms do not need labeled data‎.‎

Graphical Abstract

Illegal Miner Detection based on Dynamic Mode Decomposition and Unsupervised Machine Learning Algorithms

Highlights

 

❖ Employing a real data set from Markazi Province Distribution Company to detect illegal miners.
❖ Dynamic Mode Decomposition (DMD) has been used to extract new features.
❖ Obtaining high accuracy of classification using unsupervised algorithms.

Keywords

Miner detection
Unsupervised algorithms
Dynamic mode decomposition

Declaration of competing interest
The authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper. The ethical issues, including plagiarism, informed consent, misconduct, data fabrication and/or falsification, double publication and/or submission, redundancy, have been completely observed by the authors.

 

Credit Authorship Contribution Statement

Alireza Simorgh: Data curation, Formal analysis, Investigation, Methodology, Resources, Software, Validation, Visualization, Roles/Writing - original draft. Khosro Khandani: Conceptualization, Investigation, Project administration, Supervision, Roles/Writingoriginal draft, Writing-review & editing. Maryam Amiri: Conceptualization, Data curation, Project administration, Resources, Writing-review & editing.

 

Bibliography

Alireza Simorgh  was born in 1997 in Boroujerd, Iran. He obtained his Bachelor's and Master's degrees in Electrical Engineering and Electrical Control Engineering, respectively, from Ayatollah Boroujerdi University in Boroujerd, Iran, and Arak University in Arak, Iran, in 2021 and 2024. Currently, he is working as an Industry Liaison Officer at Shoroue Innovation Center. His specialized interests include robotics, multi-agent systems, data science and machine learning, and fuzzy systems

Khosro Khandani received his BS degree in electrical engineering from Sahand University of Technology, Tabriz, Iran, in 2007; MS degree in electrical engineering from Iran University of science and Technology, Tehran, Iran, in 2011; Ph.D. degree in electrical engineering from Tarbiat Modares University, Tehran, Iran, in 2016. He is currently an associate professor in electrical engineering department, faculty of engineering, Arak University, Arak, Iran. His research interests include cooperative control of multi-agent systems, fuzzy systems,
fractional-order systems and machine learning.

Maryam Amiri received the BS degree in computer engineering from Arak University, Arak, Iran, in 2009; the MS degree in computer engineering from the Bu-Ali Sina University, Hamedan, Iran, in 2012; the Ph.D. degree in computer engineering from the University of Tabriz, Tabriz, Iran, in 2018. She is currently an assistant professor in the Department of Computer Engineering, the faculty of engineering, Arak University, Arak, Iran. Her research interests include cloud computing, machine learning, and data mining.

 

Citation

A. Simorgh, K. Khandani, and M. Amiri," Illegal Miner Detection based on Dynamic Mode Decomposition and Unsupervised Machine Learning Algorithms," Journal of Green Energy Research and Innovation, vol. 2, no. 6, pp. 79-88, 2025.

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Journal of Green Energy Research and Innovation
Volume 2, Issue 2
Spring 2025
Pages 79-88

  • Receive Date 19 February 2025
  • Revise Date 24 March 2025
  • Accept Date 25 March 2025
  • Publish Date 01 June 2025

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