Cybersecurity in Smart Grids: Detecting False Data Injection Attacks Utilizing Supervised Machine Learning Techniques
By integrating advanced technologies and data-driven systems in smart grids, there has been a significant revolution in the energy distribution sector, bringing a new era of efficiency and sustainability. Nevertheless, with this advancement comes vulnerability, particularly in the form of cyber thre...
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| Format: | Article |
| Language: | English |
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MDPI AG
2024-11-01
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| Series: | Energies |
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| Online Access: | https://www.mdpi.com/1996-1073/17/23/5870 |
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| _version_ | 1846124259817029632 |
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| author | Anwer Shees Mohd Tariq Arif I. Sarwat |
| author_facet | Anwer Shees Mohd Tariq Arif I. Sarwat |
| author_sort | Anwer Shees |
| collection | DOAJ |
| description | By integrating advanced technologies and data-driven systems in smart grids, there has been a significant revolution in the energy distribution sector, bringing a new era of efficiency and sustainability. Nevertheless, with this advancement comes vulnerability, particularly in the form of cyber threats, which have the potential to damage critical infrastructure. False data injection attacks are among the threats to the cyber–physical layer of smart grids. False data injection attacks pose a significant risk, manipulating the data in the control system layer to compromise the grid’s integrity. An early detection and mitigation of such cyberattacks are crucial to ensuring the smart grid operates securely and reliably. In this research paper, we demonstrate different machine learning classification models for detecting false data injection attacks, including the Extra Tree, Random Forest, Extreme Gradient Boosting, Logistic Regression, Decision Tree, and Bagging Classifiers, to secure the integrity of smart grids. A comprehensive dataset of various attack scenarios provides insights to explore and develop effective detection models. Results show that the Extra Tree, Random Forest, and Extreme Gradient Boosting models’ accuracy in detecting the attack outperformed the existing literature, an achieving accuracy of 98%, 97%, and 97%, respectively. |
| format | Article |
| id | doaj-art-0043cb66886d475b94e0c9c7dc993759 |
| institution | Kabale University |
| issn | 1996-1073 |
| language | English |
| publishDate | 2024-11-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | Energies |
| spelling | doaj-art-0043cb66886d475b94e0c9c7dc9937592024-12-13T16:25:12ZengMDPI AGEnergies1996-10732024-11-011723587010.3390/en17235870Cybersecurity in Smart Grids: Detecting False Data Injection Attacks Utilizing Supervised Machine Learning TechniquesAnwer Shees0Mohd Tariq1Arif I. Sarwat2Department of Electrical and Computer Engineering, Florida International University, Miami, FL 33174, USADepartment of Electrical and Computer Engineering, Florida International University, Miami, FL 33174, USADepartment of Electrical and Computer Engineering, Florida International University, Miami, FL 33174, USABy integrating advanced technologies and data-driven systems in smart grids, there has been a significant revolution in the energy distribution sector, bringing a new era of efficiency and sustainability. Nevertheless, with this advancement comes vulnerability, particularly in the form of cyber threats, which have the potential to damage critical infrastructure. False data injection attacks are among the threats to the cyber–physical layer of smart grids. False data injection attacks pose a significant risk, manipulating the data in the control system layer to compromise the grid’s integrity. An early detection and mitigation of such cyberattacks are crucial to ensuring the smart grid operates securely and reliably. In this research paper, we demonstrate different machine learning classification models for detecting false data injection attacks, including the Extra Tree, Random Forest, Extreme Gradient Boosting, Logistic Regression, Decision Tree, and Bagging Classifiers, to secure the integrity of smart grids. A comprehensive dataset of various attack scenarios provides insights to explore and develop effective detection models. Results show that the Extra Tree, Random Forest, and Extreme Gradient Boosting models’ accuracy in detecting the attack outperformed the existing literature, an achieving accuracy of 98%, 97%, and 97%, respectively.https://www.mdpi.com/1996-1073/17/23/5870smart gridfalse data injection attackcyber attackmachine learning |
| spellingShingle | Anwer Shees Mohd Tariq Arif I. Sarwat Cybersecurity in Smart Grids: Detecting False Data Injection Attacks Utilizing Supervised Machine Learning Techniques Energies smart grid false data injection attack cyber attack machine learning |
| title | Cybersecurity in Smart Grids: Detecting False Data Injection Attacks Utilizing Supervised Machine Learning Techniques |
| title_full | Cybersecurity in Smart Grids: Detecting False Data Injection Attacks Utilizing Supervised Machine Learning Techniques |
| title_fullStr | Cybersecurity in Smart Grids: Detecting False Data Injection Attacks Utilizing Supervised Machine Learning Techniques |
| title_full_unstemmed | Cybersecurity in Smart Grids: Detecting False Data Injection Attacks Utilizing Supervised Machine Learning Techniques |
| title_short | Cybersecurity in Smart Grids: Detecting False Data Injection Attacks Utilizing Supervised Machine Learning Techniques |
| title_sort | cybersecurity in smart grids detecting false data injection attacks utilizing supervised machine learning techniques |
| topic | smart grid false data injection attack cyber attack machine learning |
| url | https://www.mdpi.com/1996-1073/17/23/5870 |
| work_keys_str_mv | AT anwershees cybersecurityinsmartgridsdetectingfalsedatainjectionattacksutilizingsupervisedmachinelearningtechniques AT mohdtariq cybersecurityinsmartgridsdetectingfalsedatainjectionattacksutilizingsupervisedmachinelearningtechniques AT arifisarwat cybersecurityinsmartgridsdetectingfalsedatainjectionattacksutilizingsupervisedmachinelearningtechniques |