Abstract :

The Smart Grid Integrates Advanced Communication Technologies, Intelligent Monitoring Systems, And Automated Control Mechanisms To Enhance The Efficiency, Reliability, And Sustainability Of Modern Power Distribution Networks. However, The Increasing Connectivity And Digitalization Of Smart Grid Infrastructures Have Also Exposed Them To Various Cyber Threats, Including Denial-of-service Attacks, False Data Injection Attacks, Malware Infections, And Unauthorized Access Attempts. These Attacks Can Disrupt Grid Operations, Compromise Data Integrity, And Threaten The Stability Of Power Systems. Traditional Security Mechanisms Often Struggle To Detect Sophisticated And Evolving Cyberattacks In Real Time. This Paper Presents A Comprehensive Study Of Machine Learning Methods For Attack Detection In The Smart Grid. The Proposed Framework Utilizes Network Traffic Data, Operational Measurements, Sensor Readings, And System Logs To Identify Abnormal Activities And Malicious Behaviors. Machine Learning Algorithms Are Employed To Learn Normal Grid Operations And Classify Potential Attacks Based On Extracted Features And Behavioral Patterns. Experimental Analysis Demonstrates That Machine Learningbased Detection Methods Significantly Improve Attack Identification Accuracy, Reduce False Alarm Rates, And Enhance The Resilience Of Smart Grid Infrastructures. The Proposed Approach Provides An Intelligent And Scalable Solution For Strengthening Cybersecurity In Modern Energy Systems.

Published:

26-9-2026

Issue:

Vol. 26 No. 9 (2026)

Page Nos:

440-447

Section:

Articles

License:

This work is licensed under a Creative Commons Attribution-NonCommercial-NoDerivatives 4.0 International License.

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