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Author(s):

Mrs. P. Uma Maheshwari | Swetha S | Riyashika R | Dhanupriya RD
Keywords:

Smart Meter, Electricity Theft Detection, Machine Learning, AES Encryption, Smart Grid Security
Abstract:
Electricity Theft Is One Of The Major Challenges Faced By Power Distribution Systems, Especially In Developing Countries Like India. Traditional Electricity Monitoring Systems Mainly Depend On Manual Inspection And Basic Monitoring Techniques, Which Are Inefficient In Identifying Real-time Power Theft And Cyber-attacks. Smart Meters Are Widely Used For Monitoring Electricity Consumption, But The Communication Between Smart Meters And Electricity Providers Is Vulnerable To Data Tampering And Unauthorized Access. This Paper Proposes GridShield, A Secure Smart Meter Communication And Intelligent Energy Theft Detection System Using Encryption And Machine Learning Techniques. The Proposed System Encrypts Smart Meter Data Using AES Encryption Before Transmitting It To The Server, Thereby Ensuring Secure Communication. Machine Learning Algorithms Are Used To Analyse Electricity Consumption Patterns And Identify Abnormal Usage Behaviour That May Indicate Electricity Theft. The System Provides Real-time Alerts To Electricity Authorities When Suspicious Activity Is Detected. The Proposed Solution Improves Smart Grid Security, Reduces Power Loss, Minimizes Manual Monitoring, And Increases Theft Detection Accuracy. The System Is Scalable And Suitable For Smart City And Smart Grid Environments.