Performance Analysis of CYBERSECURITY IN SMART ENERGY GRIDS
Implementation Plan:
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Step 1: Initially, we will collect and load the IEEE 118-bus CPPS Dataset.
Step 2: Then, we will preprocess the collected data by normalizing values, handling missing entries, and label fault/attack types and organizing features (Voltage, Frequency, Phase Angle) and labels for Training process.
Step 3: Next, we will train the collected data using Federated Learning ML Model to detect the intrusion.
Step 4: Next, we will aggregate readings from smart grid nodes without revealing raw data using Secure Multi-Party Computation (SMPC) protocols based on collected data.
Step 5: Next, we will use the blockchain to record transactions, alerts and AI based prediction with Zero-Knowledge Proofs for Tamper-proof verification based on collected data.
Step 6: Next, we will integrate the software-based trusted computing mechanism based on collected data.
Step 7: Finally, we plot performance for the following metrics:
7.1: Number of Epochs vs. Accuracy (%)
7.2: Number of Epochs vs. Precision (%)
7.3: Time vs. Memory Usage (MB)
7.4: Number of Transaction vs. Throughput (Mbps)
7.5: Number of Transaction vs. Latency (ms)
Software Requirements:
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1. Development Tool: Python 3.11.x or above version
2. Operating System: Windows 10 (64-bit) or above
Dataset Link:
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Link: https://github.com/HosseinH24/IEEE118-Bus-PowerGridData
Note:
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1) If the proposed plan does not fully align with your requirements, please provide all necessary details—including steps, parameters, models, and expected outcomes—in advance. Kindly ensure that any missing configurations or specifications are clearly outlined in the plan before confirming.
2) If there’s no built-in solution for what the project needs, we can always turn to reference models, customize our own, different math models or write the code ourselves to fulfil the process.
3) If the plan satisfies your requirement, Please confirm with us.
4) Project based on Simulation only.
