ECC & ML Projects for Secure E-Commerce 🔒

Title:- Elliptic Curve Cryptography and Machine Learning for Secure E-Commerce Transactions

implementation plan

Step 1: Initially, we load and collect the data from the E-Commerce dataset.

Step 2: Then, we pre-process the collected data using Z-score normalization and SMOTE techniques.

Step 3: Next, we encrypt the data using hybrid Genetic-Optimized Elliptic Curve Elgamal with IDEA (GenEC-IDEA) for secured data transactions.

Step 4: Next, we perform ML-based fraud detection using BearBoost-LogiFraud with Gradient Boosting, Logistic Regression, and Brown-Bear Optimization (BBO).

Step 5: Finally, we plot performance for the following metrics:

5.1: Number of epochs vs. Accuracy (%)
5.2: Number of epochs vs. Precision (%)
5.3: Number of epochs vs. Recall (%)
5.4: Number of epochs vs. F1-score (%)
5.5: True positive rate (FPR) vs. False positive rate (TPR)


Software requirement:

1. Development Tool: Python 3.11.4 or above
2. Operating System: Windows 10 (64-bit) or above



Dataset:

Link :- https://www.kaggle.com/datasets/carrie1/ecommerce-data



Note

1) If the plan does not meet your requirements, provide detailed steps, parameters, models, or expected results in advance. Once implemented, changes won't be possible without prior input; otherwise, we'll proceed as per our implementation plan.

2) If the plan satisfies your requirement, Please confirm with us.

3) Project based on Simulation only, not a real time project.

4) If you have any changes in the Dataset , kindly provide before implementation.

5) Please understand that any modifications made to the confirmed implementation plan will not be made after the project development.

We perform with an Existing Approach : Reference 5: Title :- E-Commerce Fraud Detection Model by Computer Artificial Intelligence Data Mining
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