Performance Analysis of Neural Transformations for Anomaly Detection
Implementation Plan:
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Step 1: Initially, we collect and load data from Enron Fraud Email dataset.
Step 2: Then, we Convert raw timestamps into time series sequences per user for modeling temporal patterns.
Step 3: Next, we Perform exploratory analysis to identify trends, cycles, and user-specific communication behavior.
Step 4: Next, we evaluate traditional forecasting data using LSTM-AD .
Step 5: Next, we Implement the NeutralAD with contrastive learning for anomaly detection.
Step 6: Next, we implement Train baseline TSAD to Train the model using normal patterns and augmentations to learn semantic deviations.
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: Number of epochs vs. Recall (%)
7.4: Number of epochs vs. F1-score (%)
Software Requirements:
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1. Development Tool: Python 3.12.9
2. Operating System: Windows-11 (64-bit)
Dataset:
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Link: https://www.kaggle.com/datasets/advaithsrao/enron-fraud-email-dataset
Note :-
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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. Our work is completely based on dataset values.
5) Please understand that any modifications made to the confirmed implementation plan will not be made after the project development.
