Performance Analysis of Attack detection in Vehicular Networks

Performance Analysis of Attack detection in Simulation of Vehicular Networks

Title: – Attack detection in Simulation of Vehicular Networks using Machine Learning
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Implementation Plan:
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Step 1: Initially, we start with configuring the SUMO Map.

Step 2: Next, we construct a Network consisting of 50-Vehicle, 4-RSU and 1-Gateway.

Step 3: Next, we implement the Identity-Based Cryptography (IBC) for Vehicle and RSU Communication and we will check trust worthiness for each vehicle using a Reputation Based System.

Step 4: Next, we Collect and Preprocess the simulation Data from the Vehicles.

Step 5: Next, we Train a Convolutional Neural Network (CNN) Model for the Attack Classification Process.

Step 6: Finally, we plot Performance for the following metrics:

6.1: Number of Epochs vs Accuracy (%)

6.2: Number of Epochs vs Precision (%)

6.3: Number of Epochs vs Recall (%)

6.4: Number of Epochs vs F1-Score (%)

6.5: False Positive Rate (%) vs True Positive Rate (%)

Software Requirements:
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1. Development Tools: Omnet++ 4.6 with python and above (if needed), SUMO

2. Operating System: Windows 10 [64-bit]

Note:
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1. We make a simulation based process only, not a real time process.

2. If the above plan does not satisfy your requirement, please provide the processing details, like the above step-by-step.

3. Please note that this implementation plan does not include any further steps after it is put into implementation.

4. If the above plan satisfies your requirement, please confirm us soon.

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