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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1. #VehicularNetworks
2. #AttackDetection
3. #PerformanceAnalysis
4. #NetworkSecurity
5. #SimulationResearch
6. #Cybersecurity
7. #SmartVehicles
8. #DataProtection
9. #TrafficSafety
10. #IoTSecurity
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We possess a team of professional researchers who specialize in the subsequent research domains
• Wireless sensor network
• IOT
• VANET
• SDN
• Network Security
• Wireless communication

The above mentioned video serves as a guide in conducting omnet manual research work.
Reach us at: https://omnet-manual.com/
mail us at : [email protected]
call us at : +91 9444856435