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logoV-Secure: An Intelligent Data-Driven Model to Secure Intra-Vehicle Communications 🚗🛡️

Overview

This project aims to secure intra-vehicle communications using a dual-layer model combining image-based and data-based approaches. By leveraging deep learning and structured data analysis, the system can effectively detect and prevent cyber threats, ensuring robust vehicle network security.

Demo Video

demo.1.mp4

Features

Image-Based Model

Image-Based Model Visualization Image-Based Model Visualization

  • Pattern Recognition in Visual Data: Utilizes deep learning to analyze visual representations of network activity, such as CAN bus signal patterns or heatmaps, to detect unusual shapes or spikes that may indicate cyber threats.
  • Enhanced Detection of Anomalies: Identifies deviations in normal waveform patterns to spot attacks that alter typical signal flow, such as spoofing or injection attacks.

Data-Based Model

  • Structured Data Analysis: Processes network data (e.g., frequency, timing, and sequence of sensor readings and messages) to detect irregularities that could signal attacks like Man-in-the-Middle (MITM) or Denial of Service (DoS).
  • Adaptable to Data Types: Can analyze various structured data points from different Electronic Control Units (ECUs), allowing for a versatile approach across multiple vehicle components and communication protocols.

Integrated Dual-Layer Security

  • Cross-Model Validation: Combines outputs from both models to strengthen accuracy and minimize false positives, ensuring comprehensive detection across image and structured data.
  • Robust Against Complex Attacks: Capable of detecting both visual and non-visual anomalies, covering a wide range of potential intra-vehicle threats.

Common Cyber Threats

  • Fuzzy Attack: A cybersecurity testing method where random, malformed, or unexpected data is fed to software, systems, or devices to identify vulnerabilities and weaknesses.
  • Denial of Service (DoS) Attacks: Overloads the vehicle network with false data to disrupt essential functions like braking or steering control.
  • Spoofing Attacks (RPM and Gear): Injects false signals or messages into the network, impersonating legitimate components to mislead the vehicle system.

Detection Challenges

  • Subtle, Low-Signal Attacks: Sophisticated attackers often create small, unnoticeable anomalies, which are hard to distinguish from normal fluctuations in high-speed data flows.
  • High Data Volume: Vehicle networks generate and exchange massive amounts of data in real-time, making it difficult to identify attacks without causing system delays.
  • Time-Sensitive Processing: Attack detection must happen instantly to be effective, requiring highly optimized algorithms that balance security with processing speed.

Image-Based Models

MODEL NAME ACCURACY
CNN 0.9762
XCEPTION 1.00
VGG16 0.9989
VGG19 1.00
RESNET 0.98
INCEPTION 0.9996
INCEPTION RESNET 0.9999

Data-Based Models

MODEL NAME ACCURACY
Logistic Regression 0.9957
KNN 0.9999
Random Forest 1.00

Technologies Used

  • Programming Language: Python
  • Deep Learning Frameworks: TensorFlow, PyTorch
  • Visualization Tools: Matplotlib, Seaborn
  • Dataset: Car Hacking Dataset
  • Frontend: HTML, CSS, JavaScript
  • Backend: Flask

Installation

  1. Clone the repository: 
    git clone https://github.com/Rijul1607/V-Secure.git
  2. Navigate to the project directory: 
    cd V-Secure
  3. Install the required dependencies: 
    pip install -r requirements.txt

Usage

  1. Data Preprocessing: Use the provided scripts to preprocess both image and structured data.
  2. Model Training: Train the image-based and data-based models using the preprocessed data.
  3. Real-Time Monitoring: Deploy the dual-layer model for real-time intra-vehicle communication monitoring.

DataSet

Access the DataSet here: Database Repository

Contributing

Contributions are welcome! Please feel free to submit a Pull Request.

License

This project is licensed under the MIT License. See the LICENSE file for details.

Contact

For any questions or inquiries, please contact Rijul.