IOV's Intrsion-Detection-System Using CNN and Transfer Learning

This repository introduces how to

• Develop INTRUSION DETECTION SYSTEMS using -
  • convolutional neural networks (CNNs)
  • transfer learning techniques.
• Achieve optimized model performance using -
  • Ensemble learning
    • hyperparameter optimization techniques.

Abstract

Modern vehicles, including autonomous vehicles and connected vehicles, are increasingly connected to the external world, which enables various functionalities and services. However, the improving connectivity also increases the attack surfaces of the Internet of Vehicles (IoV), causing its vulnerabilities to cyber-threats.

• Due to the lack of authentication and encryption procedures in vehicular networks, Intrusion Detection Systems (IDSs) are essential approaches to protect modern vehicle systems from network attacks.

• A transfer learning and ensemble learning-based IDS is proposed for IoV systems using convolutional neural networks (CNNs) and hyper-parameter optimization techniques.

• In the experiments, the proposed IDS has demonstrated over 99.25% detection rates and F1-scores on two well-known public benchmark IoV security datasets:

  • Car-Hacking dataset .

• This shows the effectiveness of the proposed IDS for cyber-attack detection in both intra-vehicle and external vehicular networks.

Implementation

• CNN Models
• VGG16
• VGG19
• Xception
• Inception
• Resnet *InceptionResnet

Ensemble Learning Models

• Bagging
• Probability Averaging
• Concatenation

Hyperparameter Optimization Methods

• Random Search (RS)
• Bayesian Optimization - Tree Parzen Estimator(BO-TPE)

Dataset

CAN-intrusion/Car-Hacking dataset, a benchmark network security dataset for intra-vehicle intrusion detection Publicly available at: https://ocslab.hksecurity.net/Datasets/CAN-intrusion-dataset Can be processed using the same code