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A Multiple Rényi Entropy Based Intrusion Detection System for Connected Vehicles.

Ki-Soon Yu1, Sung-Hyun Kim2, Dae-Woon Lim1

  • 1Major in Information Communication Engineering, Dongguk University, Seoul 04620, Korea.

Entropy (Basel, Switzerland)
|December 8, 2020
PubMed
Summary

This study introduces a novel intrusion detection system (IDS) for vehicular networks using Rényi entropy. The system effectively identifies network attacks with less than 1% false positive and negative rates.

Keywords:
Rényi entropyShannon entropyconnected vehiclesintrusion detection system (IDS)vehicular network

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Area of Science:

  • Cybersecurity
  • Information Theory
  • Network Security

Background:

  • Vehicular networks face increasing cybersecurity threats.
  • Existing intrusion detection systems (IDS) may not adequately address the complexities of vehicular communication protocols.
  • Rényi entropy offers a generalized framework for information measurement, potentially enhancing anomaly detection.

Purpose of the Study:

  • To develop and evaluate a novel intrusion detection system (IDS) for vehicular networks.
  • To leverage the estimation of Rényi entropy with multiple orders for improved attack detection accuracy.
  • To specifically target network intrusions within Controller Area Network (CAN) bus systems.

Main Methods:

  • Utilized an efficient Rényi entropy estimation method for arbitrary real orders.
  • Implemented a multi-order Rényi entropy estimation (orders 2, 3, and 4) based on Controller Area Network (CAN)-IDs.
  • Employed a retrospective sliding window method for attack decision-making based on estimated entropy values.

Main Results:

  • The proposed IDS demonstrated high accuracy in detecting intrusions.
  • Achieved simultaneous false negative and false positive error rates below 1%.
  • Validated performance using a recognized CAN-ID attack dataset.

Conclusions:

  • The multi-order Rényi entropy-based IDS is a promising approach for securing vehicular networks.
  • The method provides a robust and accurate solution for detecting network attacks in automotive systems.
  • The low error rates indicate the system's reliability in real-world applications.