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Updated: Aug 1, 2025

Author Spotlight: Advancing Alzheimer's Research &#8211; Exploring Early Detection and Multi-Omics Approaches
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Cyber Attack Detection for Self-Driving Vehicle Networks Using Deep Autoencoder Algorithms.

Fawaz Waselallah Alsaade1, Mosleh Hmoud Al-Adhaileh2

  • 1College of Computer Science and Information Technology, King Faisal University, P.O. Box 4000, Al-Ahsa 7057, Saudia Arabia.

Sensors (Basel, Switzerland)
|April 28, 2023
PubMed
Summary
This summary is machine-generated.

Machine learning and deep learning effectively detect cyber threats in connected and autonomous vehicles (CAVs). These advanced methods identify malicious data in in-vehicle networks (IVNs), enhancing vehicle security against attacks like spoofing and flooding.

Keywords:
artificial intelligencecontroller area networkin-vehicle networksintrusion detection systemsecurity

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Area of Science:

  • Cybersecurity
  • Machine Learning
  • Deep Learning
  • Automotive Engineering

Background:

  • Connected and autonomous vehicles (CAVs) rely on complex electronic control units (ECUs) networked via in-vehicle networks (IVNs).
  • These networks are vulnerable to cyber threats, including data manipulation, which can compromise vehicle safety and efficiency.
  • Existing security measures may not adequately address the sophisticated nature of modern cyberattacks on automotive systems.

Purpose of the Study:

  • To investigate the efficacy of machine learning and deep learning techniques for defending against cyber threats in CAVs.
  • To focus on the detection of erroneous information injected into the data buses of automotive electronic control units (ECUs).
  • To evaluate the performance of various algorithms in identifying and classifying malicious data packets within in-vehicle networks (IVNs).

Main Methods:

  • Utilized machine learning (gradient boosting, k-nearest neighbour, decision trees) and deep learning (LSTM, deep autoencoders) algorithms.
  • Pre-processed real-world datasets (Car-Hacking, UNSE-NB15) including benign and attack traffic (spoofing, flooding, replay).
  • Trained and validated models on automotive network data to detect various cyberattacks within the in-vehicle network (IVN).

Main Results:

  • Decision tree and k-nearest neighbour (KNN) achieved high accuracy (98.80% and 99%, respectively).
  • Deep learning models, LSTM and deep autoencoder, demonstrated strong performance (96% and 99.98% accuracy, respectively).
  • The deep autoencoder model achieved a determination coefficient of R² = 95%, indicating robust detection capabilities.

Conclusions:

  • Machine learning and deep learning algorithms significantly outperform existing methods for detecting in-vehicle network (IVN) cyber threats.
  • The developed security solution, particularly using decision tree and deep autoencoder, offers near-perfect accuracy in identifying malicious data.
  • This research provides a robust framework for enhancing the cybersecurity of connected and autonomous vehicles (CAVs).