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Related Experiment Video

Updated: Jul 4, 2026

Design and Analysis for Fall Detection System Simplification
08:05

Design and Analysis for Fall Detection System Simplification

Published on: April 6, 2020

Explainable & Deterministic Intrusion Detection for CAN-FD: A Logic Extraction Framework.

Rithvika G1, Radha R2

  • 1School of Computer Science and Engineering, Vellore Institute of Technology, Chennai, Tamil Nadu, India.

Scientific Reports
|July 2, 2026
PubMed
Summary
This summary is machine-generated.

This study introduces a novel framework for detecting cyberattacks in software-defined vehicles. It uses interpretable boolean rules for high-precision, low-latency intrusion detection in Controller Area Networks (CAN).

Keywords:
Automotive securityController area networkExplainable artificial intelligenceIntrusion detectionSafety-critical systems

Related Experiment Videos

Last Updated: Jul 4, 2026

Design and Analysis for Fall Detection System Simplification
08:05

Design and Analysis for Fall Detection System Simplification

Published on: April 6, 2020

Area of Science:

  • Cybersecurity
  • Automotive Engineering
  • Embedded Systems

Background:

  • Software-Defined Vehicles (SDVs) increase vulnerability to cyberattacks on internal networks like Controller Area Network (CAN).
  • Deep Learning Intrusion Detection Systems (DL-IDS) offer accuracy but lack the determinism and explainability required by automotive safety standards due to their black-box nature.
  • Existing solutions struggle to balance high detection rates with the strict latency and interpretability demands of safety-critical automotive systems.

Purpose of the Study:

  • To propose and validate a Multi-Standard (CAN/CAN-FD) logic extraction framework for reconciling high-precision intrusion detection with deterministic embedded system requirements.
  • To develop a white-box approach that generates interpretable boolean rules from complex attack signatures, ensuring deterministic, bounded worst-case execution time.
  • To demonstrate the framework's effectiveness on both legacy CAN and high-bandwidth CAN Flexible Data-rate (CAN-FD) traffic.

Main Methods:

  • Developed a logic extraction framework utilizing an elastic parsing mechanism.
  • Employed a constrained Classification and Regression Tree (CART) algorithm to distill attack signatures into compact, interpretable boolean rules.
  • Validated the framework on diverse datasets, including legacy CAN and high-bandwidth CAN-FD traffic.

Main Results:

  • The extracted boolean logic achieved a deterministic algorithmic inference latency as low as 0.15 microseconds on CAN-FD.
  • Demonstrated a 14× empirical speedup over optimized ML baselines (XGBoost) in an identical hardware environment.
  • Maintained an average detection accuracy above 99.97% for CAN-FD, while theoretically eliminating the computational overhead of deep learning models.

Conclusions:

  • The proposed framework successfully reconciles high-precision intrusion detection with the deterministic and interpretability requirements of automotive safety standards.
  • The white-box approach provides deterministic, bounded worst-case execution time, crucial for safety-critical embedded systems.
  • The generated interpretable boolean logic satisfies functional safety mandates and offers significant performance advantages over traditional ML and DL methods.