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DDoS attack detection in intelligent transport systems using adaptive neuro-fuzzy inference system.

G Usha1,2, H Karthikeyan1,2, Kumar Gautam3,4

  • 1Department of Networking and Communications, SRMIST, Kattankulathur, Chennai, 603203, India.

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Summary

This study introduces a novel Distributed Denial of Service attack detection scheme for Intelligent Transportation Systems (ITS). The proposed Adaptive Neuro-Fuzzy Inference System enhances ITS security with 94.3% accuracy.

Keywords:
Adaptive neuro-fuzzy inference systemDistributed denial of service attackFuzzy logicIntelligent transportation systemsIntrusion detection systemVehicular network security

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Area of Science:

  • Computer Science
  • Network Security
  • Artificial Intelligence

Background:

  • Intelligent Transportation Systems (ITS) are vital for traffic management and public benefit.
  • ITS operate in dynamic environments, making them vulnerable to threats like Distributed Denial of Service (DDoS) attacks.
  • Current DDoS detection methods in ITS have flaws, necessitating improved security solutions.

Purpose of the Study:

  • To propose a robust Distributed Denial of Service attack detection scheme for Intelligent Transportation Systems.
  • To enhance the security and reliability of the ITS ecosystem against cyber threats.
  • To address the limitations of existing DDoS detection techniques in ITS.

Main Methods:

  • Development of a Distributed Denial of Service attack detection scheme utilizing the Adaptive Neuro-Fuzzy Inference System (ANFIS).
  • Integration of artificial neural network learning capabilities with fuzzy logic models within the ANFIS framework.
  • Comparative analysis against traditional classifiers including Support Vector Machine, Random Forest, Extreme Gradient Boosting, and Convolutional Neural Network.

Main Results:

  • The proposed ANFIS-based model achieved a high accuracy of 94.3% in detecting DDoS attacks.
  • Demonstrated superior performance compared to Support Vector Machine, Random Forest, Extreme Gradient Boosting, and Convolutional Neural Network.
  • Exhibited low false positive rates and high detection reliability, confirming its suitability for real-world ITS security.

Conclusions:

  • The Adaptive Neuro-Fuzzy Inference System offers a highly effective solution for enhancing Intelligent Transportation Systems security.
  • The proposed scheme provides significant improvements in accuracy, precision, recall, and F1 score for DDoS attack detection.
  • The developed system is well-suited for real-world applications, ensuring the continuous and secure operation of ITS.