Q-FuzzyNet: A Quantum Inspired QIF-RNN and SA-FPA Optimizer for Intrusion Detection and Mitigation in Intelligent Connected Vehicles

In the evolving landscape of Intelligent Connected Vehicles (ICVs), ensuring cybersecurity is crucial due to the increasing number of cyber threats. Besides, challenges like data breaches, unauthorized access, and hacking attempts are prevalent due to the interconnected nature of ICVs. Several methods have been proposed to secure ICVs; however, accurate intrusion detection remains a challenging task yet to be fully achieved. For this reason, this paper proposes a comprehensive intrusion detection scheme denoted a Q-FuzzyNet, which is specifically tailored to safeguard ICV networks using Deep Learning (DL) approaches. This Q-FuzzyNet approach consists of five phases: (i) Data Collection (ii) Data Pre-processing (iii) Feature extraction (iv) Dimensionality Reduction and (v) Intrusion Detection and Mitigation. Initially, the raw data are gathered from the CICIoV2024 dataset. The collected data are pre-processed via Mean Imputation (MI) for data cleaning. Then, significant features are extracted through higher-order statistical features, Proposed Improved Mutual Information (IMI), Correlation, and Entropy approaches. Subsequently, the dimensionality is reduced via new Improved Linear Discriminant Analysis (ILDA). Ultimately, the data are classified (attacker/Normal) via the Meta-Heuristic Quantum-Inspired Fuzzy-Recursive Neural Network (QIF-RNN) model by combining the Quantum Neural Network (QNN), Recurrent Neural Network (RNN), and Fuzzy logic. The membership function of fuzzy logic is optimized via the new Self Adaptive-Flower Pollination Algorithm (SA-FPA). The identified attackers are mitigated from the network using the Policy Gradient Method. The acquired outcomes from Q-FuzzyNet are validated in terms of Accuracy, Precision, Sensitivity, and F1-score, as well. The highest accuracy of 98.6% has been recorded by the proposed model.

@article{Alenizi2024,
  title     = {Q-FuzzyNet: A Quantum Inspired QIF-RNN and SA-FPA Optimizer for Intrusion Detection and Mitigation in Intelligent Connected Vehicles},
  journal   = {International Journal of Advanced Computer Science and Applications},
  volume    = {15},
  number    = {11},
  year      = {2024},
  publisher = {The Science and Information Organization},
  author    = {Abdullah Alenizi},
  doi       = {10.14569/IJACSA.2024.0151122},
  url       = {https://doi.org/10.14569/IJACSA.2024.0151122}
}