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<p>Modern connected vehicles face increased cyber threats due to expanded IoVattack surfaces. Robust intrusion detection systems are essential to counter vulnerabilities from weak authentication and encryption. Traditional methods are inadequate, therefore, an advanced real-time intrusion detection system (IDS) is needed to detect and mitigate evolving cyber-attacks in connected vehicles. To overcome these complications, optimized PRNN-ENet for robust IDS in IoV networks (IDS-PRNN-ENet-IoV) is proposed. The input data is collected from car hacking dataset. The gathered dataare provided to the preprocessing phase. Here, fast guided median filter is used to normalize the data. Afterward, the pre-processed data are fed into the physically recurrent neural network (PRNN) with EfficientCovNet (PRNN-ENet),which classifies and detects the intrusion asnormal, DoS, fuzzy, gear spoofing and RPM spoofing. Finally, a bitterling fish optimization algorithm is employed to optimize the weight parameters of PRNN-ENet. The IDS-PRNN-ENet-IoV technique is executed in Python and the metrics like accuracy, recall, f1-score is examined. The proposed IDS-PRNN-ENet-IoV achieves 6.14%, 7.13%, 6.06% higher accuracy, 7.28%, 10.24% and5.39% higher recall and 5.10%, 5.20% and7.48% higher f1-score when compared with the existing techniques.</p>

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Optimized PRNN-ENet for robust IDS in IoV networks

  • 期刊论文

  • 发表日期：

    2026年5月17日

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  • Mukund B. Wagh,
  • Vishnu A. Suryawanshi,
  • Rambhau B. Lagdive,
  • Surendra K. Waghmare,
  • Suvarna S. Pawar

摘要

Modern connected vehicles face increased cyber threats due to expanded IoVattack surfaces. Robust intrusion detection systems are essential to counter vulnerabilities from weak authentication and encryption. Traditional methods are inadequate, therefore, an advanced real-time intrusion detection system (IDS) is needed to detect and mitigate evolving cyber-attacks in connected vehicles. To overcome these complications, optimized PRNN-ENet for robust IDS in IoV networks (IDS-PRNN-ENet-IoV) is proposed. The input data is collected from car hacking dataset. The gathered dataare provided to the preprocessing phase. Here, fast guided median filter is used to normalize the data. Afterward, the pre-processed data are fed into the physically recurrent neural network (PRNN) with EfficientCovNet (PRNN-ENet),which classifies and detects the intrusion asnormal, DoS, fuzzy, gear spoofing and RPM spoofing. Finally, a bitterling fish optimization algorithm is employed to optimize the weight parameters of PRNN-ENet. The IDS-PRNN-ENet-IoV technique is executed in Python and the metrics like accuracy, recall, f1-score is examined. The proposed IDS-PRNN-ENet-IoV achieves 6.14%, 7.13%, 6.06% higher accuracy, 7.28%, 10.24% and5.39% higher recall and 5.10%, 5.20% and7.48% higher f1-score when compared with the existing techniques.