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Correction

Correction: Kousar et al. A Deep Learning Approach for Real-Time Intrusion Mitigation in Automotive Controller Area Networks. World Electr. Veh. J. 2025, 16, 492

1
Department of Electrical Engineering, Mirpur University of Science and Technology (MUST), Mipur AJK-10250, Pakistan
2
School of Computing and Engineering, University of Huddersfield, Queensgate, Huddersfield HD1 3DH, UK
*
Author to whom correspondence should be addressed.
World Electr. Veh. J. 2025, 16(10), 549; https://doi.org/10.3390/wevj16100549
Submission received: 17 September 2025 / Accepted: 18 September 2025 / Published: 24 September 2025
Error in Table
In the original publication [1], there was a mistake in Table 18. Some data points on Row # 39 and # 40 in Table 18 were missing inadvertently. The corrected Table 18 appears below. The authors state that the scientific conclusions are unaffected. This correction was approved by the Academic Editor. The original publication has also been updated.

Reference

  1. Kousar, A.; Ahmed, S.; Khan, Z.A. A Deep Learning Approach for Real-Time Intrusion Mitigation in Automotive Controller Area Networks. World Electr. Veh. J. 2025, 16, 492. [Google Scholar] [CrossRef]
Table 18. Representation of original data reconstruction by GANs for Dataset 2.
Table 18. Representation of original data reconstruction by GANs for Dataset 2.
DoS Intrusion
Best Reconstructed Data
Original Value0.0078431370.047058820.0047846890.0196078430.003968254
Reconstructed Value0.00758610.04656960.00372330.01833250.0024771
Reconstruction Error0.000257060.000489240.00106140.00127540.0014912
Error Ratio0.0327750.0103960.221820.0650430.37578
Worst Reconstructed Data
Original Value0.9682539680.9843137250.9790575920.9882352940.996078431
Reconstructed Value0.00896330.00640300.0009805550.00616390.0012052
Reconstruction Error0.959290.977910.978080.982070.99487
Error Ratio0.990740.993490.9990.993760.99879
Reconstruction Error ( μ ± σ ) = 0.1473 ± 0.1349
Fuzzy Intrusion
Best Reconstructed Data
Original Value0.1176470590.113725490.0352941180.0666666670.074509804
Reconstructed Value0.11765970.11370340.03521240.06654760.0746459
Reconstruction Error1.2592 × 10−52.2083 × 10−58.1761 × 10−51.191 × 10−41.3612 × 10−4
Error Ratio0.000107030.000194180.00231660.00178650.0018268
Worst Reconstructed Data
Original Value0.9803921570.9490196080.9294117650.9450980390.976470588
Reconstructed Value0.13129010.09843430.07265540.05529330.0850940
Reconstruction Error0.84910.850590.856760.88980.89138
Error Ratio0.866080.896280.921830.941490.91286
Reconstruction Error ( μ ± σ ) = 0.2023 ± 0.0922
Spoofing (Gear) Intrusion
Best Reconstructed Data
Original Value0.0047846890.0196078430.1780104710.0047846890.062745098
Reconstructed Value0.00469320.01971560.17812140.00452820.0623568
Reconstruction Error9.1474 × 10−51.0777 × 10−41.1096 × 10−42.5652 × 10−43.8834 × 10−4
Error Ratio0.0191180.00549630.000623340.0536130.0061891
Worst Reconstructed Data
Original Value0.913725490.9294117650.9647058820.9764705880.945098039
Reconstructed Value0.00920110.00494510.03224660.04014720.0066587
Reconstruction Error0.904520.924470.932460.936320.93844
Error Ratio0.989930.994680.966570.958890.99295
Reconstruction Error ( μ ± σ ) = 0.1867 ± 0.0849
Spoofing (RPM) Intrusion
Best Reconstructed Data
Original Value0.1411764710.8823529410.1254901960.0313725490.141176471
Reconstructed Value0.14122810.88241740.12531470.03117870.1414160
Reconstruction Error0.0000515940.000064440.000175540.000193880.00023951
Error Ratio0.000365460.0000730320.00139890.006180.0016965
Worst Reconstructed Data
Original Value0.9803921570.9568627450.9843137250.9882352940.996078431
Reconstructed Value0.13350450.10354330.12783260.07530570.0731756
Reconstruction Error0.846890.853320.856480.912930.9229
Error Ratio0.863830.891790.870130.92380.92654
Reconstruction Error ( μ ± σ ) = 0.1664 ± 0.0739
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MDPI and ACS Style

Kousar, A.; Ahmed, S.; Khan, Z.A. Correction: Kousar et al. A Deep Learning Approach for Real-Time Intrusion Mitigation in Automotive Controller Area Networks. World Electr. Veh. J. 2025, 16, 492. World Electr. Veh. J. 2025, 16, 549. https://doi.org/10.3390/wevj16100549

AMA Style

Kousar A, Ahmed S, Khan ZA. Correction: Kousar et al. A Deep Learning Approach for Real-Time Intrusion Mitigation in Automotive Controller Area Networks. World Electr. Veh. J. 2025, 16, 492. World Electric Vehicle Journal. 2025; 16(10):549. https://doi.org/10.3390/wevj16100549

Chicago/Turabian Style

Kousar, Anila, Saeed Ahmed, and Zafar A. Khan. 2025. "Correction: Kousar et al. A Deep Learning Approach for Real-Time Intrusion Mitigation in Automotive Controller Area Networks. World Electr. Veh. J. 2025, 16, 492" World Electric Vehicle Journal 16, no. 10: 549. https://doi.org/10.3390/wevj16100549

APA Style

Kousar, A., Ahmed, S., & Khan, Z. A. (2025). Correction: Kousar et al. A Deep Learning Approach for Real-Time Intrusion Mitigation in Automotive Controller Area Networks. World Electr. Veh. J. 2025, 16, 492. World Electric Vehicle Journal, 16(10), 549. https://doi.org/10.3390/wevj16100549

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