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

Anila Kousar; Saeed Ahmed; Zafar A. Khan

doi:10.3390/wevj16100549

,

and

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

Version Notes

Order Reprints

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.

Table 18. Representation of original data reconstruction by GANs for Dataset 2.

Reference

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.

DoS Intrusion
Best Reconstructed Data
Original Value	0.007843137	0.04705882	0.004784689	0.019607843	0.003968254
Reconstructed Value	0.0075861	0.0465696	0.0037233	0.0183325	0.0024771
Reconstruction Error	0.00025706	0.00048924	0.0010614	0.0012754	0.0014912
Error Ratio	0.032775	0.010396	0.22182	0.065043	0.37578
Worst Reconstructed Data
Original Value	0.968253968	0.984313725	0.979057592	0.988235294	0.996078431
Reconstructed Value	0.0089633	0.0064030	0.000980555	0.0061639	0.0012052
Reconstruction Error	0.95929	0.97791	0.97808	0.98207	0.99487
Error Ratio	0.99074	0.99349	0.999	0.99376	0.99879
Reconstruction Error ( $μ \pm σ$ ) = 0.1473 $\pm$ 0.1349
Fuzzy Intrusion
Best Reconstructed Data
Original Value	0.117647059	0.11372549	0.035294118	0.066666667	0.074509804
Reconstructed Value	0.1176597	0.1137034	0.0352124	0.0665476	0.0746459
Reconstruction Error	1.2592 × 10⁻⁵	2.2083 × 10⁻⁵	8.1761 × 10⁻⁵	1.191 × 10⁻⁴	1.3612 × 10⁻⁴
Error Ratio	0.00010703	0.00019418	0.0023166	0.0017865	0.0018268
Worst Reconstructed Data
Original Value	0.980392157	0.949019608	0.929411765	0.945098039	0.976470588
Reconstructed Value	0.1312901	0.0984343	0.0726554	0.0552933	0.0850940
Reconstruction Error	0.8491	0.85059	0.85676	0.8898	0.89138
Error Ratio	0.86608	0.89628	0.92183	0.94149	0.91286
Reconstruction Error ( $μ \pm σ$ ) = 0.2023 $\pm$ 0.0922
Spoofing (Gear) Intrusion
Best Reconstructed Data
Original Value	0.004784689	0.019607843	0.178010471	0.004784689	0.062745098
Reconstructed Value	0.0046932	0.0197156	0.1781214	0.0045282	0.0623568
Reconstruction Error	9.1474 × 10⁻⁵	1.0777 × 10⁻⁴	1.1096 × 10⁻⁴	2.5652 × 10⁻⁴	3.8834 × 10⁻⁴
Error Ratio	0.019118	0.0054963	0.00062334	0.053613	0.0061891
Worst Reconstructed Data
Original Value	0.91372549	0.929411765	0.964705882	0.976470588	0.945098039
Reconstructed Value	0.0092011	0.0049451	0.0322466	0.0401472	0.0066587
Reconstruction Error	0.90452	0.92447	0.93246	0.93632	0.93844
Error Ratio	0.98993	0.99468	0.96657	0.95889	0.99295
Reconstruction Error ( $μ \pm σ$ ) = 0.1867 $\pm$ 0.0849
Spoofing (RPM) Intrusion
Best Reconstructed Data
Original Value	0.141176471	0.882352941	0.125490196	0.031372549	0.141176471
Reconstructed Value	0.1412281	0.8824174	0.1253147	0.0311787	0.1414160
Reconstruction Error	0.000051594	0.00006444	0.00017554	0.00019388	0.00023951
Error Ratio	0.00036546	0.000073032	0.0013989	0.00618	0.0016965
Worst Reconstructed Data
Original Value	0.980392157	0.956862745	0.984313725	0.988235294	0.996078431
Reconstructed Value	0.1335045	0.1035433	0.1278326	0.0753057	0.0731756
Reconstruction Error	0.84689	0.85332	0.85648	0.91293	0.9229
Error Ratio	0.86383	0.89179	0.87013	0.9238	0.92654
Reconstruction Error ( $μ \pm σ$ ) = 0.1664 $\pm$ 0.0739

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© 2025 by the authors. Published by MDPI on behalf of the World Electric Vehicle Association. Licensee MDPI, Basel, Switzerland. This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY) license (https://creativecommons.org/licenses/by/4.0/).

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

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