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
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]
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 () = 0.1473 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 () = 0.2023 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 () = 0.1867 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 () = 0.1664 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/).
Share and Cite
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
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 StyleKousar, 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 StyleKousar, 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