A new method for the detection and identification of the replay attack on cars using SDR technology and classification algorithms
Context: Currently, a wide variety of cars use Remote Keyless Entry (RKE) to activate the security system by radio frequency. Problem: RKE systems have security vulnerabilities with attacks such as brute force, replay, spoofing, and replay. Objective: This study proposes a replay-type attack detecti...
| Main Authors: | , , |
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| Format: | Article |
| Language: | English |
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Elsevier
2023-09-01
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| Series: | Results in Engineering |
| Subjects: | |
| Online Access: | http://www.sciencedirect.com/science/article/pii/S2590123023003705 |
| _version_ | 1797682463758090240 |
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| author | Juan Carlos Martinez Quintero Edith Paola Estupiñan Cuesta Leonardo Juan Ramirez Lopez |
| author_facet | Juan Carlos Martinez Quintero Edith Paola Estupiñan Cuesta Leonardo Juan Ramirez Lopez |
| author_sort | Juan Carlos Martinez Quintero |
| collection | DOAJ |
| description | Context: Currently, a wide variety of cars use Remote Keyless Entry (RKE) to activate the security system by radio frequency. Problem: RKE systems have security vulnerabilities with attacks such as brute force, replay, spoofing, and replay. Objective: This study proposes a replay-type attack detection system using SDR and intelligent algorithms. Method: The present dataset was obtained from an RKES type Key-fob of a 2016 model vehicle taken with different antenna polarizations at different distances. There were used three test and configuration systems based on two neural network identification models. Results: The proposed designs with the different classification models were able to detect replay-type vulnerabilities. In the first system, retransmitted indicators were detected with a maximum identification percentage of 90.9% and original signs of 90.9%; for the second system, 100% of the retransmitted signals and 89% of the original ones were detected, and for the third system, detected 100% of the retransmitted signals and 89.09% of the first signs. Conclusion: The models used in the system's design showed an identification percentage of more than 87% in all cases, managing to detect and identify the replay-type attack on the vehicles. System 1 presented better results with the VGG 16 system than in system 2, within the VGG 16 and SVM models having identical identification percentages obtained and, in system 3, which has the characteristics of the previous two, attained better results with SVM. The reception of signals with SDR is an effective tool for detecting vulnerabilities in vehicle security systems. |
| first_indexed | 2024-03-12T00:00:05Z |
| format | Article |
| id | doaj.art-630c6a74624a4afd98fa759bb6a71832 |
| institution | Directory Open Access Journal |
| issn | 2590-1230 |
| language | English |
| last_indexed | 2024-03-12T00:00:05Z |
| publishDate | 2023-09-01 |
| publisher | Elsevier |
| record_format | Article |
| series | Results in Engineering |
| spelling | doaj.art-630c6a74624a4afd98fa759bb6a718322023-09-18T04:30:30ZengElsevierResults in Engineering2590-12302023-09-0119101243A new method for the detection and identification of the replay attack on cars using SDR technology and classification algorithmsJuan Carlos Martinez Quintero0Edith Paola Estupiñan Cuesta1Leonardo Juan Ramirez Lopez2GISSIC Research Group, Faculty of Engineering, Universidad Militar Nueva Granada, Bogotá, Colombia; Corresponding author.GISSIC Research Group, Faculty of Engineering, Universidad Militar Nueva Granada, Bogotá, ColombiaTIGUM Research Group, Faculty of Engineering, Universidad Militar Nueva Granada, Bogotá, ColombiaContext: Currently, a wide variety of cars use Remote Keyless Entry (RKE) to activate the security system by radio frequency. Problem: RKE systems have security vulnerabilities with attacks such as brute force, replay, spoofing, and replay. Objective: This study proposes a replay-type attack detection system using SDR and intelligent algorithms. Method: The present dataset was obtained from an RKES type Key-fob of a 2016 model vehicle taken with different antenna polarizations at different distances. There were used three test and configuration systems based on two neural network identification models. Results: The proposed designs with the different classification models were able to detect replay-type vulnerabilities. In the first system, retransmitted indicators were detected with a maximum identification percentage of 90.9% and original signs of 90.9%; for the second system, 100% of the retransmitted signals and 89% of the original ones were detected, and for the third system, detected 100% of the retransmitted signals and 89.09% of the first signs. Conclusion: The models used in the system's design showed an identification percentage of more than 87% in all cases, managing to detect and identify the replay-type attack on the vehicles. System 1 presented better results with the VGG 16 system than in system 2, within the VGG 16 and SVM models having identical identification percentages obtained and, in system 3, which has the characteristics of the previous two, attained better results with SVM. The reception of signals with SDR is an effective tool for detecting vulnerabilities in vehicle security systems.http://www.sciencedirect.com/science/article/pii/S2590123023003705Replay attackSoftware defined radioVGGSVMAttack |
| spellingShingle | Juan Carlos Martinez Quintero Edith Paola Estupiñan Cuesta Leonardo Juan Ramirez Lopez A new method for the detection and identification of the replay attack on cars using SDR technology and classification algorithms Results in Engineering Replay attack Software defined radio VGG SVM Attack |
| title | A new method for the detection and identification of the replay attack on cars using SDR technology and classification algorithms |
| title_full | A new method for the detection and identification of the replay attack on cars using SDR technology and classification algorithms |
| title_fullStr | A new method for the detection and identification of the replay attack on cars using SDR technology and classification algorithms |
| title_full_unstemmed | A new method for the detection and identification of the replay attack on cars using SDR technology and classification algorithms |
| title_short | A new method for the detection and identification of the replay attack on cars using SDR technology and classification algorithms |
| title_sort | new method for the detection and identification of the replay attack on cars using sdr technology and classification algorithms |
| topic | Replay attack Software defined radio VGG SVM Attack |
| url | http://www.sciencedirect.com/science/article/pii/S2590123023003705 |
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