CF-AIDS: Comprehensive Frequency-Agnostic Intrusion Detection System on In-Vehicle Network
Many studies have focused on obtaining high accuracy in the design of Intrusion Detection Systems (IDS) for in-vehicle networks, neglecting the significance of different intensive packet injection techniques. Because of their reliance on scenario-specific training datasets, these IDSs are vulnerable...
| Main Authors: | , , , , |
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| Format: | Article |
| Language: | English |
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IEEE
2024-01-01
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| Series: | IEEE Access |
| Subjects: | |
| Online Access: | https://ieeexplore.ieee.org/document/10373830/ |
| _version_ | 1827368505574424576 |
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| author | Md. Rezanur Islam Mahdi Sahlabadi Keunkyoung Kim Yoonji Kim Kangbin Yim |
| author_facet | Md. Rezanur Islam Mahdi Sahlabadi Keunkyoung Kim Yoonji Kim Kangbin Yim |
| author_sort | Md. Rezanur Islam |
| collection | DOAJ |
| description | Many studies have focused on obtaining high accuracy in the design of Intrusion Detection Systems (IDS) for in-vehicle networks, neglecting the significance of different intensive packet injection techniques. Because of their reliance on scenario-specific training datasets, these IDSs are vulnerable to failing to detect real-world attacks. This study implemented deep learning (DL)–based classification for intrusion detection using a Gated Recurrent Unit (GRU) while considering various intrusion frequencies. Different intrusion frequencies are comprehensively addressed with frequency-agnostic intrusion and resolved by generalizing features for DL input through time series segmentation and frequency domain conversion using Gabor filtering. For training purposes, five types of vehicle data are used, encompassing DoS, fuzzing, and replay attack scenarios. The accuracy range for mechanical version vehicles is typically between 95% and 100%. For electronic vehicles, it is around 90%. Considering the nature of this IDS system, it has been named a Comprehensive Frequency-Agnostic Intrusion Detection System (CF-AIDS). Although this IDS can perform better in all aspects, achieving more efficient results requires a larger amount of situational data. |
| first_indexed | 2024-03-08T09:32:29Z |
| format | Article |
| id | doaj.art-f9d59e7c40ae4e9ea962b246aa307250 |
| institution | Directory Open Access Journal |
| issn | 2169-3536 |
| language | English |
| last_indexed | 2024-03-08T09:32:29Z |
| publishDate | 2024-01-01 |
| publisher | IEEE |
| record_format | Article |
| series | IEEE Access |
| spelling | doaj.art-f9d59e7c40ae4e9ea962b246aa3072502024-01-31T00:00:48ZengIEEEIEEE Access2169-35362024-01-0112139711398510.1109/ACCESS.2023.334694310373830CF-AIDS: Comprehensive Frequency-Agnostic Intrusion Detection System on In-Vehicle NetworkMd. Rezanur Islam0https://orcid.org/0000-0002-1183-7741Mahdi Sahlabadi1Keunkyoung Kim2Yoonji Kim3Kangbin Yim4https://orcid.org/0000-0002-1361-1455Department of Software Convergence, Soonchunhyang University, Asan, South KoreaDepartment of Information Security Engineering, Soonchunhyang University, Asan, South KoreaDepartment of Software Convergence, Soonchunhyang University, Asan, South KoreaDepartment of Mobility Convergence Security, Soonchunhyang University, Asan, South KoreaDepartment of Information Security Engineering, Soonchunhyang University, Asan, South KoreaMany studies have focused on obtaining high accuracy in the design of Intrusion Detection Systems (IDS) for in-vehicle networks, neglecting the significance of different intensive packet injection techniques. Because of their reliance on scenario-specific training datasets, these IDSs are vulnerable to failing to detect real-world attacks. This study implemented deep learning (DL)–based classification for intrusion detection using a Gated Recurrent Unit (GRU) while considering various intrusion frequencies. Different intrusion frequencies are comprehensively addressed with frequency-agnostic intrusion and resolved by generalizing features for DL input through time series segmentation and frequency domain conversion using Gabor filtering. For training purposes, five types of vehicle data are used, encompassing DoS, fuzzing, and replay attack scenarios. The accuracy range for mechanical version vehicles is typically between 95% and 100%. For electronic vehicles, it is around 90%. Considering the nature of this IDS system, it has been named a Comprehensive Frequency-Agnostic Intrusion Detection System (CF-AIDS). Although this IDS can perform better in all aspects, achieving more efficient results requires a larger amount of situational data.https://ieeexplore.ieee.org/document/10373830/In-vehicle networkCANIDSGabor transformfrequency-agnostic |
| spellingShingle | Md. Rezanur Islam Mahdi Sahlabadi Keunkyoung Kim Yoonji Kim Kangbin Yim CF-AIDS: Comprehensive Frequency-Agnostic Intrusion Detection System on In-Vehicle Network IEEE Access In-vehicle network CAN IDS Gabor transform frequency-agnostic |
| title | CF-AIDS: Comprehensive Frequency-Agnostic Intrusion Detection System on In-Vehicle Network |
| title_full | CF-AIDS: Comprehensive Frequency-Agnostic Intrusion Detection System on In-Vehicle Network |
| title_fullStr | CF-AIDS: Comprehensive Frequency-Agnostic Intrusion Detection System on In-Vehicle Network |
| title_full_unstemmed | CF-AIDS: Comprehensive Frequency-Agnostic Intrusion Detection System on In-Vehicle Network |
| title_short | CF-AIDS: Comprehensive Frequency-Agnostic Intrusion Detection System on In-Vehicle Network |
| title_sort | cf aids comprehensive frequency agnostic intrusion detection system on in vehicle network |
| topic | In-vehicle network CAN IDS Gabor transform frequency-agnostic |
| url | https://ieeexplore.ieee.org/document/10373830/ |
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