In-Vehicle Network Intrusion Detection System Using Convolutional Neural Network and Multi-Scale Histograms
Cybersecurity in modern vehicles has received increased attention from the research community in recent years. Intrusion Detection Systems (IDSs) are one of the techniques used to detect and mitigate cybersecurity risks. This paper proposes a novel implementation of an IDS for in-vehicle security ne...
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| Format: | Article |
| Language: | English |
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MDPI AG
2023-11-01
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| Series: | Information |
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| Online Access: | https://www.mdpi.com/2078-2489/14/11/605 |
| _version_ | 1797458928559194112 |
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| author | Gianmarco Baldini |
| author_facet | Gianmarco Baldini |
| author_sort | Gianmarco Baldini |
| collection | DOAJ |
| description | Cybersecurity in modern vehicles has received increased attention from the research community in recent years. Intrusion Detection Systems (IDSs) are one of the techniques used to detect and mitigate cybersecurity risks. This paper proposes a novel implementation of an IDS for in-vehicle security networks based on the concept of multi-scale histograms, which capture the frequencies of message identifiers in CAN-bus in-vehicle networks. In comparison to existing approaches in the literature based on a single histogram, the proposed approach widens the informative context used by the IDS for traffic analysis by taking into consideration sequences of two and three CAN-bus messages to create multi-scale dictionaries. The histograms are created from windows of in-vehicle network traffic. A preliminary multi-scale histogram model is created using only legitimate traffic. Against this model, the IDS performs traffic analysis to create a feature space based on the correlation of the histograms. Then, the created feature space is given in input to a Convolutional Neural Network (CNN) for the identification of the windows of traffic where the attack is present. The proposed approach has been evaluated on two different public data sets achieving a very competitive performance in comparison to the literature. |
| first_indexed | 2024-03-09T16:44:11Z |
| format | Article |
| id | doaj.art-94dc048a89224d2ca3f2791904cf2c23 |
| institution | Directory Open Access Journal |
| issn | 2078-2489 |
| language | English |
| last_indexed | 2024-03-09T16:44:11Z |
| publishDate | 2023-11-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | Information |
| spelling | doaj.art-94dc048a89224d2ca3f2791904cf2c232023-11-24T14:48:14ZengMDPI AGInformation2078-24892023-11-01141160510.3390/info14110605In-Vehicle Network Intrusion Detection System Using Convolutional Neural Network and Multi-Scale HistogramsGianmarco Baldini0Joint Research Centre, European Commission, 21027 Ispra, ItalyCybersecurity in modern vehicles has received increased attention from the research community in recent years. Intrusion Detection Systems (IDSs) are one of the techniques used to detect and mitigate cybersecurity risks. This paper proposes a novel implementation of an IDS for in-vehicle security networks based on the concept of multi-scale histograms, which capture the frequencies of message identifiers in CAN-bus in-vehicle networks. In comparison to existing approaches in the literature based on a single histogram, the proposed approach widens the informative context used by the IDS for traffic analysis by taking into consideration sequences of two and three CAN-bus messages to create multi-scale dictionaries. The histograms are created from windows of in-vehicle network traffic. A preliminary multi-scale histogram model is created using only legitimate traffic. Against this model, the IDS performs traffic analysis to create a feature space based on the correlation of the histograms. Then, the created feature space is given in input to a Convolutional Neural Network (CNN) for the identification of the windows of traffic where the attack is present. The proposed approach has been evaluated on two different public data sets achieving a very competitive performance in comparison to the literature.https://www.mdpi.com/2078-2489/14/11/605cybersecurityautomotivedeep learningintrusion detection systems |
| spellingShingle | Gianmarco Baldini In-Vehicle Network Intrusion Detection System Using Convolutional Neural Network and Multi-Scale Histograms Information cybersecurity automotive deep learning intrusion detection systems |
| title | In-Vehicle Network Intrusion Detection System Using Convolutional Neural Network and Multi-Scale Histograms |
| title_full | In-Vehicle Network Intrusion Detection System Using Convolutional Neural Network and Multi-Scale Histograms |
| title_fullStr | In-Vehicle Network Intrusion Detection System Using Convolutional Neural Network and Multi-Scale Histograms |
| title_full_unstemmed | In-Vehicle Network Intrusion Detection System Using Convolutional Neural Network and Multi-Scale Histograms |
| title_short | In-Vehicle Network Intrusion Detection System Using Convolutional Neural Network and Multi-Scale Histograms |
| title_sort | in vehicle network intrusion detection system using convolutional neural network and multi scale histograms |
| topic | cybersecurity automotive deep learning intrusion detection systems |
| url | https://www.mdpi.com/2078-2489/14/11/605 |
| work_keys_str_mv | AT gianmarcobaldini invehiclenetworkintrusiondetectionsystemusingconvolutionalneuralnetworkandmultiscalehistograms |