Design of Anomaly Detection Functions for Controller Area Networks
Vehicles are becoming increasingly autonomous and connected, leading to an increase in the types of security threats to vehicles. Controller Area Network (CAN) is a serial bus system that is used to connect sensors and controllers (Electronic Control Units – ECUs) within a vehicle. ECUs v...
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| Format: | Article |
| Language: | English |
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IEEE
2021-01-01
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| Series: | IEEE Open Journal of Intelligent Transportation Systems |
| Subjects: | |
| Online Access: | https://ieeexplore.ieee.org/document/9512278/ |
| _version_ | 1818717075310379008 |
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| author | Vinayak Tanksale |
| author_facet | Vinayak Tanksale |
| author_sort | Vinayak Tanksale |
| collection | DOAJ |
| description | Vehicles are becoming increasingly autonomous and connected, leading to an increase in the types of security threats to vehicles. Controller Area Network (CAN) is a serial bus system that is used to connect sensors and controllers (Electronic Control Units – ECUs) within a vehicle. ECUs vary widely in processing power, storage, memory, and connectivity. There is a need for efficient security countermeasures for protecting the CAN from various attacks. In this paper, we present a novel process to efficiently design functions that can be used for anomaly detection. Our earlier work successfully demonstrated the use of Long Short-Term Memory (LSTM) Networks to perform anomaly detection. This paper focuses on the efficient design and testing of functions that are attack-resistant and can be used in our anomaly detection engine. Once trained, our system is capable of efficiently detecting anomalies in real-time. We report the results of our anomaly detection function design process. We also present the results of our overall anomaly detection engine that are used as inputs to our detection engine. Our function design process and anomaly detection engine have been tested on data from real automobiles. We present the results of our experiments and analyze our findings. |
| first_indexed | 2024-12-17T19:29:23Z |
| format | Article |
| id | doaj.art-8a74c98bc9dc4478b7062a0670b51924 |
| institution | Directory Open Access Journal |
| issn | 2687-7813 |
| language | English |
| last_indexed | 2024-12-17T19:29:23Z |
| publishDate | 2021-01-01 |
| publisher | IEEE |
| record_format | Article |
| series | IEEE Open Journal of Intelligent Transportation Systems |
| spelling | doaj.art-8a74c98bc9dc4478b7062a0670b519242022-12-21T21:35:17ZengIEEEIEEE Open Journal of Intelligent Transportation Systems2687-78132021-01-01231232110.1109/OJITS.2021.31044959512278Design of Anomaly Detection Functions for Controller Area NetworksVinayak Tanksale0https://orcid.org/0000-0002-5775-6146Department of Computer Science, Ball State University, Muncie, IN, USAVehicles are becoming increasingly autonomous and connected, leading to an increase in the types of security threats to vehicles. Controller Area Network (CAN) is a serial bus system that is used to connect sensors and controllers (Electronic Control Units – ECUs) within a vehicle. ECUs vary widely in processing power, storage, memory, and connectivity. There is a need for efficient security countermeasures for protecting the CAN from various attacks. In this paper, we present a novel process to efficiently design functions that can be used for anomaly detection. Our earlier work successfully demonstrated the use of Long Short-Term Memory (LSTM) Networks to perform anomaly detection. This paper focuses on the efficient design and testing of functions that are attack-resistant and can be used in our anomaly detection engine. Once trained, our system is capable of efficiently detecting anomalies in real-time. We report the results of our anomaly detection function design process. We also present the results of our overall anomaly detection engine that are used as inputs to our detection engine. Our function design process and anomaly detection engine have been tested on data from real automobiles. We present the results of our experiments and analyze our findings.https://ieeexplore.ieee.org/document/9512278/Controller area networklong short-term memoryintrusion detection |
| spellingShingle | Vinayak Tanksale Design of Anomaly Detection Functions for Controller Area Networks IEEE Open Journal of Intelligent Transportation Systems Controller area network long short-term memory intrusion detection |
| title | Design of Anomaly Detection Functions for Controller Area Networks |
| title_full | Design of Anomaly Detection Functions for Controller Area Networks |
| title_fullStr | Design of Anomaly Detection Functions for Controller Area Networks |
| title_full_unstemmed | Design of Anomaly Detection Functions for Controller Area Networks |
| title_short | Design of Anomaly Detection Functions for Controller Area Networks |
| title_sort | design of anomaly detection functions for controller area networks |
| topic | Controller area network long short-term memory intrusion detection |
| url | https://ieeexplore.ieee.org/document/9512278/ |
| work_keys_str_mv | AT vinayaktanksale designofanomalydetectionfunctionsforcontrollerareanetworks |