| Title | ECU Fingerprinting through Parametric Signal Modeling and Artificial Neural Networks for In-vehicle Security against Spoofing Attacks |
| Publication Type | Conference Paper |
| Year of Publication | 2019 |
| Authors | Hafeez, Azeem, Topolovec, Kenneth, Awad, Selim |
| Conference Name | 2019 15th International Computer Engineering Conference (ICENCO) |
| Date Published | dec |
| Keywords | artificial neural network (ANN), Artificial neural networks, Autonomous vehicles, basic security features, CAN messages, CAN network, channel imperfections, channel-specific step response, Collaboration, communication system, computer network security, confidentiality, connected autonomous vehicles, control system parameters, Controller area network (CAN), controller area network protocol, controller area networks, cryptographic protocols, data theft, ECU fingerprinting, eight-channel lengths, electronic control unit (ECU), feature-set, Image edge detection, in-vehicle control networks, in-vehicle security, Intrusion Detection System (IDS), lumped element model, Lumped element model (LEM), machine learning (ML), message authentication, Metrics, modern electric vehicles, neural nets, Neural Network, Neural Network Security, Neural networks, on-board communications, parametric signal modeling, performance matrix (PM), Physical layer, policy-based governance, Protocols, pubcrawl, received signal, receiver operating characteristic (ROC), spoofing attacks, telecommunication security, traditional cybersecurity methods, Transfer functions, transmitted messages, transmitter, Transmitters, transmitting electronic control unit, unique transfer function |
| Abstract | Fully connected autonomous vehicles are more vulnerable than ever to hacking and data theft. The controller area network (CAN) protocol is used for communication between in-vehicle control networks (IVN). The absence of basic security features of this protocol, like message authentication, makes it quite vulnerable to a wide range of attacks including spoofing attacks. As traditional cybersecurity methods impose limitations in ensuring confidentiality and integrity of transmitted messages via CAN, a new technique has emerged among others to approve its reliability in fully authenticating the CAN messages. At the physical layer of the communication system, the method of fingerprinting the messages is implemented to link the received signal to the transmitting electronic control unit (ECU). This paper introduces a new method to implement the security of modern electric vehicles. The lumped element model is used to characterize the channel-specific step response. ECU and channel imperfections lead to a unique transfer function for each transmitter. Due to the unique transfer function, the step response for each transmitter is unique. In this paper, we use control system parameters as a feature-set, afterward, a neural network is used transmitting node identification for message authentication. A dataset collected from a CAN network with eight-channel lengths and eight ECUs to evaluate the performance of the suggested method. Detection results show that the proposed method achieves an accuracy of 97.4% of transmitter detection. |
| DOI | 10.1109/ICENCO48310.2019.9027298 |
| Citation Key | hafeez_ecu_2019 |
ECU Fingerprinting through Parametric Signal Modeling and Artificial Neural Networks for In-vehicle Security against Spoofing Attacks