| Title | An Anomaly Detector for CAN Bus Networks in Autonomous Cars based on Neural Networks |
| Publication Type | Conference Paper |
| Year of Publication | 2019 |
| Authors | Boumiza, Safa, Braham, Rafik |
| Conference Name | 2019 International Conference on Wireless and Mobile Computing, Networking and Communications (WiMob) |
| Keywords | academic researchers, anomaly detection, anomaly detector, anomaly-detection techniques, Automotive engineering, autonomous cars, CAN bus networks, CAN packets, Computer hacking, controller area network security, controller area networks, Controller Area Networks bus, critical phase, Cyber-physical systems, Detectors, feature extraction, ID field, in-vehicle networks, industrial researchers, Internet of Things, Intrusion detection, intrusion detection method, intrusion detection system, MLP Neural Network, MultiLayer Perceptron neural network, multilayer perceptrons, Neural networks, pattern clustering, Protocols, pubcrawl, real-time detection, Resiliency, robust secure system, securing communication, security of data, wireless interfaces |
| Abstract | The domain of securing in-vehicle networks has attracted both academic and industrial researchers due to high danger of attacks on drivers and passengers. While securing wired and wireless interfaces is important to defend against these threats, detecting attacks is still the critical phase to construct a robust secure system. There are only a few results on securing communication inside vehicles using anomaly-detection techniques despite their efficiencies in systems that need real-time detection. Therefore, we propose an intrusion detection system (IDS) based on Multi-Layer Perceptron (MLP) neural network for Controller Area Networks (CAN) bus. This IDS divides data according to the ID field of CAN packets using K-means clustering algorithm, then it extracts suitable features and uses them to train and construct the neural network. The proposed IDS works for each ID separately and finally it combines their individual decisions to construct the final score and generates alert in the presence of attack. The strength of our intrusion detection method is that it works simultaneously for two types of attacks which will eliminate the use of several separate IDS and thus reduce the complexity and cost of implementation. |
| DOI | 10.1109/WiMOB.2019.8923315 |
| Citation Key | boumiza_anomaly_2019 |
An Anomaly Detector for CAN Bus Networks in Autonomous Cars based on Neural Networks