Biblio

The Controller area network (CAN) is the most extensively used in-vehicle network. It is set to enable communication between a number of electronic control units (ECU) that are widely found in most modern vehicles. CAN is the de facto in-vehicle network standard due to its error avoidance techniques and similar features, but it is vulnerable to various attacks. In this research, we propose a CAN bus intrusion detection system (IDS) based on convolutional neural networks (CNN). U-CAN is a segmentation model that is trained by monitoring CAN traffic data that are preprocessed using hamming distance and saliency detection algorithm. The model is trained and tested using publicly available datasets of raw and reverse-engineered CAN frames. With an F\_1 Score of 0.997, U-CAN can detect DoS, Fuzzy, spoofing gear, and spoofing RPM attacks of the publicly available raw CAN frames. The model trained on reverse-engineered CAN signals that contain plateau attacks also results in a true positive rate and false-positive rate of 0.971 and 0.998, respectively.

The Controller Area Network (CAN) bus is the de-facto standard for connecting the Electronic Control Units (ECUs) in automobiles. However, there are serious cyber-security risks due to the lack of security mechanisms. In order to mine the vulnerabilities in CAN bus, this paper proposes CAN-FT, a fuzz testing method for automotive CAN bus, which uses a Generative Adversarial Network (GAN) based fuzzy message generation algorithm and the Adaptive Boosting (AdaBoost) based anomaly detection mechanism to capture the abnormal states of CAN bus. Experimental results on a real-world vehicle show that CAN-FT can find vulnerabilities more efficiently and comprehensively.

Parallel with the developing world, transportation technologies have started to expand and change significantly year by year. This change brings with it some inevitable problems. Increasing human population and growing transportation-needs result many accidents in urban and rural areas, and this recursively results extra traffic problems and fuel consumption. It is obvious that the issues brought by this spiral loop needed to be solved with the use of some new technological achievements. In this context, self-driving cars or automated vehicles concepts are seen as a good solution. However, this also brings some additional problems with it. Currently many cars are provided with some digital security systems, which are examined in two phases, internal and external. These systems are constructed in the car by using some type of embedded system (such as the Controller Area Network (CAN)) which are needed to be protected form outsider cyberattacks. These attack can be detected by several ways such as rule based system, anomaly based systems, list based systems, etc. The current literature showed that researchers focused on the use of some artificial intelligence techniques for the detection of this type of attack. In this study, an intrusion detection system based on machine learning is proposed for the CAN security, which is the in-vehicle communication structure. As a result of the study, it has been observed that the decision tree-based ensemble learning models results the best performance in the tested models. Additionally, all models have a very good accuracy levels.

Cyber attacks against automobiles have increased over the last decade due to the expansion in attack surfaces. This is the result of modern automobiles having connections such as Bluetooth, WiFi, and other broadband services. While there has been numerous proposed solutions in the literature, none have been widely adopted as maintaining real-time message deliverability in the Controller Area Networks (CAN) outweighs proposed security solutions. Through iterative research, we have developed a solution which mitigates an attacker's impact on the CAN bus by using CAN's inherent features of arbitration, error detection and signaling, and fault confinement mechanism. The solution relies on an access controller and message priority thresholds added to the CAN data-link layer. The results provide no time delay for non-malicious traffic and mitigates bus impact of a subverted node attempting to fabricate messages at an unauthorized priority level.

Controller area networks (CANs) are vulnerable to spoofing attacks such as frame falsifying attacks, as electronic control units (ECUs) send and receive messages without any authentication and encryption. In this paper, we propose a physical authentication scheme that exploits the voltage features of the ECU signals on the CAN bus and applies reinforcement learning to choose the authentication mode such as the protection level and test threshold. This scheme enables a monitor node to optimize the authentication mode via trial-and-error without knowing the CAN bus signal model and spoofing model. Experimental results show that the proposed authentication scheme can significantly improve the authentication accuracy and response compared with a benchmark scheme.

Cyber-physical systems (CPSs) encompass a network of sensors and actuators that are monitored, controlled and integrated by a computing and communication core. As autonomous underwater vehicles (AUVs) become more intelligent and connected, new use cases in ocean production, security and environmental monitoring become feasible. Swarms of small, affordable and hydrobatic AUVs can be beneficial in substance cloud tracking and algae farming, and a CPS linking the AUVs with multi-fidelity simulations can improve performance while reducing risks and costs. In this paper, we present a CPS concept tightly linking the AUV network in ROS to virtual validation using Simulink and Gazebo. A robust hardware-software interface using the open-source UAVCAN-ROS bridge is described for enabling hardware-in-the-loop validation. Hardware features of the hydrobatic SAM AUV are described, with a focus on subsystem integration. Results presented include pre-tuning of controllers, validation of mission plans in simulation and real time subsystem performance in tank tests. These first results demonstrate the interconnection between different system elements and offer a proof of concept.

Modern vehicles rely on hundreds of on-board electronic control units (ECUs) communicating over in-vehicle networks. As external interfaces to the car control networks (such as the on-board diagnostic (OBD) port, auxiliary media ports, etc.) become common, and vehicle-to-vehicle / vehicle-to-infrastructure technology is in the near future, the attack surface for vehicles grows, exposing control networks to potentially life-critical attacks. This paper addresses the need for securing the controller area network (CAN) bus by detecting anomalous traffic patterns via unusual refresh rates of certain commands. While previous works have identified signal frequency as an important feature for CAN bus intrusion detection, this paper provides the first such algorithm with experiments using three attacks in five (total) scenarios. Our data-driven anomaly detection algorithm requires only five seconds of training time (on normal data) and achieves true positive / false discovery rates of 0.9998/0.00298, respectively (micro-averaged across the five experimental tests).