Biblio
The Robot Operating System (ROS) are being deployed for multiple life critical activities such as self-driving cars, drones, and industries. However, the security has been persistently neglected, especially the image flows incoming from camera robots. In this paper, we perform a structured security assessment of robot cameras using ROS. We points out a relevant number of security flaws that can be used to take over the flows incoming from the robot cameras. Furthermore, we propose an intrusion detection system to detect abnormal flows. Our defense approach is based on images comparisons and unsupervised anomaly detection method. We experiment our approach on robot cameras embedded on a self-driving car.
Vehicles are becoming increasingly connected to the outside world. We can connect our devices to the vehicle's infotainment system and internet is being added as a functionality. Therefore, security is a major concern as the attack surface has become much larger than before. Consequently, attackers are creating malware that can infect vehicles and perform life-threatening activities. For example, a malware can compromise vehicle ECUs and cause unexpected consequences. Hence, ensuring the security of connected vehicle software and networks is extremely important to gain consumer confidence and foster the growth of this emerging market. In this paper, we propose a characterization of vehicle malware and a security architecture to protect vehicle from these malware. The architecture uses multiple computational platforms and makes use of the virtualization technique to limit the attack surface. There is a real-time operating system to control critical vehicle functionalities and multiple other operating systems for non-critical functionalities (infotainment, telematics, etc.). The security architecture also describes groups of components for the operating systems to prevent malicious activities and perform policing (monitor, detect, and control). We believe this work will help automakers guard their systems against malware and provide a clear guideline for future research.
In most produced modern vehicles, Passive Keyless Entry and Start System (PKES), a newer form of an entry access system, is becoming more and more popular. The PKES system allows the consumer to enter within a certain range and have the vehicle's doors unlock automatically without pressing any buttons on the key. This technology increases the overall convenience to the consumer; however, it is vulnerable to attacks known as relay and amplified relay attacks. A relay attack consists of placing a device near the vehicle and a device near the key to relay the signal between the key and the vehicle. On the other hand, an amplified relay attack uses only a singular amplifier to increase the range of the vehicle sensors to reach the key. By exploiting these two different vulnerabilities within the PKES system, an attacker can gain unauthorized access to the vehicle, leading to damage or even stolen property. To minimize both vulnerabilities, we propose a coordinate tracing system with an additional Bluetooth communication channel. The coordinate tracing system, or PKES Forcefield, traces the authorized key's longitude and latitude in real time using two proposed algorithms, known as the Key Bearing algorithm and the Longitude and Latitude Key (LLK) algorithm. To further add security, a Bluetooth communication channel will be implemented. With an additional channel established, a second frequency can be traced within a secondary PKES Forcefield. The LLK Algorithm computes both locations of frequencies and analyzes the results to form a pattern. Furthermore, the PKES Forcefield movement-tracing allows a vehicle to understand when an attacker attempts to transmit an unauthenticated signal and blocks any signal from being amplified over a fixed range.
Modern vehicles are opening up, with wireless interfaces such as Bluetooth integrated in order to enable comfort and safety features. Furthermore a plethora of aftermarket devices introduce additional connectivity which contributes to the driving experience. This connectivity opens the vehicle to potentially malicious attacks, which could have negative consequences with regards to safety. In this paper, we survey vehicles with Bluetooth connectivity from a threat intelligence perspective to gain insight into conditions during real world driving. We do this in two ways: firstly, by examining Bluetooth implementation in vehicles and gathering information from inside the cabin, and secondly, using war-nibbling (general monitoring and scanning for nearby devices). We find that as the vehicle age decreases, the security (relatively speaking) of the Bluetooth implementation increases, but that there is still some technological lag with regards to Bluetooth implementation in vehicles. We also find that a large proportion of vehicles and aftermarket devices still use legacy pairing (and are therefore more insecure), and that these vehicles remain visible for sufficient time to mount an attack (assuming some premeditation and preparation). We demonstrate a real-world threat scenario as an example of the latter. Finally, we provide some recommendations on how the security risks we discover could be mitigated.
In this paper, we propose SAFE (Security Aware FlexRay scheduling Engine), to provide a problem definition and a design framework for FlexRay static segment schedule to address the new challenge on security. From a high level specification of the application, the architecture and communication middleware are synthesized to satisfy security requirements, in addition to extensibility, costs, and end-to-end latencies. The proposed design process is applied to two industrial case studies consisting of a set of active safety functions and an X-by-wire system respectively.