Abstract-Intrusion detection systems designed for conventional computer systems and networks are not necessarily suitable for mobile cyber-physical systems, such as robots, drones and automobiles. They tend to be geared towards attacks of different nature and do not take into account mobility, energy consumption and other physical aspects that are vital to a mobile cyber-physical system. We have developed a decision tree-based method for detecting cyber attacks on a small-scale robotic vehicle using both cyber and physical features that can be measured by its on-board systems and processes. We evaluate it experimentally against a variety of scenarios involving denial of service, command injection and two types of malware attacks. We observe that the addition of physical features noticeably improves the detection accuracy for two of the four attack types and reduces the detection latency for all four.
With the growing threat of cyber and cyber-physical attacks against automobiles, drones, ships, driverless pods and other vehicles, there is also a growing need for intrusion detection approaches that can facilitate defence against such threats. Vehicles tend to have limited processing resources and are energy-constrained. So, any security provision needs to abide by these limitations. At the same time, attacks against vehicles are very rare, often making knowledge-based intrusion detection systems less practical than behaviour-based ones, which is the reverse of what is seen in conventional computing systems. Furthermore, vehicle design and implementation can differ wildly between different types or different manufacturers, which can lead to intrusion detection designs that are vehicle-specific. Equally importantly, vehicles are practically defined by their ability to move, autonomously or not. Movement, as well as other physical manifestations of their operation may allow cyber security breaches to lead to physical damage, but can also be an opportunity for detection. For example, physical sensing can contribute to more accurate or more rapid intrusion detection through observation and analysis of physical manifestations of a security breach. This paper presents a classification and survey of intrusion detection systems designed and evaluated specifically on vehicles and networks of vehicles. Its aim is to help identify existing techniques that can be adopted in the industry, along with their advantages and disadvantages, as well as to identify gaps in the literature, which are attractive and highly meaningful areas of future research.
Abstract-Mobile cyber-physical systems, such as automobiles, drones and robotic vehicles, are gradually becoming attractive targets for cyber attacks. This is a challenge because intrusion detection systems built for conventional computer systems tend to be unsuitable. They can be too demanding for resource-restricted cyber-physical systems or too inaccurate due to the lack of realworld data on actual attack behaviours. Here, we focus on the security of a small remote-controlled robotic vehicle. Having observed that certain types of cyber attacks against it exhibit physical impact, we have developed an intrusion detection system that takes into account not only cyber input features, such as network traffic and disk data, but also physical input features, such as speed, physical jittering and power consumption. As the system is resource-restricted, we have opted for a decision tree-based approach for generating simple detection rules, which we evaluate against denial of service and command injection attacks. We observe that the addition of physical input features can markedly reduce the false positive rate and increase the overall accuracy of the detection.
Abstract-Responding to an emergency situation is a challenging and time critical procedure. The primary goal is to save lives and this is directly related to the speed and efficiency at which help is provided to the victims. Rescue robots are able to benefit an emergency response procedure by searching for survivors, providing access to inaccessible areas and establishing an on-site communication network. This paper investigates how a cyber attack on a rescue robot can adversely affect its operation and impair an emergency response operation. The focus is on identifying physical indicators of an ongoing cyber attack, which can help to design more efficient detection and defense mechanisms. A number of experiments have been conducted on an Arduino based robot, under different cyber attack scenarios. The results show that the cyber attack's effects have physical features that can be used in order to improve the robot's robustness against this type of threat.
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