Analysis of Attack Intensity on Autonomous Mobile Robots

Autonomous mobile robots (AMRs) combine a remarkable combination of mobility, adaptability, and an innate capacity for obstacle avoidance. They are exceptionally well-suited for a wide range of applications but usually operate in uncontrolled, non-deterministic environments, so the analysis and classification of security events are very important for their safe operation. In this regard, we considered the influence of different types of attacks on AMR navigation systems to subdivide them into classes and unified the effect of attacks on the system through their level of consequences and impact. Then, we built a model of an attack on a system, taking into account five methods of attack implementation and identified the unified response thresholds valid for any type of parameter, which allows for creating universal correlation rules and simplifies this process, as the trigger threshold is related to the degree of impact that the attack has on the finite subsystem. Also, we developed a methodology for classifying incidents and identifying key components of the system based on ontological models, which makes it possible to predict risks and select the optimal system configuration. The obtained results are important in the context of separating different types of destructive effects based on attack classes. Our study showed that it is sometimes difficult to divide spoofing attacks into classes by assessing only one parameter since the attacker can use a complex attack scenario, mixing the stages of the scenarios. We then showed how adding an attack intensity factor can make classification more flexible. The connections between subsystems and parameters, as well as the attack impact patterns, were determined. Finally, a set of unique rules was developed to classify destructive effects with uniform response thresholds for each parameter. In this case, we can increase the number of parameters as well as the type of parameter value.