Abstract
While recent studies addressed security attacks in real-time embedded systems, most of them assumed prior knowledge of parameters of periodic tasks, which is not realistic under many environments. In this paper, we address how to infer task parameters, from restricted information obtained by simple system monitoring. To this end, we first develop static properties that are independent of inference results and therefore applied only once in the beginning. We further develop dynamic properties each of which can tighten inference results by feeding an update of the inference results obtained by other properties. Our simulation results demonstrate that the proposed inference framework infers task parameters for RM (Rate Monotonic) with reasonable tightness; the ratio of exactly inferred task periods is 95.3% and 65.6%, respectively with low and high task set use. The results also discover that the inference performance varies with the monitoring interval length and the task set use.
Highlights
Real-Time Embedded Systems (RTES) have been deployed in time-critical environments, often involving control tasks each of which invokes a series of jobs with periodic releases, which has been widely studied in the industrial informatics community [1,2,3,4,5,6]
The proposed framework aims at inferring task parameters ( the period (Ti), case execution time (Ci) ) for every task executed in the monitoring interval MI, so as to facilitate other security attacks
Despite difficulties due to lack of knowledge of job release times, task/job priority ordering, and distribution of actual execution times, we showed that the framework can effectively narrow down the feasible range of task parameters
Summary
Real-Time Embedded Systems (RTES) have been deployed in time-critical environments, often involving control tasks each of which invokes a series of jobs with periodic releases, which has been widely studied in the industrial informatics community [1,2,3,4,5,6]. We propose a framework to infer task parameters, only from information about the task index of currently-executing jobs, and we apply the framework to RM (Rate Monotonic) [18]; once developed, the framework makes it possible to predict future job release and execution patterns of a given RTES so as to facilitate security attacks. To this end, we develop two types of properties that can narrow down (a) the feasible range of each task period and (b) the feasible group of each task’s execution chunks that belong to the same job.
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