CoMID: Context-based Multi-Invariant Detection for Monitoring Cyber-Physical Software
This work addresses runtime monitoring for cyber-physical software to prevent failures, offering incremental improvements over existing methods.
The paper tackles the problem of runtime monitoring for cyber-physical software to detect abnormal states caused by environmental errors, presenting CoMID, which achieves a 5.7-28.2% higher true-positive rate and a 6.8-37.6% lower false-positive rate compared to state-of-the-art approaches, and improves task execution success rates by 15.3-31.7% in field tests.
Cyber-physical software continually interacts with its physical environment for adaptation in order to deliver smart services. However, the interactions can be subject to various errors when the software's assumption on its environment no longer holds, thus leading to unexpected misbehavior or even failure. To address this problem, one promising way is to conduct runtime monitoring of invariants, so as to prevent cyber-physical software from entering such errors (a.k.a. abnormal states). To effectively detect abnormal states, we in this article present an approach, named Context-based Multi-Invariant Detection (CoMID), which consists of two techniques: context-based trace grouping and multi-invariant detection. The former infers contexts to distinguish different effective scopes for CoMID's derived invariants, and the latter conducts ensemble evaluation of multiple invariants to detect abnormal states. We experimentally evaluate CoMID on real-world cyber-physical software. The results show that CoMID achieves a 5.7-28.2% higher true-positive rate and a 6.8-37.6% lower false-positive rate in detecting abnormal states, as compared with state-of-the-art approaches (i.e., Daikon and ZoomIn). When deployed in field tests, CoMID's runtime monitoring improves the success rate of cyber-physical software in its task executions by 15.3-31.7%.