Likelihood Ratio Based Scheduler for Secure Detection in Cyber Physical Systems

TL;DR

This paper introduces a likelihood ratio based scheduler for secure detection in cyber-physical systems, enabling effective measurement selection under communication constraints and cyber attacks, with performance close to full data use.

Contribution

The paper proposes a novel LRB scheduler that improves detection performance and security in cyber-physical systems under communication and attack constraints.

Findings

Optimal LRB scheduler achieves near full-measurement detection performance.

LRB scheduler outperforms random scheduling under attack.

Performance degrades gracefully with attack intensity.

Abstract

This paper is concerned with a binary detection problem over a non-secure network. To satisfy the communication rate constraint and against possible cyber attacks, which are modeled as deceptive signals injected to the network, a likelihood ratio based (LRB) scheduler is designed in the sensor side to smartly select sensor measurements for transmission. By exploring the scheduler, some sensor measurements are successfully retrieved from the attacked data at the decision center. We show that even under a moderate communication rate constraint of secure networks, an optimal LRB scheduler can achieve a comparable asymptotic detection performance to the standard N-P test using the full set of measurements, and is strictly better than the random scheduler. For non-secure networks, the LRB scheduler can also maintain the detection functionality but suffers graceful performance degradation under different attack intensities. Finally, we perform simulations to validate our theoretical results.