A resilient condition assessment monitoring system

Abstract

Abstract-An architecture and supporting methods are presented for the implementation of a resilient condition assessment monitoring system that can adaptively accommodate both cyber and physical anomalies to a monitored system under observation. In particular, the architecture includes three layers: information, assessment, and sensor selection. The information layer estimates probability distributions of process variables based on sensor measurements and assessments of the quality of sensor data. Based on these estimates, the assessment layer then employs probabilistic reasoning methods to assess the plant health. The sensor selection layer selects sensors so that assessments of the plant condition can be made within desired time periods. Resilient features of the developed system are then illustrated by simulations of a simplified power plant model, where a large portion of the sensors are under attack.Index Terms-Resilient systems, resilient monitoring, cyberphysical attacks, cyber/physical condition assessments, rational controllers, graceful degradation, measure of resiliency. I. INTRODUCTION A. MotivationComplex engineering systems need to be reliably monitored in order to ensure safety and proper operations. To this end, sensors are typically deployed within the monitored facility in order to observe the behavior of key process variables and access system conditions. Monitoring challenges include efficient processing of information and correct assessment of facility health despite possible natural or malicious disturbances. While natural disturbances can often be characterized reasonably well, malicious disturbances are illcharacterized. Regarding the latter, a significantly damaging disturbance to design against is the cyber-physical coordinated attack. In a cyber-physical coordinated attack, an attacker may cause a physical damage to the monitored facility and, furthermore, coordinately compromise the information layer via a cyber attack (e.g., by causing sensors to provide false readings of process variables) so as to confuse the operator of the actual plant health conditions. As intended by the attacker, a potential result may be that the operator, due to this confusion, takes a wrong decision, such as shutting down the monitored process or switching the plant to an inappropriate operating mode, while he/she otherwise could have gracefully maintained operations amid in a degraded mode, for example. Here, coordinated means that attacks occur at different locations of the monitoring system, while cyber-physical means that there are not only physical but also cyber attacks. A resilient monitoring system, which meets the above challenges, should possess the following properties:• exhibit graceful degradation in performance, as opposed to sudden collapse, under severe disturbances;

A General Distributed Architecture for Resilient Monitoring over Heterogeneous Networks

“…The work in [10] presents an intelligent resilient control algorithm for a wireless network control system based on the quantification of the concept of resiliency in terms of the quality of control. The authors developed an intelligent resilient control algorithm that maintains operational normalcy in face of wireless interference incidents, such as radio frequency jamming and signal blocking, while [11] proposes a resilient condition assessment monitoring system, able to dynamically adapt and reconfigure, depending on the assessed conditions.…”

Inclusion of game-theoretic formulations for resilient condition assessment monitoring

“…This section reviews the ReCAM system developed in [2], which is the specific resilient monitoring system under consideration here. We first give an overview of the ReCAM system, with Subsections II-A-II-D providing more detailed explanations.…”

“…Based on the difference between the predicted and desired decision periods, penalties are accordingly generated for the rational controllers to select sensor configurations for subsequent computation in the process variable assessment layer. We refer the reader to [2] for a complete description of this ReCAM system. We note that the game-theoretic approach developed in this paper improve operation of the ReCAM system by dynamically changing the sensor network at the bottom of Fig.…”

“…If, after reset, the assessment entropy is not able to decrease below the decision threshold within a time period defined by (1 + λ)DDP , where DDP is a (user defined) desired decision period and λ > 0 is a design parameter, then, at (1 + λ)DDP + 1, the current plant assessment PMF is reported to the plant operator and the belief of plant state is then reset to complete ignorance. We note that two modifications to the ReCAM system described in [2] have been made here. The first is the introduction of a stopping rule for the repeated use of BBNs.…”

A distributed multi-agent approach for resilient supervision over a IPv6 WSAN infrastructure

“…This kind of vulnerabilities can, to some extent, be mitigated by implementing dedicated "co-adjutants", which contribute to make the overall control system more robust and resilient. In this work, a resilient control system is considered as a control system that maintains state awareness and an acceptable level of operational normalcy in response to disturbances, including threats of an unexpected and malicious nature [7], or even intermittent communication breakdown. The implementation of resilient enforcement mechanisms can be carried out based on dedicated algorithms and heuristics within a multi-agent framework [8], [9].…”