Runtime Verification with Particle Filtering

Abstract

Abstract. We introduce Runtime Verification with Particle Filtering (RVPF), a powerful and versatile method for controlling the tradeoff between uncertainty and overhead in runtime verification. Overhead and accuracy are controlled by adjusting the frequency and duration of observation gaps, during which program events are not monitored, and by adjusting the number of particles used in the RVPF algorithm. We succinctly represent the program model, the program monitor, their interaction, and their observations as a dynamic Bayesian network (DBN). Our formulation of RVPF in terms of DBNs is essential for a proper formalization of peek events: low-cost observations of parts of the program state, which are performed probabilistically at the end of observation gaps. Peek events provide information that our algorithm uses to reduce the uncertainty in the monitor state after gaps.We estimate the internal state of the DBN using particle filtering (PF) with sequential importance resampling (SIR). PF uses a collection of conceptual particles (random samples) to estimate the probability distribution for the system's current state: the probability of a state is given by the sum of the importance weights of the particles in that state. After an observed event, each particle chooses a state transition to execute by sampling the DBN's joint transition probability distribution; particles are then redistributed among the states that best predicted the current observation. SIR exploits the DBN structure and the current observation to reduce the variance of the PF and increase its performance.We experimentally compare the overhead and accuracy of our RVPF algorithm with two previous approaches to runtime verification with state estimation: an exact algorithm based on the forward algorithm for HMMs, and an approximate version of that algorithm, which uses precomputation to reduce runtime overhead. Our results confim RVPF's versatility, showing how it can be used to control the tradeoff between execution time and memory usage while, at the same time, being the most accurate of the three algorithms.

ModelPlex: verified runtime validation of verified cyber-physical system models

“…We, instead, use prediction on the basis of disturbed plant models for hybrid systems at runtime to ensure safety for future behavior of the system and switch to a fail-safe fallback controller if necessary. Adaptive runtime verification [4] uses state estimation to reduce monitoring overhead by sampling while still maintaining accuracy with Hidden Markov Models, or more recently, particle filtering [17] to fill the sampling gaps. The authors present interesting ideas for managing the overhead of runtime monitoring, which could be beneficial to transfer into the hybrid systems world.…”

“…-Unlike [4,17,19,22], who focus on the discrete aspects of CPS, we use hybrid system models with differential equations to address controller and plant. -Unlike [19,39], who assume that fail-safe controllers have been verified with some other approach and do not synthesize code, we can use the same technical approach (dL ) for verifying controllers and synthesizing provably correct monitors.…”

R2U2: monitoring and diagnosis of security threats for unmanned aerial systems

“…In R2U2, however, we use the SamIam tool for the graphical modeling of our Bayesian Network models. In Runtime Verification with Particle Filtering [19] the authors use dynamic Bayesian networks to model the program, the program monitor, their interaction, and their observations. They provide a method to control the tradeoff between uncertainty and overhead.…”

ModelPlex: Verified Runtime Validation of Verified Cyber-Physical System Models

“…We, instead, use prediction on the basis of disturbed plant mod-els for hybrid systems at runtime to ensure safety for future behavior of the system and switch to a fail-safe fallback controller if necessary. Adaptive runtime verification [4] uses state estimation to reduce monitoring overhead by sampling while still maintaining accuracy with Hidden Markov Models, or more recently, particle filtering [15] to fill the sampling gaps. The authors present interesting ideas for managing the overhead of runtime monitoring, which could be beneficial to transfer into the hybrid systems world.…”