Configuration Monitor Synthesis

• URL: http://arxiv.org/abs/2408.17368v1
• Date: Fri, 30 Aug 2024 15:57:35 GMT
• Title: Configuration Monitor Synthesis
• Authors: Maximilian A. Köhl, Clemens Dubslaff, Holger Hermanns,
• Abstract summary: We introduce configuration monitoring to determine an unknown configuration of a running system. We develop a modular and generic pipeline to synthesize automata-theoretic configuration monitors. We show that our approach also generalizes and unifies existing work on runtime monitoring and fault diagnosis.
• Score: 1.9977040198878984
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: The observable behavior of a system usually carries useful information about its internal state, properties, and potential future behaviors. In this paper, we introduce configuration monitoring to determine an unknown configuration of a running system based on observations of its behavior. We develop a modular and generic pipeline to synthesize automata-theoretic configuration monitors from a featured transition system model of the configurable system to be monitored. The pipeline further allows synthesis under partial observability and network-induced losses as well as predictive configuration monitors taking the potential future behavior of a system into account. Beyond the novel application of configuration monitoring, we show that our approach also generalizes and unifies existing work on runtime monitoring and fault diagnosis, which aim at detecting the satisfaction or violation of properties and the occurrence of faults, respectively. We empirically demonstrate the efficacy of our approach with a case study on configuration monitors synthesized from configurable systems community benchmarks.

Related papers

• Explanatory Model Monitoring to Understand the Effects of Feature Shifts on Performance [61.06245197347139]
  We propose a novel approach to explain the behavior of a black-box model under feature shifts. We refer to our method that combines concepts from Optimal Transport and Shapley Values as Explanatory Performance Estimation.
  arXiv  Detail & Related papers  (2024-08-24T18:28:19Z)
• Interactive System-wise Anomaly Detection [66.3766756452743]
  Anomaly detection plays a fundamental role in various applications. It is challenging for existing methods to handle the scenarios where the instances are systems whose characteristics are not readily observed as data. We develop an end-to-end approach which includes an encoder-decoder module that learns system embeddings.
  arXiv  Detail & Related papers  (2023-04-21T02:20:24Z)
• System Resilience through Health Monitoring and Reconfiguration [56.448036299746285]
  We demonstrate an end-to-end framework to improve the resilience of man-made systems to unforeseen events. The framework is based on a physics-based digital twin model and three modules tasked with real-time fault diagnosis, prognostics and reconfiguration.
  arXiv  Detail & Related papers  (2022-08-30T20:16:17Z)
• syslrn: Learning What to Monitor for Efficient Anomaly Detection [3.071931695335886]
  syslrn is a system that first builds an understanding of a target system offline, and then tailors the online monitoring instrumentation based on the learned identifiers of normal behavior. We show in a case study for the monitoring of failures that it can outperform state-of-the-art log-analysis systems with little overhead.
  arXiv  Detail & Related papers  (2022-03-29T08:10:06Z)
• Towards Partial Monitoring: It is Always too Soon to Give Up [0.0]
  This paper revises the notion of monitorability from a practical perspective. We show how non-monitorable properties can still be used to generate partial monitors, which can partially check the properties.
  arXiv  Detail & Related papers  (2021-10-25T01:55:05Z)
• Sparsity in Partially Controllable Linear Systems [56.142264865866636]
  We study partially controllable linear dynamical systems specified by an underlying sparsity pattern. Our results characterize those state variables which are irrelevant for optimal control.
  arXiv  Detail & Related papers  (2021-10-12T16:41:47Z)
• No Need to Know Physics: Resilience of Process-based Model-free Anomaly Detection for Industrial Control Systems [95.54151664013011]
  We present a novel framework to generate adversarial spoofing signals that violate physical properties of the system. We analyze four anomaly detectors published at top security conferences.
  arXiv  Detail & Related papers  (2020-12-07T11:02:44Z)
• Monitoring and Diagnosability of Perception Systems [21.25149064251918]
  We propose a mathematical model for runtime monitoring and fault detection and identification in perception systems. We demonstrate our monitoring system, dubbed PerSyS, in realistic simulations using the LGSVL self-driving simulator and the Apollo Auto autonomy software stack.
  arXiv  Detail & Related papers  (2020-11-11T23:03:14Z)
• Safety Verification of Neural Network Controlled Systems [0.0]
  We propose a system-level approach for verifying the safety of neural network controlled systems. We assume a generic model for the controller that can capture both simple and complex behaviours. We perform a reachability analysis that soundly approximates the reachable states of the overall system.
  arXiv  Detail & Related papers  (2020-11-10T15:26:38Z)
• Lipschitz Recurrent Neural Networks [100.72827570987992]
  We show that our Lipschitz recurrent unit is more robust with respect to input and parameter perturbations as compared to other continuous-time RNNs. Our experiments demonstrate that the Lipschitz RNN can outperform existing recurrent units on a range of benchmark tasks.
  arXiv  Detail & Related papers  (2020-06-22T08:44:52Z)
• Monitoring and Diagnosability of Perception Systems [21.25149064251918]
  Perception is a critical component of high-integrity applications of robotics and autonomous systems, such as self-driving cars. Despite the paramount importance of perception systems, there is no formal approach for system-level monitoring. We propose a mathematical model for runtime monitoring and fault detection of perception systems.
  arXiv  Detail & Related papers  (2020-05-24T18:09:46Z)

This list is automatically generated from the titles and abstracts of the papers in this site.

This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.