(English) Tracing Cascading Data Corruption in CPS with the Information Flow Monitor

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Stefan Gries, Marc Hesenius, Volker Gruhn: Tracing Cascading Data Corruption in CPS with the Information Flow Monitor. In: New Trends in Intelligent Software Methodologies, Tools and Techniques - Proceedings of the 16th International Conference, SoMeT_17, Kitakyushu City, Japan, September 26-28, 2017, S. 399–408, 2017.

Abstract

Cyber-physical systems are context-aware networked systems containing sensors, aggregators and actuators. Raw sensor data and aggregated information are spread among the network and processed in multiple nodes to result in an action, which is possibly executed at a different physical location in the network. Due to the flexible topology and the emergent features of CPS, decisions within the network that trigger actions are not always trivial to understand. These decisions are based on raw sensor data which are not clearly visible at the location of their execution. This can be problematic if decisions have to be justifiable. It becomes even more difficult if decisions has been flawed, and it is not ascertainable why they were made. In order to determine the reasons for incorrect decisions in the network, the dependencies and input values of a decision must be known. Without this information, debugging is quite difficult. In this paper, we present the Information Flow Monitor, which can capture and visualize dependencies between nodes and information in CPS.

BibTeX (Download)

@inproceedings{DBLP:conf/somet/0001HG17,
title = {Tracing Cascading Data Corruption in CPS with the Information Flow  Monitor},
author = {Stefan Gries and Marc Hesenius and Volker Gruhn},
url = {https://doi.org/10.3233/978-1-61499-800-6-399},
doi = {10.3233/978-1-61499-800-6-399},
year  = {2017},
date = {2017-09-11},
booktitle = {New Trends in Intelligent Software Methodologies, Tools and Techniques 
 - Proceedings of the 16th International Conference, SoMeT_17, Kitakyushu 
 City, Japan, September 26-28, 2017},
pages = {399--408},
crossref = {DBLP:conf/somet/2017},
abstract = {Cyber-physical systems are context-aware networked systems containing sensors, aggregators and actuators. Raw sensor data and aggregated information are spread among the network and processed in multiple nodes to result in an action, which is possibly executed at a different physical location in the network. Due to the flexible topology and the emergent features of CPS, decisions within the network that trigger actions are not always trivial to understand. These decisions are based on raw sensor data which are not clearly visible at the location of their execution. This can be problematic if decisions have to be justifiable. It becomes even more difficult if decisions has been flawed, and it is not ascertainable why they were made. In order to determine the reasons for incorrect decisions in the network, the dependencies and input values of a decision must be known. Without this information, debugging is quite difficult. In this paper, we present the Information Flow Monitor, which can capture and visualize dependencies between nodes and information in CPS.},
keywords = {cascading data corruption, CPS, Cyber-Phyiscal System, Cyber-Physical Systems, Data Tracking, flow monitoring, IFM, information dependencies, Information Flow Monitor, Internet of Things},
pubstate = {published},
tppubtype = {inproceedings}
}