Biblio

The security of cyber-physical systems is of paramount importance because of their pervasiveness in the critical infrastructure. Protecting cyber-physical systems greatly depends on a deep understanding of the possible attacks and their properties. The prerequisite for quantitative and qualitative analyses of attacks is a knowledge base containing attack descriptions. The structure of the attack descriptions is the indispensable foundation of the knowledge base.

This paper introduces the Cyber-Physical Attack Description Language (CP-ADL), which lays a cornerstone for the structured description of attacks on cyber-physical systems. The core of the language is a taxonomy of attacks on cyber-physical systems. The taxonomy specifies the semantically distinct aspects of attacks on cyber-physical systems that should be described. CP-ADL extends the taxonomy with the means to describe relationships between semantically distinct aspects, despite the complex relationships that exist for attacks on cyber-physical systems. The language is capable of expressing relationships between attack descriptions, including the links between attack steps and the folding of attack details.

The exponential growth of information and communication technologies have caused a profound shift in the way humans engineer systems leading to the emergence of closed-loop systems involving strong integration and coordination of physical and cyber components, often referred to as cyber-physical systems (CPSs). Because of these disruptive changes, physical systems can now be attacked through cyberspace and cyberspace can be attacked through physical means. The paper considers security and resilience as system properties emerging from the intersection of system dynamics and the computing architecture. A modeling and simulation integration platform for experimentation and evaluation of resilient CPSs is presented using smart transportation systems as the application domain. Evaluation of resilience is based on attacker-defender games using simulations of sufficient fidelity. The platform integrates 1) realistic models of cyber and physical components and their interactions; 2) cyber attack models that focus on the impact of attacks to CPS behavior and operation; and 3) operational scenarios that can be used for evaluation of cybersecurity risks. Three case studies are presented to demonstrate the advantages of the platform: 1) vulnerability analysis of transportation networks to traffic signal tampering; 2) resilient sensor selection for forecasting traffic flow; and 3) resilient traffic signal control in the presence of denial-of-service attacks.

Distributed consensus protocols are an important class of distributed algorithms. Recently, an Adversarial Resilient Consensus Protocol (ARC-P) has been proposed which is capable to achieve consensus despite false information pro- vided by a limited number of malicious nodes. In order to withstand false information, this algorithm requires a mesh- like topology, so that multiple alternative information flow paths exist. However, these assumptions are not always valid. For instance, in Smart Grid, an emerging distributed CPS, the node connectivity is expected to resemble the scale free network topology. Especially closer to the end customer, in home and building area networks, the connectivity graph resembles a tree structure.

In this paper, we propose a Range-based Adversary Re- silient Consensus Protocol (R.ARC-P). Three aspects dis- tinguish R.ARC-P from its predecessor: This protocol op- erates on the tree topology, it distinguishes between trust- worthiness of nodes in the immediate neighborhood, and it uses a valid value range in order to reduce the number of nodes considered as outliers. R.ARC-P is capable of reach- ing global consensus among all genuine nodes in the tree if assumptions about maximal number of malicious nodes in the neighborhood hold. In the case that this assumption is wrong, it is still possible to reach Strong Partial Consensus, i.e., consensus between leafs of at least two different parents.