Deutschmann, Andreas

Koch, Tobias

Möller, Dietmar P.F.

Institute of Transport Research:Publications

Two-layered Cyber-Physical System SimulationTobias Koch1, Dietmar P. F. Möller2, and Andreas Deutschmann11 German Aerospace Center, Lilienthalplatz 7, 38108 Braunschweig2 TU Clausthal, Institute of Applied Stochastics and Operations Research,Erzstraße 1, 38678 Clausthal-Zellerfeld, GermanyAbstract. With the increasing exposure of critical infrastructures to ex-ternal factors, critical infrastructure protection gained attention withinsecurity research. To develop protection methods the effect of externalfactors on critical infrastructures has to be known. In this paper wepresent an event-based, dynamic modeling approach using power flowanalysis and network flow analysis to simulate the effect on the critical in-frastructure with object-based programming. The highly interconnectedcyber-physical system is defined as a two-layered system with separatedcyber and physical properties. Network stability and cyber infection sta-tus is checked with two control feedback loops to maintain grid stabilityand to observe infection status. Therefore with given object load pow-ers and network interconnections the working capability of infrastructureparts can be predicted for various scenarios.1 IntroductionDue to an increasing number of cyber-attacks and damage related to them [1],cyber-security gained a lot of attention not only in the media, but also fromgovernmental side. The main interest from governmental side is critical infras-tructure protection (CIP) [2,3]. Nowadays critical infrastructures (CIs) have tobe considered as cyber-physical-systems (CPS) i. e. their operability dependson information and communication technology (ICT) and physical propertiessuch as power supplies. Control systems build the interface between the physicaland cyber level and are therefore an excellent point to attack a CPS. Cyber at-tacks on these control systems occurred in many different types of CIs e.g. waterservices [4], nuclear power plants [5], or electricity distribution stations [6]. Inci-dents are not always caused by human attacks. Especially the CI power supplyis vulnerable to environmental hazard (falling trees, geomagnetic storms etc.)or even human failures [7]. To improve the resilience and preparedness of CIswe have to understand the impact of external factors on the system. Modelingand simulation approaches are powerful tools to gain knowledge about complexsystems and help to identify vulnerable points within the system. This is oneof the most important parts within risk management and a necessary step todevelop emergency plans in case of successful cyber attacks. Therefore, a timedependent operability check for network components is needed from the physicalas well as the ICT side.2 Cyber-Physical System SimulationWe use a two-level network system for our description of the cyber-physicalsystem. The first one describes physical and the second one ICT properties.In addition lists with all directly connected objects are assigned to each singleobject for both levels. Due to power line communication physically connectedobjects are also considered to be connected on ICT level. To check operabilityat each timesteps we use two control feedback loops. The first one checks wethervoltage and frequency are within a tolerance interval that enables operability andthe second one is the status of software infection of the object. The input forthis feedback loops is calculated via a dynamical load flow analysis and networkinfection flow.2.1 Load Flow DynamicsThe principle of static load flow analysis is a commonly used method to deter-mine the voltages and phases within power distribution systems at given loadpowers [8] via solving a system of non-linear equations with iterative methods.As load power conditions at object i vary over time we introduce time dependentapparent power Si(tj) as a dynamic boundary condition. Repeating the load flowanalysis at several time steps leads to a time-series for voltage magnitude Ui(tj)and phase φi(ti). The frequency can be obtained by the change of phase withtime (1). The timestep ∆t to resolve fmax is limited by the Nyquist-Shannonsampling theorem via ∆tmax = 12f max .f(t) =12pidφdt≈ 12pi∆φ∆t=12piφn+1 − φntn+1 − tn (1)Under the constraints that the voltage magnitudes Ui(tj) and frequency fi(tj)determined for object i at time tj is within an a priori defined tolerance inter-val and that the source is able to deliver the demanded power, the operabilityof object i on physical side is guaranteed. If the grid is not stable, load shed-ding schemes are used to balance demanded and supplied power.. In additionblackouts can be simulated by directly setting object working status to false.2.2 Network Infection FlowWe represent the ICT network level as an undirected, weighted graph. Thisweights are introduced to combine all factors affecting propagation time like theoperating system, computer architecture, data transfer rate, type of connection,used protocols and encryptions or security precautions into one macroscopicmeasure (in seconds) being a weighting parameter wij for the edge betweenobjects i and j. If objects i and j are not connected wij is set to infinity. Fromthere on Dijkstra algorithm is applied to determine the shortest path from theintrusion point of the network, which is defined within the cyber attack event.This pathlength sets the time ti,inf for an infection of object i. The second controlloop checks whether the for the object indispensable applications are affected ornot. This is done via a simple comparison of listed properties of the object andthe defined event. If the software is aﬄicted, the objects functionability is set tofalse.3 ConclusionsA new simulation approach for CPS was presented that is based on a two-layered network approach, dividing physical from software properties. The staticload flow analysis, know from electrical engineering, was enhanced to a time-dependent network stability control method driven by dynamic boundary con-ditions. Occuring events may trigger (partial-)blackouts or the intrusion of ma-licious software. The propagation time of the infection within the network iscalculated via solving the shortest graph problem in an undirected graph withweighted edges.Though, this approach needs a lot of information about the network struc-ture, the consideration of expert knowledge is possible. Moreover, even in moremodern CIs the data needed for data-driven machine learning approaches [9] isnot available. As the simulation approach can be adapted to other CIs, the appli-cation within the field of CIP might be very diverse. However, a lot of work hasto be done, while connecting the operability of single objects or object groupswith CI specific processes.References1. “Managing cyber risks in an interconnected world - Key findings from The GlobalState of Information Security Survey 2015,” PricewaterhouseCoopers International,Tech. Rep., 2014.2. “Critical Infrastructure Protection - Challenges and Efforts to Secure Control Sys-tems,” U.S. Government Accountability Office, Tech. Rep., 2004.3. “On a new approach to the European Programme for Critical Infrastructure Pro-tection Making European Critical Infrastructures more secure,” 2013.4. M. Abrams and J. Weiss, “Malicious Control System Cyber Security Attack CaseStudy - Maroochy Water Services, Australia,” The MITRE Corporation AppliedControl Solutions, Tech. Rep., 2008.5. B. Kesler, “The Vulnerability of Nuclear Facilities to Cyber Attack,” Strategic In-sights, Tech. Rep., 2011.6. “Analysis of the Cyber Attack on the Ukrainian Power Grid,” Electricity InformationSharing and Analysis Center, Tech. Rep., 2016.7. “Report by the Federal Network Agency for Electricity, Gas, Telecommunications,Post and Railways on the disturbance in the German and European power systemon the 4th of November 2006,” Bundesnetzagentur, Tech. Rep., 2007.8. J. J. Grainger and W. D. Stevenson Jr., Power System Analysis, 1st ed., ser. Elec-trical and Computer Engineering. McGraw-Hill Education, 1994.9. O. Niggemann, G. Biswas, K. J. S., H. Khorasgani, S. Volgmann, and A. Bunte,“Data-Driven Monitoring of Cyber-Physical Systems Leveraging on Big Data andthe Internet-of-Things for Diagnosis and Control,” in International Workshop onPrinciples of Diagnosis, 2016.

Two-layered Cyber-Physical System Simulation

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