Design Methodologies for Securing Cyber-PhysicalSystems

Mohammad Al FaruqueDept. of Electrical Engineering

& Computer ScienceUniversity of California

Irvine, CA, USAalfaruqu@uci.edu

Francesco RegazzoniALaRI Institute

University of LuganoLugano, Switzerland

regazzoni@alari.ch

Miroslav PajicDept. of Electrical andComputer Engineering

Duke UniversityDurham, NC, USA

miroslav.pajic@duke.edu

ABSTRACTCyber-Physical Systems (CPS) are in most cases safety- andmission-critical. Standard design techniques used for secur-ing embedded systems are not suitable for CPS due to therestricted computation and communication budget availablein the latter. In addition, the sensitivity of sensed data andthe presence of actuation components further increase thesecurity requirements of CPS. To address these issues, itis necessary to provide new design methods in which secu-rity is considered from the beginning of the whole designflow and addressed in a holistic way. In this paper, we fo-cus on the design of secure CPS as part of the completeCPS design process, and provide insights into new require-ments on platform-aware design of control components, de-sign methodologies and architectures posed by CPS design.We start by discussing methods for the multi-disciplinarymodeling, simulation, tools, and software synthesis chal-lenges for CPS. We also present a framework for design ofsecure control systems for CPS, while taking into accountproperties of the underlying computation and communica-tion platforms. Finally, we describe the security challengesin the computing hardware that is used in CPS.

1. INTRODUCTIONCyber-Physical Systems (CPS) feature tight integration of

computational nodes, communication networks, and physi-cal environment that might include human users. CPS haveto fulfill a number of strict requirements in terms of powerand energy consumption, while providing real-time interac-tion with (i.e., control of) the physical world using reducedcommunication and computation resources. Nevertheless,the sensitivity of sensed data and the presence of actua-tion components further increase the security requirementsof CPS. Standard design techniques used for securing em-bedded systems are not suitable for CPS, due to the con-strained computation and communication budget availablein the latter. To address these issues, we require a new

design approach in which security is considered from the be-ginning of the whole design flow and addressed in a holisticway.

Recently, there have been high-profile attacks against CPS,exploiting the tight integration between physical, compu-tational, and networking aspects of CPS, and illustratingvulnerabilities of these systems. In [24, 12], several simplemethods to disrupt the operation of a vehicle were presented.Additional incidents that have raised the attention aboutsecurity problems in CPS, include Maroochy Water inci-dent [49] and Stuxnet virus attack on a SCADA system usedin industrial processes control [13, 25, 14]. Furthermore, re-cent studies have found that a large number of widely-usedsoftware based medical devices have been compromised [52].For example, in a VA hospital a virus infected 104 medi-cal devices such as X-rays, causing interruption of patientcare [61]. Also, methods to perform attacks on a widely usedglucose monitoring and insulin delivery system [26] and at-tack vectors on a networked PCA pump in a system of in-teroperable medical devices [53] have been reported.

In this paper, we address the issues related to the designof secure CPS. Existing methods for securing embedded sys-tems have proven to not be completely effective in this do-main. For instance, recent attacks demonstrated that con-sidering security as an afterthought has not been the bestway to address physical attacks, such as sensor spoofing;recent examples include GPS spoofing attacks to misguidea yacht off route [2], while [59, 16, 55] present steps andequipment required for GPS spoofing.

It is thus of crucial importance that designers of futureCPS are aware of the most important security challengeswhich needs to be addressed during the design. In addition,they have to have the proper basic blocks and tools to solvethem in a correct and reliable way. We describe the mainchallenges and opportunities related to security of CPS andprovide an updated overview of design tools, methodologies,and basic blocks currently used to address them. Note thatwe present the design of secure CPS as part of the com-plete design process for CPS, in order to provide a betterinsight into the new requirements on platform-aware designof control components, design methodologies and architec-tures posed by CPS design.

Specifically, we start by discussing challenges in the multi-disciplinary modeling, simulation, tools, and software syn-thesis for secure CPS (Section 2). We then present a de-sign framework for secure control of CPS, which takes intoaccount properties of the underlying computation and com-

munication platforms. Finally, we describe the security chal-lenges in the computing hardware used in CPS.

2. FUNCTIONAL LEVEL DESIGN FOR SE-CURITY

The increasing deployment of software and communica-tion is making CPS more vulnerable to cyber attacks [21,42]. However, there lacks the design automation supportfor the CPS security. For this reason, researchers are cur-rently trying to solve the CPS security challenge at the sys-tem level. In [60], the authors propose a Model-Based De-sign (MBD) method to assess the security of CPS with fourarchitecture-level attack models. Authors in [34] have dis-cussed a MBD technique to quantify the security metricsat the early design stage. Some researchers have proposedand used graph-based modeling methods to solve many se-curity problems. Authors in [62] have proposed a system-atic method for analyzing cyber-attacks on CPS using anextended Data Flow Diagram (xDFD) approach. Lastly,in [31] the authors have offered an attack tree-based ap-proach for system level security design. Unfortunately, themajority of these existing approaches to CPS security arelimited to modeling the software used in security analysis.For this reason, the group at UCI has proposed a design au-tomation model and tool to formulate and solve the securityproblem(s) before the system is built.

The work proposed by the UCI group exploits the observa-tion that identifying and fixing problems at the early stagesis economically beneficial. They proposes to formulate thesecurity problem before the system is built. They model cy-bersecurity attacks and countermeasure functionalities usinga novel security-aware functional modeling language imple-mented in the commercial design and simulation tools. Andthey create a design automation tool that uses simulation tovalidate cybersecurity vulnerabilities at the system-level.

Existing functional models include two types of functions:physical and cyber [9, 10, 11, 57]. Functions interact witheach other through energy, material, and signal flows. Theseflows carry real physical and cyber properties such as me-chanical, electrical, thermal energy, and data. Thus, exist-ing functional models naturally leak information that can beused to attack the system via the signal flows in the cyber do-main or energy/material flows in the physical domain. TheUCI group extends the functional modeling concept with cy-bersecurity functions. Their proposed security-aware func-tional models provide the means to both analyze the effectof cybersecurity attack functions on the system, and refinethe design using cybersecurity countermeasure functions.

An example of a security aware functional model for a caris shown in Figure 1. In this example, both cyber and physi-cal attacks are modeled. The blue arrows in the flows repre-sent the cyber attack vectors, while the red arrows representthe physical attack vectors. The purpose of the analysis is toquantify the effects of these attacks to the Export TME func-tion that maps to the velocity of the car. In other words,we want to determine if the velocity controls are vulnerableto any cyber physical attacks.

In order to evaluate the security level of the Export TMEfunction, we simulate the model and analyze the impactof the attack at the system-level. This step is importantto identify functions that are vulnerable to attacks. Theselow-security functions can then be protected by refining the

functional model and adding cybersecurity countermeasurefunctions. This iterative approach facilitates the analysisof different scenarios. An important benefit of the iterativeapproach is that more complex scenarios can be modeled.

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Figure 1: Security aware functional level model ofautomotive [58].

From the analysis of this security-aware functional model,a system-level simulation model can be directly generatedusing the synthesis technology provided in the existing works [9,10, 11, 57].

To validate the presented methodology, we implemented adesign automation tool using commercial off-the-shelf soft-ware as shown in Figure 2. Taking advantage of its sub-system libraries, we utilized Amesim to model the systemfunctions (multi-physics), cybersecurity functions, and sce-narios (e.g., environmental conditions). In addition, we usedMatlab/Simulink for its mathematical capabilities to modelthe cybersecurity attacks. Six types of attack models areintegrated in our current design automation tool.

Figure 2: The design automation tool developed bythe UCI group [58].

To demonstrate the proposed design automation tool andthe attack models during the early design stage, we usedthe design of an Electric Vehicle (EV) as a case study . We

instantiate an attack model interface using an attack modelfrom the library and insert it between the motor and themotor-controlling ECU. Figure 3, we see that the fuzzy at-tack successfully destabilizes the motor behavior by addingnoise to the control signals.

Fuzzy attack on the control signal

Attacked motor behavior

Figure 3: Simulation results with the proposed at-tack models.

In this work, the UCI group developed a functional modelto analyze security challenges during the early design stage.In the future, the UCI group will explore the capability ofthe functional model to not only help the security analysis,but also to automatically generate/synthesize system-levelsimulation models in response to the analysis. Moreover, fordemonstration purposes, they included six different attackmodels in their library but in the future, they may developadditional attack models that can capture both cyber andphysical domain attacks. Lastly, the group plans on devel-oping security cost metrics to integrate with the proposedsecurity-aware functional models.

3. PLATFORM-AWARE CONTROL DESIGNFOR SECURE CPS

There is a need to change the way we reason about con-trol in CPS, and to start designing attack-resilient controlschemes and architectures capable of dealing with cyber-physical attacks on the environment of the controller (e.g.,sensors, actuators, and communication media). Recent at-tacks on control component of CPS have clearly revealedthat relying exclusively on cyber-security techniques for se-curing CPS is insufficient. Consequently, they have spannedresearch into control-level techniques that address the prob-lem of state estimation and intrusion detection under attackson the environment of the controller, i.e., attacks on sensors,actuators and communication networks (e.g., [50, 54, 39, 15,51, 35, 18, 17, 30, 29]).

In this section, we present recent efforts by the Duke re-search group, to exploit the knowledge of the system dynam-ics for attack-resilient control of CPS. The goal of our workhas been to develop tools and techniques to ensure that CPSmaintain a degree of control even when the system is undercyber and/or physical attack.

We propose adding security-awareness to the control sys-tem design that allows control systems to recover the infor-

Figure 4: The proposed design framework for securecontrol of cyber-physical systems.

mation about the state of the controlled process despite theattacks. Our approach to building attack-resilient controlsystems is to combine secure-detection and attack-identificationwith added logical redundancy in system design (see Fig-ure 4). Here, we assume that control design for no-attackcase has been developed and concentrate on techniques forstate estimation and sensor fusion under external attacks.Note that since previously developed methods for attack-resilient state estimation (e.g., [15, 39, 48]) require the un-practical assumption of the exact knowledge of the con-trolled plant’s dynamics, resilience-to-attack guarantees donot hold when these assumptis are violated.

From the perspective of controlling CPS, the main ideahas been to exploit knowledge of the system’s dynamics forstate estimation and attack detection and identification inthe presence of sensor and actuator attacks and attacks oncontrol resources. For instance, in [37], we introduced amethod for attack-resilient state estimation for systems withmodeling errors and illustrated its use on a real-world casestudy – design of attack-resilient cruise control on an un-manned ground robot (Figure 5(a)). To obtain the state ofa controlled physical process when the attacker compromisessystem sensors and actuators, we introduced an Integer Pro-gramming (IP) based procedure that utilizes a window ofprevious sensor measurement vectors and (limited) knowl-edge of the system’s dynamics.

Furthermore, we showed how to capture effects of the uti-lized computation and communication platforms on the ac-curacy of the dynamical model and described how implemen-tation issues including jitter, latency, and synchronizationerrors can be captured by the model. This has allowed forthe mapping of attack-resilient control objectives into real-time performance requirements from the utilized platform,which facilitates reasoning about attack-resilience across dif-ferent implementation layers as illustrated in Figure 5(b).

As shown in [24, 12], the lack of understanding betweencontrol design assumptions and system implementation canbe heavily exploited to compromise system’s functionality.For example, by changing scheduling sequence for controland sensing actions/computations, we could dramatically

Figure 5: (a) The LandShark ground robot running attack-resilient cruise control, (b) Zoomed on Z3 executiontimes for verification of TF invariants – note that this approach does not scale well because controllers withsize greater than two can not be verified with this type of invariants.

affect the stability and safety of the controlled process [33].Consequently, to facilitate design of secure control for CPSwe have developed a framework for cross-layer analysis ofplatform effects on security properties of employed controlalgorithms. For illustration, in time-triggered architectures [23]we can rigorously quantify the modeling and performancegap between the model-level semantics of linear dynamiccontrollers and their implementation-level semantics [33].Thus, we could analyze the effect and provide performanceguarantees when a malicious adversary imposes the worstcomputing sequence from control’s perspective. In [17, 19]we reported preliminary work on this topic, focused on im-pacts of communication schedule on attack-resilient sensorfusion when the system model is not known. In addition, weproposed methods for attack-detection and identification formore complex system models. For example, motivated bysensor fault models in some CPS applications (e.g., GPS) weconsidered attack-resilient sensor fusion that exploits knowl-edge of temporal sensor fault-models without conservativelytreating them as compromised [38].

Finally, in the context of CPS, resource constraints mightimpose an insurmountable obstacle for the use of developedcontrol techniques. Hence, it is necessary to provide non-optimal methods for attack-resilient control with formal re-silience guarantees. For example, in [36] we show how toexploit techniques from compressed sensing to investigateconditions that will enable the use of convex estimators forattack-identification while providing formal resiliency guar-antees. Note that since extracting accurate-enough dynam-ical models for some CPS (e.g., patient modeling in MedicalCPS) is quite challenging (if at all possible), there are limita-tions to the use of model-driven methods for attack-resilientcontrol. To overcome this weakness, an avenue of our fu-ture work is on data-driven methods for attack-detection andidentification when some of the sensors are potentially cor-rupted.

4. SECURING CPS FROM THE HARDWAREPOINT OF VIEW

The main difference between Cyber-physical and othersystems is probably the interaction that CPS have withthe physical world. CPS, in fact, integrate sensing, com-

putations and actuation capabilities, and they are used tointeract and to control critical infrastructure or critical ap-plications. Applications range from automotive to indus-trial control systems or medical devices, and for many ofthem, safety is of utmost importance, as a failure of the sys-tem could have catastrophic consequences. Often, CPSs aredeployed in an harsh environment, thus requiring reliabil-ity and tolerance to faults, and they are characterized byvery strict constraints in terms of battery and computationpower.

Low power, reliability and safety however are not the onlyproperties which designers have to provide to CPSs. Theuse of CPSs in critical applications makes them an attrac-tive target for cyber-attacks. The most famous example ofattack to cyber-physical system is probably STUXNET [13],but several other works demonstrate the severity of the prob-lem also for automotive industry [47] and smart grids [63].In this section we discuss Lightweight cryptography [41] isa branch of cryptography aiming at implementing cypto-graphic algorithms using an extremely limited amount ofresources. This goal is achieved following two main ap-proaches [3]. The first approach consists in minimizing theamount of resources needed for implementing standard algo-rithms (AES for instance can be implemented using approx-imately 2500 Gate Equivalent [32]). The second approach isto design novel algorithms considering since the beginningthat they have to be implemented using a limited amount ofresources. The most successful examples of the second ap-proach are PRESENT [8] and CLEFIA [46], which recentlybecome ISO standard for lightweight cryptography. Furthersuitable algorithms can be identified also among the candi-dates recently submitted to the CAESAR contest [1] whichhas the goal of selecting a portfolio of algorithms for provid-ing authenticate encryption. The designer has to select theappropriate hardware block according to the needs of the ap-plication, considering also that, in particular for control sys-tems, CPSs might have extremely strict legacy requirements.Furthermore, designers should be aware that in lightweightcryptography usually trade resources with performance. AsCPSs, due to their nature of being a controlling systems,often have strict real time requirements, it should be guar-anteed that the included security primitives are capable ofmeeting them.

CPSs, as several other embedded systems can be deployedin an hostile environment, thus they are potentially in thehand of the attacker. For this reason, designer of securehardware for cyber-physical systems have to implement itin such a way that is robust against physical attacks. Whencarrying out physical attacks, the adversary, instead of at-tacking the mathematical structure of a cryptographic prim-itive, tries to exploit weaknesses of its implementation foraccessing secret information. Physical attacks are usuallydivided in active or passive [27]. During active attacks, theadversary tampers with the device in order to modify itsbehavior. Example of these attacks are fault attacks [4],in which an adversary induce a fault into a device, for in-stance by underfeeding the power supply [5], and extractthe secret key by exploiting the differences between correctand faulty output. In passive attacks, usually called sidechannel attacks, the adversary extracts secret informationby analyzing a physical observable and exploiting its corre-lation with the secret which is computed. The most commonside channel attack is power analysis [20], which extract thesecret key by relating it to the power consumed during en-cryption. However, also other channels such as timing [22]or electromagnetic emission [45] were successfully used inthe past.

Resistance against timing attacks can be achieved by guar-anteeing that all the operations depending on secret data arecarried out in the same amount of time. This approach issuccessfully used in servers and in embedded systems, thuscan be used also by CPSs designers. Resistance againstpower analysis attacks can be obtained by breaking the linkbetween the data computed and the actual data (this ap-proach is called masking [28]) or by breaking the correlationbetween the data being processed and the power consump-tion (this approach is called hiding [56]). Hardware blocksresistant against power analysis attacks are usually designedand tested by well trained engineers which manually applya countermeasure or a number of them to a cryptographicblock []. However, there have been several attempts to auto-matically realize hardware resistant against power analysisattacks [56, 43, 44, 40]. Similar approaches, can be used bythe cyber-physical systems designers for securing their de-vices against physical attacks. It is important to underlinehowever that design of secure hardware is still an open prob-lem. These countermeasures are only protecting only thecyber part of the device, not the physical one. A hardwaredesign flow which considers security also for the physicalpart of the CPS, to date, still does not exist.

The overall security of a CPS depends from the securityof its building blocks. CPS as other systems, are subjectto hardware Trojans, which, potentially, is one of the mostserious threat for hardware security. Hardware Trojans canbe defined as a deliberate and malicious modification of ahardware component carried out with the goal of alteringits correct behavior. Possible example of alteration can bethe leak of secret data or a denial of service. Hardware Tro-jans received a lot of attention from the community due tothe potential devastating effects which they can have on se-curity [6]. Trojans can be inserted at several points of thedesign flow: by malicious designer or IP provider, by a ma-licious foundry, or by malicious tool. Several technique todetect hardware Trojans were proposed in the past, rang-ing from testing, analysis of side channel, or optical inspec-tion [7], nevertheless, none of them is perfect, and several

of them require a gold model to be effective. Hardware de-signer of CPS has to be aware of this threat and apply theappropriate detection techniques to identify hardware Tro-jans or to adopt the appropriate approach at system level totolerate the presence of Trojans.

Finally, as mentioned, in addition to security requirements,cyber-physical systems needs to provide safety and reliabil-ity, whose needs might be in contrast with the ones of se-curity. For instance, it is important to consider the effectswhich redundancy, added to provide fault tolerance, mighthave on security. Designers of secure cyber-physical systemsshould always keep a global vision of all the requirementsand evaluate the effects which each design choice has on theothers. This is a difficult and error prone task, which wouldbe dramatically simplified by the eventual coming of dedi-cated design tools.

5. CONCLUSIONIn this paper, we have focused on the design challenges for

securing Cyber-Physical Systems. Specifically, we have pre-sented an overview of tools, design methods, and buildingblocks used to design secure CPS. We have considered threecomplementary approaches for ensuring security in CPS.First, we have described methods for multi-domain model-ing, simulation, and software synthesis for secure CPS. Sec-ond, we have presented a control-aware design framework toensure attack-resiliency in CPS. Third, we have addressedthe security challenges related to the design and use of com-puting hardware in CPS. Finally, potential avenues for fu-ture work have been discussed.

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