Solving the Grid Defender’s Dilemma: Tamper Protection for

Distributed Cyber-Physical Systems

Jason Reeves and Sean W. Smith

Department of Computer Science, Dartmouth College, Hanover, NH, U.S.A.

Keywords:

Tamper Detection, Critical Infrastructure, Cyber-Physical Systems.

Abstract:

Embedded devices installed as part of the smart grid rollout present a major dilemma for grid defenders,

because they are soft targets that could allow an attacker to access critical assets (generators, control centers,

etc.) deeper in the utility’s network. While both physical tampering and intrusion protection are large, well-

studied ﬁelds, state-of-the-art protection schemes suffer from several ﬂaws: They are not powerful enough

to respond properly to different tamper events, their severe responses can lead to reduced grid availability,

and they often require more setup resources than a utility operator can provide. To protect these networks,

we present TEDDI (Tamper Event Detection on Distributed Infrastructure), a distributed, sensor-based tamper

protection architecture for embedded devices on utility networks. TEDDI uses data gathered from across

the network to make more-informed and more-accurate tamper decisions, and can customize its response

based on the event it sees. It can also be conﬁgured and installed quickly, without needing a large base of

knowledge beforehand. In this paper, we lay out the TEDDI architecture, and discuss how TEDDI solves the

grid defender’s dilemma better than current work.

1 INTRODUCTION

As part of the push towards a smarter electric grid,

utilities have installed a number of edge devices

(resource-constrained embedded devices that live on

the periphery of a network, e.g. at a consumer’s home

or on a telephone pole) on their SCADA

networks.

These devices present a major security challenge for

the power grid, since they are relatively easy to ac-

cess, they have little physical security, and they have

direct access to a utility’s SCADA network. There-

fore, edge devices provide a potential gateway to any

of the devices on a utility’s broader communication

and control network, which makes protecting edge

devices a high priority.

While a large corpus of work exists in the area of

tamper detection and physical security, the grid’s fo-

cus on availability and resilience presents a dilemma

for grid defenders. Choosing an incorrect or unnec-

Sections 1 through 4, as well as Section 7, are based on our pre-

vious paper from the International Cryptographic Module Confer-

ence (Reeves and Smith, 2014). ICMC, however, did not publish

its proceedings.

SCADA stands for “Supervisory Control and Data Acquisition,”

and is generally used to describe the command-and-control net-

works used by critical industries like the power grid.

essarily severe response to an event could unneces-

sarily reduce the grid’s availability, which is a worse

outcome than a malicious attack. Most current tam-

per protection solutions, however are unable to differ-

entiate between important tamper events and do not

have the power to tailor their responses accordingly,

which increases the chances of choosing the wrong

response.

To address this dilemma, we propose TEDDI

(Tamper Event Detection on Distributed Infrastruc-

ture), a distributed, sensor-based system that can use

information from the larger network to help inform

local tamper decisions. TEDDI collects information

about the physical environment of edge devices, and

uses this data to determine what events, whether ma-

licious or benign, might be affecting these devices.

Once our system makes this decision, we determine

and execute the optimal sequence of responses for

each affected device.

Our contributions are as follows:

• We have built a novel system that addresses the

security concerns surrounding edge devices in a

SCADA network. Our system handles a wider

range of events than existing protection solutions,

and also allows for multiple responses.

309

Reeves J. and Smith S..

Solving the Grid Defender’s Dilemma: Tamper Protection for Distributed Cyber-Physical Systems.

DOI: 10.5220/0005549503090316

In Proceedings of the 12th International Conference on Security and Cryptography (SECRYPT-2015), pages 309-316

ISBN: 978-989-758-117-5

 2015 SCITEPRESS (Science and Technology Publications, Lda.)

• Our system features a novel way to make tamper

decisions, using factor graphs (Frey, 2003) to fuse

together sensor data and quickly determine what

event is occurring.

• Our system will be more useable for power

engineers who have performance goals and

operational-related events in mind, but may not

have a large training data set at hand or have time

to ﬁne-tune a complex protection system.

We structure the rest of this paper as follows: Sec-

tion 2 discusses important background information,

Section 3 introduces the relevant related work in this

space, Section 4 introduces TEDDI and explains its

individual components, Section 5 highlights TEDDI’s

advantages over prior work, Section 6 outlines the

next steps in our research, and Section 7 offers our

conclusions.

2 BACKGROUND

In this section, we discuss the dilemma faced by op-

erators trying to protect the grid, and the beneﬁts of

choosing factor graphs as our data fusion algorithm.

2.1 The Grid Defender’s Dilemma

In defending the power grid, operators face a concern-

ing scenario:

• Grid SCADA networks are open to attacks that

could have severe, long-lasting consequences: A

2014 Wall Street Journal article (Smith, 2014) de-

clared that malicious attackers could cause a na-

tionwide blackout by taking down fewer than ten

critical substations during a period of high de-

mand on the grid, and that such a blackout “could

plunge the country into darkness for weeks, if not

months” (Smith, 2014).

• As stated earlier, edge devices pose a large secu-

rity risk to utilities for three reasons: They are

relatively easy to access, they have little physical

security, and they have direct access to a utility’s

SCADA network. An attacker could potentially

use an edge device as a launching point for further

attacks on high-value targets deeper in the net-

work. Such attacks have been shown to be plau-

sible and effective: For example, a single mal-

formed packet “can crash an entire SCADA trans-

mission system” (Peterson, 2013).

• However, availability reigns supreme for SCADA

networks, so operators have to be careful about

properly identifying and responding to the events

affecting the grid. Choosing an incorrect or un-

necessarily severe response to an event could re-

duce the grid’s availability by leading to increased

technician visits, frequent device replacements,

and unnecessary service losses.

• While operators have a clear idea of their security

goals and the attacks they want to guard against,

their resources are limited: They may not have the

time nor the training data to conﬁgure a complex

protection system for their network.

To solve this dilemma, grid defenders need an

easy-to-use tool that requires minimal prior knowl-

edge about important events, but also has the power

and ﬂexibility to differentiate between these events

and choose an appropriate response for each one. This

is the gap we want to ﬁll with TEDDI.

2.2 Factor Graphs

In TEDDI, we use factor graphs (Frey, 2003) as our

fusion algorithm for our sensor data. Formally, a fac-

tor graph is a bipartite graph that connects a set of

nodes V that represents system variables with a set of

nodes F that represents functions relating these vari-

ables. If a function is dependent on a variable, an edge

is added to the graph between the nodes that represent

this variable and function.

Data fusion within tamper protection systems is

usually done using a standard technique such as

Bayesian networks (Pearl, 1988) or a custom al-

gorithm such as SCADAHawk’s snapshots (Sousan

et al., 2013). These techniques, however, have lim-

ited detection power: For example, Bayesian analyses

are unable to capture complex dependencies between

events due to their inherent independence assump-

tions, and adapting them to cover these dependen-

cies can make these models infeasibly complex (Cao

et al., 2015). In contrast, factor graphs allow for

“arbitrary factorizations of joint distributions” (Frey,

2003), giving them the power to perform any task

that a Bayesian network could while using a simpler

model that can capture the defender’s intuition about

suspicious activity (Cao et al., 2015). This added

power means factor graphs can give us improved

event detection and protection capabilities over cur-

rent SCADA protection systems, while also giving

utilities more ﬂexibility to balance security with their

larger availability goals.

3 RELATED WORK

In this section, we discuss some of the related work

in both hardware tamper protection and general intru-

SECRYPT2015-InternationalConferenceonSecurityandCryptography

310

sion protection.

3.1 Hardware Tamper Protection

The roots of hardware tamper protection run deep,

and include seminal works such as Tygar and Yee’s

Dyad platform (Tygar and Yee, 1994) and Smith,

Palmer, and Weingart’s work on the IBM 4758 (Smith

et al., 1998; Smith and Weingart, 1999). More recent

academic work has focused on placing conductive

patches randomly within potting material (Dragone,

2013) and obfuscating the system’s hardware (De-

sai, 2013). Other solutions include chip-level trusted

platform modules (for example, (Atmel Corporation,

2015)) and cryptographic coprocessors (IBM, 2011).

However, these solutions are geared towards single-

device protection, and do not have the response gran-

ularity needed for grid protection.

3.2 Intrusion Protection Systems

Many protection systems in this space use Bayesian

networks for data fusion. For example, Process Query

Systems (Roblee et al., 2005) use conditional prob-

abilities to relate events to one of its attack/failure

models, while the Bayesian trust assessment frame-

work (Wang and Hauser, 2011) tries to evaluate the

trust an operator can place in a device based on

the evidence they collect. The Probabilistic Event

Correlation system (Valdes and Skinner, 2001) uses

Bayesian-based data fusion as a way to reduce false

positives and determine which alerts should be shown

to the administrator.

Some systems forgo standard fusion methods in

favor of building a custom detection algorithm. Ex-

amples of this approach include SCADAHawk (Sou-

san et al., 2013), which organizes data into snap-

shots that it uses for anomaly detection, and Ami-

lyzer (Berthier and Sanders, 2013), which organizes

smart meter trafﬁc into ﬂows that are scrutinized for

policy violations or other unwanted behaviors.

SCPSE (Zonouz et al., 2012) combines intrusion

detection alerts with power system information to

more accurately estimate the security state of an elec-

trical network. The system builds an attack graph that

traces the possible paths an attacker could follow via

exploiting network nodes, and later uses the informa-

tion it gathers to estimate the attacker’s path and thus

reveal what nodes may be compromised. The Re-

sponse and Recovery Engine (Zonouzet al., 2014), on

the other hand, uses attack response trees (ARTs) to

deﬁne the security goals of the system and track how

far an attacker has progressed in violating these goals,

thus determining the potential responses to apply.

Finally, we should note that we are not the ﬁrst

to propose factor graphs as a security mechanism:

Cao et al. (Cao et al., 2015) demonstrated how such

graphs can be used to detect compromised user ac-

counts, even before the accounts had compromised

the actual system. To the best of our knowledge, how-

ever, TEDDI is the ﬁrst system to use factor graphs for

physical tamper protection.

4 TEDDI OVERVIEW

In this section, we offer a high-level example of how

TEDDI works, and cover each of its components in

detail.

4.1 Why Distributed?

We built TEDDI as a distributed system to better al-

low it to gather contextual information for its tamper

decisions. Our setup is inspired by XACML (Organi-

zation for the Advancement of Structured Information

Standards, 2013), which includes policy information,

decision, and enforcement points as distributed com-

ponents that work together to rule on policy decisions.

Since we also want to make and enforce policy deci-

sions on a distributed system, we adopt a similar ar-

chitecture.

TEDDI is made up of three components:

• Tamper Information Points (TIPs, Section 4.5):

Programs that collect sensor data and attempt to

make local tamper decisions.

• Tamper Decision Points (TDPs, Section 4.6): Pro-

grams that take the sensor data from TIPs, deter-

mine the overall state of the network they moni-

tor, and make tamper decisions when asked by the

TIP.

• Tamper Enforcement Points (TEPs, Section 4.7):

Programs that listen for tamper decisions and ex-

ecute responses based on those decisions.

4.2 Attacker Model

Before we begin, we ﬁrst deﬁne the capabilities of the

attacker we are considering.

We assume that the attacker must go through an

edge device to access the SCADA network, and can-

not access the network just by tapping the wire. More

speciﬁcally, we assume that the attacker must pene-

trate some sort of physical boundary, which could be

as simple as the device’s own exterior, to access the

edge device’s hardware. Network attacks originat-

ing from outside the SCADA network are considered

SolvingtheGridDefender'sDilemma:TamperProtectionforDistributedCyber-PhysicalSystems

311

TDP

TIP A

TIP B

TIP C

Attacker?

Earthquake?

To substation

To remote devices

TEP

Response?

Figure 1: A diagram of the example given in Section 4.3.

First, a utility operator builds the tamper system using the

generation tool (Step 1), and then deploys the various com-

ponents to their proper locations in the network (Step 2).

When a TIP senses shaking (Step 3), it sends an alert to the

TDP (Step 4), which then uses it full information base to

decide exactly what is happening (Step 5). This decision is

then sent to the appropriate TEPs (Step 6), who then decide

the proper response to the event (Step 7).

out of scope, given the availability of techniques such

as encryption (Solomakhin, 2010) to handle these at-

tacks.

While TEDDI’s sensors monitor the device’s

physical boundary,

there are no limits to the tools an

attacker can use or the time they can take to penetrate

this boundary.

4.3 How TEDDI Works: An Example

To clarify how the pieces of TEDDI ﬁt together, con-

sider the simple example illustrated in Figure 1:

1. A utility operator constructs a simple tamper sys-

tem consisting of a single TDP and three TIPs: A,

B, and C. The operator denotes that the TIPs are

equipped with an accelerometer as part of their

sensor set, and tells the system to watch out for

shaking as either part of an attack or an earth-

quake.

2. The TDP and TIPs programs are deployed, and

the TIPs begin monitoring their environment for

event indicators.

3. TIP A experiences intense shaking, causing its ac-

celerometer to exceed its threshold. A’s limited

factor graph (Section 4.4) knows that either an at-

tack or an earthquake is occurring, but it cannot

differentiate between the two possibilities without

knowing the state of other boxes in the system.

4. A sends an alert to the TDP about its situation, and

requests assistance on making a decision.

While we did not monitor any software alerts or device load in-

formation, many current intrusion detection systems already do

this, and we could easily adapt TEDDI to monitor both digital and

physical signals.

5. The TDP receives A’s alert and attempts to deter-

mine the overall state of the system. In doing so,

it ﬁnds that B and C have also experienced intense

shaking (and in turn, have also sent the TDP alerts

about potential tampering).

6. The widespread shaking causes the TDP’s factor

graph (Section 4.4) to decide that the shaking is

due to an earthquake. This decision is passed

along to the two TEPs associated with A.

7. A’s TEPs decide not to take any action for the time

being, since the utility wants to keep the system

running as long as possible.

4.4 TEDDI Factor Graphs

At the heart of our tamper decision engine is a fac-

tor graph (Frey, 2003) that looks for sequences within

its sensor data to determine what event is occurring

on the system. Our graph, as shown in Figure 2, is

constructed from two important datasets:

Events: The set E = {e

, . . . , e

} of events we want

to detect.

Indicators: The set I = {i

, . . . , i

} of phenomena

connected to the events in E. For example, if E

includes an earthquake in its set, I might include

shaking. The presence or absence of an indicator i

is calculated by looking at its corresponding sen-

sor s to see if the sensor reading has crossed an

operator-deﬁned threshold.

We build our factor graph via an efﬁcient three-

step process:

1. For each concerning event e

, the operator deﬁnes

the sequence of indicators that signals e

’s pres-

ence.

2. The operator then ranks the events by their impor-

tance, declaring which events are the most impor-

tant to detect.

3. Finally, the indicator sequences are arranged

within the factor graph in order of their impor-

tance.

Whenever we poll our sensors for data, we calcu-

late the presence of indicators based on this data, and

then run through our factor graph to see what events

are occurring, starting from the most-important event.

When we generate the ﬁnal factor graph, we cre-

ate two versions: A full version for tamper decision

points, and a limited version for tamper information

points. We create the two versions to account for dif-

ferences in the information bases between decision

and information points:

SECRYPT2015-InternationalConferenceonSecurityandCryptography

312

START

No Event

Event 1

Event 2

Event 3

Indicator 1?

Indicator 2?Indicator 3?

Indicator 1?

Indicator 4?

Indicator 1?

Indicator 5?

YYY

N N

Figure 2: An example factor graph generated by our TEDDI

system. The blank nodes represent intermediate steps in

each event sequence.

• If an information source is only available to tam-

per decision points, sensors and indicators associ-

ated with that information source are not included

in the limited factor graph.

• If a tamper information point is unable to distin-

guish between two events due to a lack of infor-

mation, those events are combined in the limited

factor graph.

4.5 Tamper Information Points (TIPs)

Tamper information points are the eyes and ears of the

system, and are responsible for collecting the sensor

data needed for decision making. A TIP is assigned

to each edge device in the network, and lives in the

same location as that device (for example, in the same

cabinet) monitoring the surrounding environment.

Every few seconds, the TIP takes a snapshot of

its sensor values and analyzes this snapshot to see

what events, if any, are presently affecting the device.

When a sensor exceeds the threshold set by the opera-

tor, the corresponding indicator is considered present

for the purposes of our factor graph, and absent oth-

erwise. These values are used to construct the I set

for use in our factor graph, and also sent to a tamper

decision point to keep the decision point’s worldview

current and to alert the decision point that this TIP is

still active.

If the TIP is able to make an event decision by it-

self, it sends this decision to the appropriate tamper

enforcement points. However, the TIP only has a lim-

ited copy of the system’s factor graph, which means

the TIP is not always able to distinguish between cer-

tain events. In these cases, it sends a request to its

tamper decision point for assistance.

4.6 Tamper Decision Points (TDPs)

Tamper decision points live in higher-security areas

of a utility’s SCADA network, such as within a sub-

station, and they serve multiple TIPs within their ser-

vice area. TDPs are given a full copy of the system’s

factor graph, and they can access data from both from

the TIPs they serve and any external data sources they

query, which allows them to make a more-informed

tamper decision than a single TIP.

When a TDP receives an assistance request from

one of its TIPs, the TDP ﬁrst tries to determine the

overall state of the area it monitors. It does this by

combining the data it receives from all of its TIPs to

see what sensor values are held by the majority of its

TIPs. If a TDP’s factor graph includes l indicators,

the TDP’s indicator state I

overall

is set as follows:

overall

= {maj(i

), maj(i

), . . . , maj(i

)} (1)

maj(i

) =







1 if the majority of TIPs

see i

as present

0 otherwise







(2)

Once the TDP calculates I

overall

, it combines the

data with the local indicators provided by the request-

ing TIP and runs the entire set through the full fac-

tor graph to calculate its own event probabilities and

make a ﬁnal decision as to what event is occurring.

Two edge cases, however, may interfere with this pro-

cess:

• If a TIP stops reporting to the TDP, its data is

eventually declared stale, and the offending TIP

is removed from the summation. The TDP will

also run the factor graph with a “lost TIP” ﬂag set

to see if further response if needed.

• If any of the ma j(i

) functions do not ﬁnd a clear

majority (i.e. the TIPs are evenly split on sensor

p), the TDP can reach out to a second TDP for

assistance. The second TDP attempts to break the

tie by combining its information with that of the

ﬁrst TDP, running the combined I

overall

through

its factor graph, and sending the result back to the

ﬁrst TDP.

Once a decision is made, if an event is thought to

be present, the appropriate tamper enforcement points

are alerted to initiate a response.

4.7 Tamper Enforcement Points (TEPs)

Tamper enforcement points are positioned between

the TIP and its TDP on the SCADA network, and they

SolvingtheGridDefender'sDilemma:TamperProtectionforDistributedCyber-PhysicalSystems

313

are responsible for responding to decisions made by

these devices.

For each TIP, an edge TEP is installed at the TIP’s

location, and a central TEP is placed closer to the

TIP’s TDP. The two TEPs allow us to execute re-

sponses in the location that makes the most sense in

the current context. For example, erasing secret data

on the edge device is best handled by the edge TEP,

while ﬁltering network trafﬁc might be more appro-

priate for the central TEP.

The TEP is not limited to making a single re-

sponse to a decision, but instead can execute an or-

dered series of responses to mitigate any problems.

Exactly which responses to take, and what order to

take them in, is predetermined by the system admin-

istrator. In cases where responses need to be taken on

both the edge and central TEPs, the TEPs coordinate

their response to ensure that responses occur in the

proper order.

5 ADVANTAGES OF TEDDI

In this section, we compare TEDDI to selected prior

works within the context of an example usage sce-

nario, and highlight how TEDDI is able to solve the

grid defender’s dilemma outlined in Section 2.1.

5.1 An Expanded Example

Suppose a utility wants to monitor a set of edge de-

vices deployed in a section of their SCADA network.

The devices are all installed inside metal cabinets, and

are all equipped with a small set of sensors, includ-

ing an accelerometer, light sensor, temperature sen-

sor, and a switch that indicates whether or not the cab-

inet door is open.

The utility is particularly concerned about bypass

attacks, in which an attacker breaks the lock off of an

edge device cabinet, and then opens the cabinet and

replaces the edge device with a device of their choos-

ing, such as an exploit-equipped laptop. (This attack

scenario was initially presented to us by one of our

industry partners.) The attacker could also enter the

cabinet in other ways, such as by drilling through it

or cutting into it with a torch, but breaking the lock is

particularly worrisome because it accesses the cabinet

through it front door, just as a utility service techni-

cian would.

In addition to TEDDI, the utility is considering

three other protection systems for their scenario: The

IBM 4765 Cryptographic Coprocessor (IBM, 2011),

the SCADAHawk system (Sousan et al., 2013), and

the Response and Recovery Engine (RRE) (Zonouz

et al., 2014).

A summary of our comparison results is shown in

Table 1.

5.2 Setup Requirements

As stated in Section 2.1, grid operators do not have a

lot of time or data at their disposal when conﬁguring

a SCADA protection system, and need to be able to

get things up and running quickly. This requirement

causes problems for the RRE, since it requires that the

operator construct a full attack response tree (ART),

complete with every potential step taken by an at-

tacker and the responses that correspond to each step,

for each security goal they want to maintain (Zonouz

et al., 2014). While SCADAHawk does not place as

heavy a burden on the operator, it suffers from the

need to capture the behavior snapshots it needs for

anomaly detection (Sousan et al., 2013). This step is

crucial to guard against false positives, but if the util-

ity does not have data on hand, SCADAHawk must

run for a prolonged period to collect the data it needs.

In contrast, TEDDI tries to minimize the prelimi-

nary work it requires, and begins monitoring the net-

work the minute it is installed. Our factor graphs only

require the event and indicator sets, as well as the re-

lationship between the two (e.g., what indicators are

generated by each event). In this case, the event rela-

tionships can be deﬁned as follows:

• If the light sensor is triggered while the cover

switch is closed, this indicates that the box has

been breached in an inappropriate manner, a tell-

tale sign that an attack is going on. The pres-

ence of shaking indicates that a drill attack is in

progress, whereas a high temperature alert indi-

cates a torch is being used.

• A “normal” bypass attack can be deﬁned as fol-

lows: It starts with shaking (from smashing the

cabinet lock), continues with the cover switch

opening, and concludes with light hitting the light

sensor. This simple sequence can quickly be en-

coded in our factor graph.

• The above bypass attack might look similar to a

technician visit, but there is a big difference: A

utility generally knows when its boxes are being

serviced, and has a record of this within its inci-

dent database. We introduce this information as

an indicator in our full factor graph (with a cor-

responding mechanism that queries the incident

database and serves as our sensor), and combine

the bypass and technician events in our limited

factor graph.

SECRYPT2015-InternationalConferenceonSecurityandCryptography

314

Table 1: A summary of our discussion in Section 5.

Security Easy Detects Physical Event Flexible Solves Grid

Solution Setup Tampering Range Response Defender’s Dilemma?

IBM 4765 No Yes Limited No No

SCADAHawk No No Wide N/A No

RRE No No Wide Yes No

TEDDI Yes Yes Wide Yes Yes

With these rules in place, TEDDI can start moni-

toring the SCADA network immediately.

The IBM 4765 is the simplest of the four pro-

tection systems to set up, as all of its physical pro-

tections are in place and armed from the start (IBM,

2011). While the device is initially equipped to per-

form “cryptographic functions common in the ﬁnance

industry and in Internet business applications” (IBM,

2011), it is also possible to create custom software

for the device if needed. However, it is a device-

centered protection system that provides no protec-

tion to a SCADA network if it gets bypassed.

5.3 Event Detection

There are two elements to consider when thinking

about event detection: How quickly a system can re-

spond to an event, and the range of events a system

can detect. SCADAHawk and the RRE are reac-

tive systems, and look for evidence that an attacker

has already breached the network, either through IDS

alerts (Zonouz et al., 2014) or anomalous system be-

havior (Sousan et al., 2013). Neither SCADAHawk

nor the RRE look for physical tamper events, so in

the case of the bypass attack, the attacker is on the

network and putting their attack plan into action by

the time these two systems notice a problem. By ob-

serving physical events, however, TEDDI can take a

proactive stance against attacks. In the above sce-

nario, for example, TEDDI’s sensors could detect the

shaking caused by the chiseling and the opening of

the cabinet’s door, deduce that an attack may be oc-

curring, and start taking protective measures.

The IBM 4765 responds to physical events as

well (IBM, 2011), but its lack of context informa-

tion can lead to costly false positives, since the de-

vice is unable to say exactly what event triggered its

sensors and thus must assume the worst. TEDDI, on

the other hand, takes advantage of its distributed na-

ture to obtain this context information and differenti-

ate between benign and malicious events. If we again

consider the bypass attack, the affected information

can send an alert to its decision point, which can then

query an external database to see if we expect a tech-

nician to be shaking the box.

5.4 Response Strategy

Differentiating between events is not useful unless

we can adjust our system’s response appropriately.

Most tamper protection systems, however, suffer from

one of two major ﬂaws in this area: They either

only attempt to detect a problem, such as SCADA-

Hawk (Sousan et al., 2013), or they have a single “nu-

clear” response to any sort of tampering, such as the

IBM 4765 (IBM, 2011).

To protect the grid, however, we require a ﬂexible

response strategy that can deal with events of varying

concern. The RRE supports this by connecting re-

sponses with speciﬁc nodes within its attack response

trees (Zonouz et al., 2014), while TEDDI equips its

TEPs with ordered response strategies that it can take

when certain events are seen. Exactly what the TEP

does depends on the event, and the response could

range from stopping the device completely to not in-

tervening at all.

5.5 Solving the Dilemma

A summary of our comparison results is shown in Ta-

ble 1. Overall, TEDDI is the system that is best suited

to solve the grid defender’s dilemma: It it easy to

conﬁgure, can differentiate between important tamper

events, and can tailor its responses to suit the event at

hand.

6 NEXT STEPS

Currently, we have built and tested a small TEDDI

prototype (1 TDP, 5 TIPs), and have demonstrated

how event decisions can change based on the status

of the edge devices. We plan to expand our proto-

type and deploy it on a realistic grid network, and

are working with the electrical network management

team here at Dartmouth to construct this network.

Because TEDDI requires that a customized pro-

gram be built for every TIP, TDP, and TEP in the sys-

tem, we are also building a generation tool that will

perform this task. This tool includes a decision point

layout tool that calculates the optimal locations to

SolvingtheGridDefender'sDilemma:TamperProtectionforDistributedCyber-PhysicalSystems

315

place TDPs, and will also suggest potential responses

for common or similar events.

7 CONCLUSIONS

In this paper, we highlighted the security threat posed

by edge devices in a SCADA network, and pro-

posed our TEDDI system as a means of addressing

this threat. SCADA networks must handle a wide

range of tamper events while supporting a specialized

combination of security goals, and we showed how

TEDDI’s distributed nature, use of factor graphs, and

ﬂexible approach to tamper response give it a distinct

advantage in handling these event and goals.

ACKNOWLEDGEMENTS

This material is based upon work supported by the

Department of Energy under Award Number DE-

OE0000097. The views expressed in this paper are

those of the authors, and do not necessarily reﬂect the

views of the sponsors.

REFERENCES

Atmel Corporation (2015). Atmel Trusted Platform Mod-

ule. Available at: http://www.atmel.com/products/se

curity-ics/embedded/default.aspx (Accessed 2 March

2015).

Berthier, R. and Sanders, W. (2013). Monitoring advanced

metering infrastructures with Amilyzer. In Cyberse-

curity of SCADA and Industrial Control Systems.

Cao, P., Badger, E., Kalbarczyk, Z., Iyer, R., and Slagell, A.

(2015). Preemptive intrusion detection: Theoretical

framework and real-world measurements. In Sympo-

sium and Bootcamp on the Science of Security.

Desai, A. (2013). Anti-counterfeit and anti-tamper imple-

mentation using hardware obfuscation. Master’s the-

sis, Virginia Polytechnic Institute and State Univer-

sity.

Dragone, S. (2013). Physical security protection based on

non-deterministic conﬁguration of integrated micro-

electronic security features. In The First International

Cryptographic Module Conference.

Frey, B. (2003). Extending factor graphs so as to unify di-

rected and undirected graphical models. In Proceed-

ings of the Nineteenth Conference on Uncertainty in

Artiﬁcial Intelligence.

IBM (2011). IBM 4765 PCIe Data Sheet. Available at: htt

p://www-03.ibm.com/security/cryptocards/pciecc/pdf

/PCIe

Spec Sheet.pdf (Accessed 2 March 2015).

Organization for the Advancement of Structured Informa-

tion Standards (2013). eXtensible Access Control

Markup Language (XACML) Version 3.0. Available

at: http://docs.oasis-open.org/xacml/3.0/xacml-3.0-co

re-spec-os-en.pdf (Accessed 2 March 2015).

Pearl, J. (1988). Probabilistic Reasoning in Intelligent

Systems: Networks of Plausible Inference. Morgan

Kauffman.

Peterson, D. (2013). Why Crain / Sistrunk vulns are a big

deal. Digital Bond. Available at: http://www.digitalb

ond.com/blog/2013/10/16/why-crain-sistrunk-vulns-a

re-a-big-deal/ (Accessed 2 March 2015).

Reeves, J. and Smith, S. W. (2014). Tamper event detec-

tion on distributed devices in critical infrastructure. In

ICMC 2014: The Second International Cryptographic

Module Conference.

Roblee, C., Berk, V., and Cybenko, G. (2005). Large-scale

autonomic server monitoring using process query sys-

tems. In IEEE International Conference on Autonomic

Computing.

Smith, R. (2014). U.S. risks national blackout from small-

scale attack. Wall Street Journal. Available at: http:

//online.wsj.com/news/articles/SB100014240527023

04020104579433670284061220 (Accessed 2 March

2015).

Smith, S. W., Palmer, E., and Weingart, S. (1998). Using

a high-performance, programmable secure coproces-

sor. In Second International Conference on Financial

Cryptography.

Smith, S. W. and Weingart, S. (1999). Building a

high-performance, programmable secure coprocessor.

Computer Networks, 31(1999):831–860.

Solomakhin, R. V. (2010). Predictive YASIR: High security

with lower latency in legacy SCADA. Master’s thesis,

Dartmouth College.

Sousan, W. L., Zhu, Q., Gandhi, R., and Mahoney, W.

(2013). Smart grid tamper detection using learned

event patterns. In Pappu, V., Carvalho, M., and Parda-

los, P., editors, Optimization and Security Challenges

in Smart Power Grids, Energy Systems, pages 99–

115. Springer Berlin Heidelberg.

Tygar, J. D. and Yee, B. (1994). Dyad: A system for us-

ing physically secure coprocessors. In Technological

Strategies for the Protection of Intellectual Property

in the Networked Multimedia Environment.

Valdes, A. and Skinner, K. (2001). Probabilistic alert corre-

lation. In Recent Advances in Intrusion Detection.

Wang, Y. and Hauser, C. (2011). An evidence-based

Bayesian trust assessment framework for critical-

infrastructure decision processing. In Fifth Annual

IFIP Working Group 11.10 International Conference

on Critical Infrastructure Protection.

Zonouz, S., Khurana, H., Sanders, W., and Yardley, T.

(2014). RRE: A game-theoretic intrusion response

and recovery engine. IEEE Transactions on Parallel

and Distributed Systems, 25(2):395–406.

Zonouz, S., Rogers, K., Berthier, R., Bobba, R., Sanders,

W., and Overbye, T. (2012). SCPSE: Security-

oriented cyber-physical state estimation for power

grid critical infrastructures. IEEE Transactions on

Smart Grid, 3(4):1790–1799.

SECRYPT2015-InternationalConferenceonSecurityandCryptography

316