From 6fac195a973ead5e2c79ecc523b19830c8268be4 Mon Sep 17 00:00:00 2001
From: jaseg <git-bigdata-wsl-arch@jaseg.de>
Date: Thu, 7 Apr 2022 17:59:50 +0200
Subject: WIP

---
 paper/safety-reset-paper.tex | 337 ++++++++++++++++++-------------------------
 1 file changed, 138 insertions(+), 199 deletions(-)

(limited to 'paper/safety-reset-paper.tex')

diff --git a/paper/safety-reset-paper.tex b/paper/safety-reset-paper.tex
index fc62bc8..3b7a93e 100644
--- a/paper/safety-reset-paper.tex
+++ b/paper/safety-reset-paper.tex
@@ -1,4 +1,6 @@
-\documentclass[runningheads]{llncs}
+\documentclass[letterpaper,twocolumn,10pt]{article}
+\usepackage{usenix}
+
 \usepackage[T1]{fontenc}
 \usepackage[
     backend=biber,
@@ -32,165 +34,64 @@
 
 % https://eepublicdownloads.entsoe.eu/clean-documents/pre2015/publications/entsoe/Operation_Handbook/Policy_1_Appendix%20_final.pdf
 
+\date{}
 \title{Ripples in the Pond: Transmitting Information through Grid Frequency Modulation}
-\titlerunning{Ripples in the Pond: Transmitting Information through Grid Frequency}
 \author{Jan Sebastian Götte \and Liran Katzir \and Björn Scheuermann}
-\institute{TU Darmstadt\\ Communication Networks Lab\\ \email{safetyreset@jaseg.de}
-\and Tel Aviv University\\ Faculty of Engineering\\ \email{lirankat@tau.ac.il}
-\and TU Darmstadt\\ Communication Networks Lab\\ \email{scheuermann@informatik.hu-berlin.de}}
+%\institute{TU Darmstadt\\ Communication Networks Lab\\ \email{safetyreset@jaseg.de}
+%\and Tel Aviv University\\ Faculty of Engineering\\ \email{lirankat@tau.ac.il}
+%\and TU Darmstadt\\ Communication Networks Lab\\ \email{scheuermann@informatik.hu-berlin.de}}
 \maketitle
-\keywords{Security, privacy and resilience in critical infrastructures \and Security and privacy in ``internet of
-things'' \and Cyber-physical systems \and Hardware security \and Network Security \and Energy systems \and Signal theory}
+%\keywords{Security, privacy and resilience in critical infrastructures \and Security and privacy in ``internet of
+%things'' \and Cyber-physical systems \and Hardware security \and Network Security \and Energy systems \and Signal theory}
 
 \begin{abstract}
-    With the rollout of the smart grid, the IT security of electrical infrastructure has attracted increased attention
-    in the last years. Smart Grid IT security has two major components: The security of central SCADA systems, and
-    the security of equipment at the consumer premises such as smart meters and IoT devices. While there is previous
-    work on both sides, their interactions have not yet received much attention.
-
-    In this paper, we consider the previously proposed scenario where a large number of compromised consumer devices is
-    used alone or in conjunction with an attack on the grid's central SCADA systems to destabilize the grid by rapidly
-    modulating the total connected load. Such attacks might include IoT devices, but they might also target Smart
-    Meters, which in many parts of the world now contain remote-controlled disconnect switches. Such attacks are hard to
-    mitigate, and existing literature focuses on hardening device firmware to prevent compromise. Although perfect
-    firmware security is not practically achievable, there is little research on \emph{post-compromise} mitigation
-    approaches. A core issue of any post-attack mitigation is that the devices normal network connection may not work
-    due to the attack and as such an out-of-band communication channel is necessary.
-
-    We propose a \emph{safety reset} controller that is controlled through a novel, resilient, grid-wide powerline
-    communication technique. Our safety reset controller can be fitted into any Smart Meter or IoT device. Its purpose
-    is to await an out-of-band command to put the device into a safe state (e.g. \emphp{relay on} or \emph{light on})
-    that interrupts attacker control over the device. The safety reset controller is separated from the system's main
-    application controller and does not have any conventional network connections to reduce attack surface and cost.
-
-    Our proposed resilient communication channel is a grid-wide broadcast channel based on modulating grid frequency. It
-    can be operated by transmission system operators (TSOs) even during black-start recovery procedures and in this
-    situation bridges the gap between the TSO's private network and the consumer devices. To demonstrate our proposed
-    channel, we have implemented a system that transmits error-corrected and cryptographically secured commands.
-
-    Our approach differs from traditional Powerline Communication (PLC) systems in that it reaches every device within
-    the same synchronous area as the signal is embedded into the fundamental grid frequency. Traditional PLC uses a
-    superimposed voltage, which is quickly attenuated across long distances.
-
-    Using simulations we have determined that control of a $\SI{25}{\mega\watt}$ load such as a large aluminium smelter,
-    load bank or photovoltaic farm would allow for the transmission of a crytographically secured \emph{reset} signal
-    within $15$ minutes. We have designed and constructed a proof-of-concept prototype receiver that demonstrates the
-    feasibility of decoding such signals on a resource-constrained microcontroller.
+    Previous work has explored the scenario of an attacker compromising a large number of Smart Meters that are equipped
+    with remote disconnect switches, and using these remote-controllable switches to cause a large-scale outage.
+    Previous work focuses on attack prevention. In this paper, we will instead look at recovery after a successful
+    attack. To transmission system operators (TSOs), the major challenge after such a Smart Meter-triggered outage is
+    that the attacker will likely persist through the outage, and compromised Smart Meters will resume malicious
+    activity after their power is restored. In the event of such an attack, TSOs would need a way to remotely put these
+    compromised devices into a \emph{safe} mode of operation. 
+
+    Given that public telecommunications networks including the internet, cellular networks, and LoRa base stations may
+    also be disrupted during a large-scale blackout, the challenging aspect of this remote \emph{Safety Reset} is the
+    communication channel between TSO and the smart meter. For this purpose, in this paper we propose a simple yet
+    effective communication channel based on modulating grid frequency by modulating the power of a connected load or
+    generator. Our proposed communciation channel (1) requires minimal infrastructure, (2) has a reach spanning the
+    entire power grid and (3) is fully independent of other telecommunication networks and functions even under severe
+    disruption of the grid.
 \end{abstract}
 
 \section{Introduction}
 
-% FIXME This is meh.
-% Maybe *start* with "the recovery from a blackout bla bla..."?
-The power grids of the world are some of the most complex man-made technological systems. Their operation is essential
-for modern human life and with the proliferation of ransomware and state-sponsored attacks their IT security has come
-under close scrutiny. To grid operators, there are two main challenges that complicate IT security efforts. First, all
-parts of the electrical grid are physically coupled and faults can have consequences far from their source. Second, many
-of the networked devices used in grid applications are special-purpose devices built in low volumes, which limits the
-amount of engineering effort that could have been spent on their firmware security.
-
-We expect that a serious compromise can never fully be ruled out since the combined attack surface of a large number of
-diverse devices is too large to effectively secure, and perimeter security measures are only effective to a point when
-devices are spread out across a vast geographical area. Thus, in this paper we focus not on the prevention of an attack,
-but on the recovery from one. 
-%The IT security of the power grid is a complicated issue. Transmission system operators are faced with multiple
-%challenges. 
-
-%First, the grid is composed of myriad different devices that are interconnected on a contintental scale. Since all these
-%devices are physically coupled, faults in one system can have ripple effects far away. In other critical infrastructure
-%such as the water supply, transportation or the public health system, a number of fundamentally independent sub-systems
-%are only linked at an organizational level, which means faults due to either natural disasters or hacking attacks are
-%likely to be localized. In contrast, a transmission system operator has to make sure no faults happen anywhere in the
-%system for the system to be stable. Ensuring faultless operation across thousands of devices is hard.
-
-%Like any other complex technological system, the components that make up the power grid are increasingly being outfitted
-%with networked computer systems for monitoring and control. 
-%They have to secure a large and diverse fleet of networked systems, many of which are special-purpose devices customized
-%for this particular application. Small production quantities
-%mean that the limit of economically achievable security is already low. Coupled with the high complexity of each of
-%these devices, this results in 
-
-\subsection{The digitalization of the grid}
-In the power grid, as in many other engineered systems, we can observe an ongoing diffusion of information systems into
-the domain of industrial control. Automation of these control systems has already been practiced for the better part of a
-century.  Throughout the 20th century this automation was mostly limited to core components of the grid. Generators in
-power stations are computer-controlled according to electromechanical and economic models. Switching in substations is
-automated to allow for fast failure recovery. Human operators are still vital to these systems, but their tasks have
-shifted from pure operation to engineering, maintenance and surveillance~\cite{crastan03,anderson02}.
-
-With the turn of the century came a large-scale trend in power systems to move from a model of centralized generation,
-built around massive large-scale fossil and nuclear power plants, towards a more heterogenous model of smaller-scale
-generators working together. In this new model large-scale fossil power plants still serve a major role, but new
-factors come into play. One such factor is the advance of renewable energies. The large-scale use of wind and solar
-power in particular seems unavoidable for continued human life on this planet. For the electrical
-grid these systems constitute a significant challenge. Fossil-fueled power plants can be controlled in a precise and
-quick way to match energy consumption. This tracking of consumption with production is vital to the stability of the
-grid. Renewable energies such as wind and solar power do not provide the same degree of controllability, and they
-introduce a larger degree of uncertainty due to the unpredictability of the forces of nature~\cite{crastan03}.
-
-Along with this change in dynamic behavior, renewable energies have brought forth the advance of distributed generation.
-In distributed generation end customers that previously only consumed energy have started to feed energy into the grid
-from small solar installations on their property. Distributed generation is a chance for customers to gain autonomy and
-shift from a purely passive role to being active participants of the electricity market~\cite{crastan03}.
-
-% FIXME the following paragraph is weird.
-
-To match this new landscape unpredictable renewable resources and of decentralized generation, the utility industry has
-had to adapt itself in major ways. One aspect of this adaptation that is particularly visible to energy consumers is the
-computerization of end-user energy metering. Despite the widespread use of industrial control systems inside the
-electrical grid and the far-reaching diffusion of computers into people's everyday lives, the energy meter has long been
-one of the last remnants of an offline, analog time. Until the 2010s many households were still served through
-electromechanical Ferraris-style meters that have their origin in the late 19th
-century~\cite{borlase01,ukgov04,bnetza02}.  Today, under the umbrella term \emph{Smart Metering}, the shift towards fully
-computerized, often networked meters is well underway. The roll out of these \emph{Smart Meters} has not been very
-smooth overall with some countries severely lagging behind. As a safety-critical technology, smart metering technology
-is usually standardized on a per-country basis.
-
-\subsection{Perfect firmware security}
-% FIXME join these paragraphs
-This leads to an inhomogenous landscape with---in some
-instances---wildly incompatible systems.  Often vendors only serve a single country or have separate models of a meter
-for each country.  This complex standardization landscape and market situation has led to a proliferation of highly
-complex, custom-coded microcontroller firmware. The complexity and scale of this---often network-connected---firmware
-makes for a ripe substrate for bugs to surface.
-
-A remotely exploitable flaw inside the firmware of a component of a smart metering system could have consequences
-ranging from impaired billing functionality to an existential threat to grid stability~\cite{anderson01,anderson02}. In a
-country where meters commonly include disconnect switches for purposes such as prepaid tariffs, a coordinated attack
-could at worst cause widespread activation of grid safety systems through oscillations caused by repeated cycling of
-megawatts of load capacity at just the wrong frequency~\cite{wu01}.
-
-Mitigation of these attacks through firmware security measures is unlikely to yield satisfactory results. The enormous
-complexity of smart meter firmware makes firmware security extremely labor intensive. The diverse standardization
-landscape makes a coordinated, comprehensive response unlikely.
-
-In this paper, we introduce \emph{Grid Frequency Modulation}, a new communication channel that can be used for grid-wide
-broadcast without relying on any other telecommunication networks being operational.  Grid Frequency Modulation uses
-Direct Sequence Spread Spectrum (DSSS) modulation carried out on grid frequency through a large controllable load such
-as an aluminium smelter.  Note that Grid Frequency Modulation is \emph{changing the grid frequency itself}.  This is
-fundamentally different in both generation and detection from systems such as traditional PLC that superimpose a signal
-on grid voltage, but leave the underlying grid frequency itself unaffected. As it requires high-fidelity control over a
-large load or producer connected to the grid, Grid Frequency Modulation provides a degree of implicit sender
-authentication.
-
-To illustrate the utility of Grid Frequency Modulation we propose a pragmatic solution to the---in our opinion
-likely---scenario of a large-scale compromise of smart meter firmware.  Instead of improving firmware security or
-resiliency of public telecommunication infrastructure, both of which are hard problems, we introduce the \emph{safety
-reset controller} as a fail-safe that allows an utility to flush an attacker out of their deployed smart meters even
-during large-scale disruption of telecommunication networks.  In our concept the components of the smart meter that are
-threatened by remote compromise are equipped with a physically separate microcontroller that listens for a ``reset''
-command transmitted through the electrical grid's frequency and on reception forcibly resets the smart meter's entire
-firmware through a low-level programming interface such as JTAG to a known-good state that disables all network
-functionality to prevent re-compromise and lock out the attackers until the device can be programmed with a patched
-firmware by a service technician.  As part of our prototype reset controller we have developed a simple cryptographic
-command protocol based on the Lamport and Winternitz One-time Signature (OTS) schemes that our prototype reset
-controller uses to receive an authenticated command to re-flashe the smart meter's main microcontroller over the
-standard JTAG interface. The safety reset controller is an off-the-shelf microcontroller much smaller than the one used
-for the meter's main application controller. To receive grid frequency-modulated commands, it measures grid frequency
-from a voltage waveform acquired using its internal analog-to-digital-converter (ADC) directly connected to the mains
-voltage input through a resistive divider chain. By using of an off-the-shelf microcontroller we keep the implementation
-overhead of our solution low in engineering cost compared to an ASIC. By keeping its firmware small, we can use a
-simpler and less expensive microcontroller, keeping per-unit implementation cost low.
+With the rollout of the smart grid, the IT security of electrical infrastructure has attracted increased attention in
+the last years. Smart Grid security has two major components: The security of central SCADA systems, and the security
+of equipment at the consumer premises such as smart meters and IoT devices. While there is previous work on both sides,
+their interactions have not yet received much attention.
+
+In this paper, we consider the previously proposed scenario where a large number of compromised consumer devices is used
+alone or in conjunction with an attack on the grid's central SCADA systems to destabilize the grid by rapidly modulating
+the total connected load. Previous work considered compromised smart meters with integrated remote disconnect switches
+as likely candidates for such an attack, but the same attack can also be performed using compromised IoT devices.  Such
+attacks are hard to mitigate, and existing literature focuses on hardening device firmware to prevent compromise.
+Despite the infeasibility of perfect firmware security, there is little research on \emph{post-compromise} mitigation
+approaches. A core issue with post-attack mitigation is that the devices normal network connection may not work due to
+the attack and as such an out-of-band communication channel is necessary.
+
+We propose a \emph{safety reset} controller that is controlled through a novel, resilient, grid-wide powerline
+communication technique. Our safety reset controller can be fitted into any Smart Meter or IoT device. Its purpose is to
+await an out-of-band command to put the device into a safe state (e.g. \emph{relay on} or \emph{light on}) that
+interrupts attacker control over the device. The safety reset controller is separated from the system's main application
+controller and does not have any conventional network connections to reduce attack surface and cost.
+
+We propose a resilient grid-wide broadcast channel based on modulating grid frequency. This channel can be operated by
+transmission system operators (TSOs) even during black-start recovery procedures and in this situation bridges the gap
+between the TSO's private network and the consumer devices. To demonstrate our proposed channel, we have implemented a
+system that transmits error-corrected and cryptographically secured commands.
+
+Our approach differs from traditional Powerline Communication (PLC) systems in that it reaches every device within one
+synchronous area as the signal is embedded into the fundamental grid frequency. Traditional PLC uses a superimposed
+voltage, which is quickly attenuated across long distances.
 
 \begin{figure}
     \centering
@@ -208,6 +109,59 @@ broadcast radio). A grid frequency-based system can function as long as power is
 restored after the attack. One powerful function this allows is ``flushing out`` an attacker from compromised smart
 meters after an attack, before restoring smart meter internet connectivity.
 
+Using simulations we have determined that control of a $\SI{25}{\mega\watt}$ load such as a large aluminium smelter,
+load bank or photovoltaic farm would allow for the transmission of a crytographically secured \emph{reset} signal within
+$15$ minutes. We have designed and constructed a proof-of-concept prototype receiver that demonstrates the feasibility
+of decoding such signals on a resource-constrained microcontroller.
+
+\subsection{Motivation}
+
+Consumer devices are increasingly becoming \emph{smart}. Large numbers of IoT devices are connected through the public
+internet, and in several countries internet-connected Smart Meters can disconnect entire households from the grid in
+case of unpaid bills. The increasing proliferation of smart devices on the consumer side presents an opportunity to grid
+operators, who rely on forecasts for the cost-optimized control of generation and power flow. The core of the
+\emph{Smart Grid} vision is that utilities can now gather detailed data for more accurate consumption forecasts, and in
+some cases can even adjust parameters of large devices like water heaters to smooth out load spikes.
+
+However, this increased degree of visibility and control comes with an increased IT security risk. In this paper we
+focus on scenarios where an attacker compromises a large number of grid-connected remote-controllable devices. This may
+be simple smart home devices such as IoT light bulbs, but it may also include Smart Meters that are outfitted with a
+remote disconnect switch as is common in some countries. By rapidly switching large numbers of such devices in a
+coordinated manner, the attacker has the opportunity to de-stabilize the electrical grid. % FIXME citation
+
+Previous work on IoT and Smart Grid security has focused on the prevention of attacks though firmware security measures.
+While research on prevention is undoubtably important, we estimate that its practical impact will be limited by the vast
+diversity of implementations found in the field combined with the slow update cycles inherent to non-functional firmware
+enhancements for consumer devices. We predict that it would be a Sisyphean task to secure sufficiently many devices
+to deny an attacker the critical mass needed to cause trouble. For this reason, in this paper we focus on recovery after
+an attack.
+
+\subsection{Black-start recovery}
+
+The recovery from a large-scale power outage is a complex operational challenge. Large outages are caused by cascading
+failures. Since all consumers and producers that are connected to the electrical grid are physically coupled through the
+electromotive force, a fault in one part of the grid affects all devices connected across the grid. To function, the
+grid relies on a delicate balance between electricity generation, transmission and consumption.  When this balance is
+disturbed, cascading failures can occur. A transmission line shutting off can lead other, nearby lines to overload and
+shut off. Due to the electromechanical coupling of all machines connected to the grid, a generator or consumer suddenly
+shutting off causes a transient in the grid's frequency. If the frequency goes too far out of bounds, protection devices
+take power plants and large industrial loads offline.
+
+The recovery from a large-scale outage requires the grid's operators to bring generators and loads back online one by
+one while continuously maintaining balance between generation and consumption to avoid their protection devices shutting
+them down again. To coordinate this process, transmission system operators cannot rely on the public internet or
+cellular networks, as they may not work during a large-scale power outage. Instead, they maintain private communication
+infrastructure using dedicated lines rented from telecommunciations providers, fibers run along transmission lines, and
+dedicated radio links.
+
+To start from a complete outage, first a number of \emph{black start}-capable power stations that can start by
+themselves without any external power are brought online. With their help, other power stations and consumers are
+gradually brought online until a part of the grid has been restored to nominal operation. This process can be performed
+simultaneously in different parts of the grid. After these \emph{islands} have been restored, they can then be joined to
+restore the grid to its normal state.
+
+\subsection{Contents}
+
 Starting from a high level architecture, we have carried out simulations of our concept's performance under real-world
 conditions. Based on these simulations we implemented an end-to-end prototype of our proposed safety reset controller as
 part of a realistic smart meter demonstrator. Finally, we experimentally validated our results and we will conclude with
@@ -222,7 +176,7 @@ This work contains the following contributions:
     \item We carry out extensive simulations of our systems to determine its performance characteristics.
 \end{enumerate}
 
-\section{Notation}
+\subsection{Notation}
 
 To a computer scientist there is one confusing aspect to the theory of grid frequency modulation. GFM can be seen as a
 frequency modulation (FM) with a baseband signal in the band below approximately $f_m = \SI{5}{\hertz}$ that is
@@ -242,53 +196,38 @@ signal and its properties such as $f_m$.
 \section{Related work} 
 \label{sec_related_work}
 
-% FIXME: intro here
-
-\subsection{Security and Privacy in the Smart Grid}
-
-The smart grid in practice is nothing more or less than an aggregation of embedded control and measurement devices that
-are part of a large control system. This implies that all the same security concerns that apply to embedded systems in
-general also apply to the components of a smart grid. Where programmers have been struggling for decades now with issues
-such as input validation~\cite{leveson01}, the same potential issue raises security concerns in smart grid scenarios as
-well~\cite{mo01, lee01}.  Only, in smart grid we have two complicating factors present: many components are embedded
-systems, and as such inherently hard to update. Also, the smart grid and its control algorithms act as a large partially
-distributed system making problems such as input validation or authentication harder~\cite{blaze01} and adding a host of
-distributed systems problems on top~\cite{lamport01}.
-
-Given that the electrical grid is essential infrastructure in our modern civilization, these problems amount to
-significant issues. Attacks on the electrical grid may have grave consequences~\cite{anderson01,lee01} while the long
-replacement cycles of various components make the system slow to adapt. Thus, components for the smart grid need to be
-built to a much higher standard of security than most consumer devices to ensure they live up to well-funded attackers
-even decades down the road. This requirement intensifies the challenges of embedded security and distributed systems
-security among others that are inherent in any modern complex technological system. The safety-critical nature of the
-modern smart metering ecosystem in particular was quickly recognized~\cite{anderson01}.
-
-A point we will not consider in much depth in this work is theft of electricity. While in publications aimed towards the
-general public the introduction of smart metering is always motivated with potential cost savings and ecological
-benefits, in industry-internal publications the reduction of electricity theft is often cited as an
-incentive~\cite{czechowski01}.  Likewise, academic publications tend to either focus on other benefits such as generation
-efficiency gains through better forecasting or rationalize the consumer-unfriendly aspects of smart metering with social
-benefits~\cite{mcdaniel01}. They do not usually point out revenue protection mechanisms as
-incentives~\cite{anderson01,anderson02}.
-
-A serious issue in smart metering setups is customer privacy. Even though the meter ``only'' collects aggregate energy
-consumption of a whole household, this data is highly sensitive~\cite{markham01}. This counterintuitive fact was
-initially overlooked in smart meter deployments leading to outrage, delays and reduced features~\cite{cuijpers01}. The
-root cause of this problem is that given sufficient time resolution these aggregate measurements contain ample
-entropy. Through disaggregation algorithms, individual loads can be identified and through pattern matching even complex
-usage patterns can be discerned with alarming accuracy~\cite{greveler01} in the same way that similar privacy issues
-arise in many other areas of modern life through other kinds of pervasive tracking and surveillance~\cite{zuboff01}.
-
-Another fundamental challenge in smart grid implementations is the central role of smart electricity meters in the smart
-grid ecosystem. Smart meters are used both for highly-granular load measurement and in some countries also for load
-switching~\cite{zheng01}.  Smart electricity meters are effectively consumer devices. They are built down to a certain
-price point that is measured by the burden it puts on consumers and that is divided by the relatively small market
-served by a single smart meter implementation. Such cost requirements can preclude security features such as the use of
-a standard hardened software environment on a high powered embedded system.  Landis+Gyr, a large manufacturer that makes
-most of its revenue from utility meters state in their 2019 annual report that they invested \SI{36}{\percent} of their
-total R\&D budget on embedded software while spending only \SI{24}{\percent} on hardware
-R\&D~\cite{landisgyr01,landisgyr02}, indicating a significant tension between firmware security and a smart meter
-vendor's bottom line. 
+Previous work has analyzed Smart Grid security from numerous angles and made several suggestions towards its
+improvement.  Apart from the critical location that Smart Grid devices occupy, they are computer systems like many
+others.  Thus, for IT security purposes the Smart Grid is simply an aggregation of embedded control and measurement
+devices that are part of a large control system. These devices share the same security concerns that apply to embedded
+systems in general. 
+
+\subsection{Smart Meter Security}
+
+Where programmers have been struggling for decades now with issues such as input validation~\cite{leveson01}, the same
+potential issue raises security concerns in smart grid scenarios as well~\cite{mo01, lee01}.  Only, in smart grid we
+have two complicating factors present: many components are embedded systems, and as such inherently hard to update.
+Also, the smart grid and its control algorithms act as a large (partially) distributed system making problems such as
+input validation or authentication harder~\cite{blaze01} and adding a host of distributed systems problems on
+top~\cite{lamport01}.
+
+Given that the electrical grid is essential infrastructure, these issues are significant. Attacks on the electrical grid
+may have grave consequences~\cite{anderson01,lee01} while the long replacement cycles of various components make the
+system slow to adapt. Thus, components for the smart grid need to be built to a higher standard of security than e.g.\
+IoT devices to live up to well-funded attackers decades down the road.  Another implication of their long service life
+is that their agility w.r.t.\ post-hoc mitigations through firmware updates is limited.
+
+%Another fundamental challenge in smart grid implementations is the central role of smart electricity meters in the
+%smart grid ecosystem. Smart meters are used both for highly-granular load measurement and in some countries also for
+%load switching~\cite{zheng01}.
+Smart electricity meters are effectively consumer devices built down to a certain price point. The small market served
+by a single smart meter implementation limits how much effort a vendor can spend on firmware security. Landis+Gyr, a
+large manufacturer that makes most of its revenue from utility meters state in their 2019 annual report that they
+invested \SI{36}{\percent} of their total R\&D budget on embedded software while spending only \SI{24}{\percent} on
+hardware R\&D~\cite{landisgyr01,landisgyr02}, indicating significant tension between firmware security and the vendor's
+bottom line. 
+
+% FIXME more sources!
 
 \subsection{The state of the art in embedded security}
 
-- 
cgit 

