Paper: WIP

1 files changed, 253 insertions, 228 deletions

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@@ -1,14 +1,8 @@
-\documentclass[letterpaper,twocolumn,10pt]{article}
-\usepackage{usenix}
-
-\usepackage{amssymb,amsmath}
-\usepackage{eurosym}
-\usepackage{wasysym}
+\documentclass[sigconf,anonymous]{acmart}
 
 \usepackage[binary-units]{siunitx}
 \DeclareSIUnit{\baud}{Bd}
 \DeclareSIUnit{\year}{a}
-\usepackage{commath}
 \usepackage{graphicx,color}
 \usepackage{subcaption}
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@@ -24,16 +18,6 @@
 
 % https://eepublicdownloads.entsoe.eu/clean-documents/pre2015/publications/entsoe/Operation_Handbook/Policy_1_Appendix%20_final.pdf
 
-\date{}
-\title{\large\bf Ripples in the Pond:\\Transmitting Information through Grid Frequency Modulation}
-\author{{\rm Jan Sebastian Götte}\\TU Darmstadt \and {\rm Liran Katzir}\\Tel Aviv University\and {\rm Björn Scheuermann}\\TU Darmstadt}
-%\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}
-
 \begin{abstract}
     The dependence of the electrical grid on networked control systems is steadily rising. While utilities are defending
     their side of the grid effectively through rigorous IT security measures such as physically separated control
@@ -56,6 +40,16 @@
     equipped with a prototype safety reset system based on an inexpensive commodity microcontroller.
 \end{abstract}
 
+\date{}
+\title{\large\bf Ripples in the Pond:\\Transmitting Information through Grid Frequency Modulation}
+\author{{\rm Jan Sebastian Götte}\\TU Darmstadt \and {\rm Liran Katzir}\\Tel Aviv University\and {\rm Björn Scheuermann}\\TU Darmstadt}
+%\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}
+
 \section{Introduction}
 
 With the rollout of the smart grid, the IT security of electrical infrastructure has attracted increased attention in
@@ -143,38 +137,17 @@ However, this increased degree of visibility and control comes with an increased
 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.
+coordinated manner, the attacker has the opportunity to de-stabilize the electrical
+grid~\cite{zlmz+21,kgma21,smp18,hcb19}.
+
+In this paper, we focus on assisting the recovery procedure after a succesful attack because we estimate that this
+approach will yield a better return of investement in overall grid stability versus resources spent on security
+measures. Previous work on IoT and Smart Grid security has focused on the prevention of attacks though firmware security
+measures. While research on prevention is important, we estimate that its practical impact will be limited by the
+diversity of implementations found in the field~\cite{nbck+19,zlmz+21}. We predict that it would be a Sisyphean task to
+secure the firmware of sufficiently many devices to deny an attacker the critical mass needed to cause trouble. Even if
+all flaws in the firmware of a broad range of devices would be fixed, users still have to update. In smart grid and IoT
+devices, this presents a difficult problem since user awareness is low~\cite{nbck+19}.
 
 \subsection{Contents}
 
@@ -194,7 +167,7 @@ This work contains the following contributions:
 \end{enumerate}
 
 \subsection{Notation}
-
+% FIXME drop or rework this section ; actually update notation to be consistent throughout
 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
 modulated on top of a carrier signal at $f_c = \SI{50}{\hertz}$ in case of the European electrical grid. The frequency
@@ -210,195 +183,238 @@ them, in this paper we will use \textbf{bold} letters to refer to the carrier wa
 $\mathbf{f_c}$ as well as its deviation $\mathbf{f_\Delta}$, and we will use normal weight for the actual modulation
 signal and its properties such as $f_m$.
 
+\section{Background on the electrical grid}
+\subsection{Components and interactions}
+
+The electrical grid transmits alternating current electrical power from generators to loads. Any device that is
+connected to the grid must run ``synchronously'' with the grid, i.e.\ it must produce or consume power following the
+grid's voltage waveform. In generators and motors, the electromotive force acts to synchronize the device with the grid.
+Connecting a generator that has not been synchronized to the grid leads to large currents flowing through the
+generator's windings, inducing extreme forces that can mechanically destroy the generator. Similarly, if the inverters
+of a solar power station would try to fight the grid, the grid would win and the inverters' power semiconductors would
+release their magic smoke.
+
+Originally, all power sources on the grid were synchronous rotating generators. Today, the shift towards renewable
+energies and the introduction of high-voltage DC links has led to some of the grid's generating capacity being replaced
+with inverters that electronically emulate the grid's voltage waveform to efficiently convert a DC input to the grid's
+alternating current.
+
+The generators and loads on the grid are linked through a complex network of transmission lines. Transformers are used
+to couple between transmission lines operating at different voltage levels, and several types of switches allow
+utilities to steer power flow throughout this network. Through the electromotive force, all synchronous generators
+connected to the grid are electromechanically coupled. Transmission lines introduce a (small) phase delay to the
+electric fields traversing the grid, but besides local differences in phase, all parts of the grid are synchronous.  
+
+\subsection{Grid frequency behavior}
+
+On the electrical grid, generation and consumption of energy must be precisely matched at all times for the grid to stay
+at a constant, synchronous frequency. If generation outpaces consumption, generators would provide less mechanical
+resistance to their source of mechanical power, or \emph{prime mover}, which would lead the generators to spin faster
+and faster. Similarly, if consumption outpaced production, the increased mechanical load would slow down generators,
+ultimately leading to a collapse.
+
+The frequency of the electrical grid is maintained at a fixed, stable level through several layers of measures.
+
+\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{Demand-side response and Smart Metering}
+
+Maintaining the balance between electricity generation and consumption under varying load conditions is critical.
+Utilities can access different energy sources, each of which have their own trade-off in response speed versus energy
+cost. For instance, the availability of wind and solar power cannot be controlled at all, while hydroelectric power
+plants can quickly regulate the speed and power output of their turbines. Combined with the complex layout of the grid's
+infrastructure such as transmission lines, these economical factors lead to a complex optimization problem, the quality
+of whose solution directly manifests itself in the utility's bottom line.
+
+For decades, one solution to this issue has been demand-side response (DSR)~\cite{rs48}. In DSR, large loads such as
+water heaters are centrally controlled by the utility to switch on outside of peak demand. Since the precise timing of
+these loads is of no consequence to their user, users are happy to get slightly better prices from their utility while
+utilities gain a degree of control allowing them to optimize their network's performance. As part of the smart grid
+vision, DSR will be utilized in a larger fraction of consumer devices.
+
+A core component of the smart grid is the rollout of ``Advanced Metering Infrastructure'' (AMI), colloquially known as
+smart meters. Smart meters are electricity meters that use a real-time communication interface to automatically transmit
+high-resolution measurements to the utility. In contrast to the yearly reading schedule of traditional electricity
+meters, smart meters can provide near-realtime data that the utility can use for more accurate load forecasting.
+
+\subsection{Powerline Communication (PLC)}
+
+A core issue in smart metering is the communication channel from the meter to the greater world. Smart meters are
+cost-constrained devices, which limits the use of landline internet or cellular conenctions. Additionally, electricity
+meters are often installed in basements, far away from the customer's router and with soil and concrete blocking radio
+signals. For these reasons, in some AMI deployments, powerline communication (PLC) has been chosen for the meters'
+uplink.
+
+Since the early days of the electrical grid, powerline communication has been used to control devices spread throughout
+the grid from a central transmitter~\cite{rs48}. PLC systems super-impose a modulated high-frequency signal on top of
+the grid voltage. When the carrier frequency of this modulation is in the audible frequency range, low data rates can be
+transmitted over distances of several tens of kilometers. By using a radio frequency carrier, higher data rates can be
+achieved across shorter distances. Audio frequency PLC, called ``ripple control'', is still used today by utilities to
+enable ``demand-side response'', i.e.\ the remote switching of loads such as water heaters to avoid times of peak
+electricity demand.
+
+Usually, such powerline communication systems are uni-directional but they are instance of bi-directional powerline
+communication for smart meter reading such as the italian smart meter deployment~\cite{ec03,rs48,gungor01,agf16}.
+
 \section{Related work} 
 \label{sec_related_work}
 
-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 consumer devices built down to a price. Firmware security research and development budgets
+\subsection{IoT and Smart Grid security}
+
+The security of IoT devices as well as the smart grid has received extensive attention in the
+literature~\cite{nbck+19,acsc20,smp18,ykll17,anderson01,anderson02,zlmz21,kgma21,hcb19,mpdm10,lzlw+20,chl20,lam21,olkd20,yomu+20,}.
+The challenges of IoT device security and the security of smart meters and other smart grid devices are similar because
+smart grid devices are essentially IoT devices in a particularly sensitive location~\cite{acsc20}. In both device types,
+the challenge is that securing embedded firmware is difficult, and adding network interfaces and cost constraints only
+makes the task harder.
+
+In~\cite{smp18}, Soltan, Mittal and Poor investigated an attack scenario where an attacker first gains control over a
+large number of high wattage devices through an IoT security vulnerability, then uses this control to cause rapid load
+spikes. The researchers performed computer simulations for a range of parameters and concluded that given sufficiently
+many compromised devices, an attacker can cause issues up to a large-scale blackout.
+
+In~\cite{hcb19}, Huang, Cardenas and Baldick raised a counter-point to the conclusions of Soltan et al., finding that
+limitations of their simulations in~\cite{smp18} have lead them to over-estimate the severity of an attack. Using a more
+accurate model, they confirmed that such attacks can cause problems such as localized blackouts and the decay of the
+grid into islands, but they found that overall the electrical grid is less vulnerable than previously assumed and
+particularly large-scale blackouts are very unlikely, primarily due to the action of protection systems such as load
+shedding and over frequency protection.
+
+From literature, we get the overall impression that both IoT and Smart Grid security are challenging. Both lack behind
+the security standard of state of the art desktop, server and smartphone operating systems. Reasons for this are the
+relatively recent nature of the IoT software ecosystem and the large number of independent implementations. A unique
+challenge to Smart Grid security is that due to the fragmentation of markets along national borders, certain devices
+such as smart meters or DSR implementations exist in large monocultures.
+
+Compared to IoT and Smart Grid devices, the embedded firmware foundations of modern smartphones have received more
+attention both from the industry and from academia. Pinto and Santos in~\cite{pinto01} conducted a survey of
+implementations based on ARM's TrustZone embedded virtualization architecture and found a significant number of reported
+vulnerabilities across different implementations. For instance, Rosenberg in~\cite{rosenberg01} found critical issues in
+Qualcomm's QSEE hypervisor, and Kanonov and Wool in~\cite{kanonov01} identified a number of design weaknesses and
+security vulnerabilities in Samsung's competing KNOX virtualization product. To us, the state of the field of embedded
+security indicates that even if significant effort is spent on the security of IoT and Smart Grid devices to catch up
+with desktop, server and smartphone security, significant vulnerabilities are likely to remain for some time to come.
+In this instance, market forces do not align with the interest of the public at large. Vulnerabilities remain likely,
+especially in code implementing complex network protocols such as TLS~\cite{georgiev01}, which may even be mandated by
+national standards in some devices such as smart electricity meters.
+
+\subsection{Oscillations in the electrical grid}
+
+Common to the attacks on the electrical grid proposed in the papers discussed above is their approach of overloading
+parts of the grid. However, scenarios have been proposed that go beyond a simple overload condition, and in which an
+attacker exploits the physcial characteristics of the grid to cause oscillations of increasing amplitude, ultimately
+triggering a cascade of protection mechanisms. The purpose of this type of attack is to use a small controllable load to
+cause outsized damage.
+
+Electro-mechanical oscillation modes between different geographical areas of an electrical grid are a well-known
+phenomenon. In their book~\cite{rogers01}, Rogers and Graham provide an in-depth analysis of these oscillations and
+their mitigation. In~\cite{grebe01}, Grebe, Kabouris, López Barba et al.\ analyzed modeskj inherent to the
+continental european grid. A report on an event where an oscillation on one such mode caused a problem can be found in
+\cite{entsoe01}.
+
+In~\cite{zlmz+21}, Zou, Liu, Ma et al.\ analyzed the possibility of a modal attack in which electric vehicle chargers
+rapidly modulate their power to force an oscillation of a poorly dampened wide-area electromechanical mode. Using
+mathematical analysis, small-scale simulations and practical experiments they validated the attack scenario and
+developed a countermeasure that can be implemented as part of generator control systems and that when activated can
+suppress forced oscillations of wide-area electromechanical modes. 
+
+On the device side of the smart grid, research has concentrated on smart meter security. Smart meters are
+architecturally similar to IoT devices~\cite{zheng01,ifixit01}, but come with different challenges. Similar to a
+high-power IoT device, an attacker could use an off-switch built as part of an attack, a scenario that was investigated
+by Anderson and Fuloria in~\cite{anderson01}. Unique to smart meters, an attacker could, however, also use their control
+to manipulate the meter's energy accounting, quickly leading to potentially severe financial impact on the meter's
+operating utility company. This scenario has received research attention~\cite{anderson02,mcdaniel01} and this is where
+industry incentives are the strongest.
+
+Smart electricity meters are consumer devices built down to a price and manufacturers' firmware security R\&D budgets
 are limited by the high degree of market fragmentation that is caused by mutually incompatible national smart metering
 standards. Landis+Gyr, a large utility meter manufacturer, 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}, which indicates tension between firmware security and the manufacturers's bottom
 line. 
 
+\subsection{Proposed Countermeasures}
+
 % FIXME more sources!
 
-\subsection{The state of the art in embedded security}
-
-Embedded software security has proven challenging compared to the security of larger computer systems. On one hand,
-embedded devices usually run highly customized firmware that is rarely updated. On the other hand, embedded devices
-often lack security mechanisms such as memory management units that are found in higher-power devices. As a result of
-these factors, even well-funded companies continue to have trouble securing their embedded systems. An example of this
-difficulty is the 2019 flaw in Apple's iPhone SoC first-stage ROM bootloader that allows for the full compromise of any
-iPhone older than iPhone X given physical access to the device~\cite{heise01}. iPhone 8, one of the affected models, was
-still being manufactured and sold by Apple until April 2020.  In another instance in 2016, researchers found multiple
-flaws in Samsung's implementation of ARM TrustZone ``secure world'' firmware that Samsung used for their own mobile
-phone SoCs.  The flaws they found were both architectural flaws such as secret user input being passed through untrusted
-userspace processes as well as cryptographic flaws such as
-CVE-2016-1919\footnote{\url{http://cve.circl.lu/cve/CVE-2016-1919}}~\cite{kanonov01}.  In a similar way, in 2014,
-researchers found an integer overflow flaw in the low-level code handling untrusted input in Qualcomm's QSEE
-firmware\footnote{For an overview of ARM TrustZone including a survey of academic work and past
-security vulnerabilities of TrustZone-based firmware see~\cite{pinto01}.}~\cite{rosenberg01}.
-
-If even companies with R\&D budgets that rival some countries' national budgets at mass-market consumer devices
-have trouble securing their mass market secure embedded software stacks, what is a much smaller smart meter manufacturer
-to do?  Especially if national standards mandate complex protocols such as TLS that are difficult to implement
-correctly~\cite{georgiev01}, this manufacturer will be short on options to secure their product.
-
-\subsection{Attack surface in the smart grid}
-
-From the incidents we outlined in the previous paragraphs we conclude that in smart metering technology, market
-incentives do not currently provide the conditions for a level of device security that will reliably last for decades
-after deployment. Considering this tension, in this paragraph we examine the cyberphysical risks that arise from attacks
-on the smart grid in the first place. These risks arise at three different infrastructure levels.
-
-The first level is that of attacks on centralized control systems. This type of attack is often cited in popular
-discourse and to our knowledge is the only type of attack against an electric grid that has ever been carried out in
-practice at scale~\cite{lee01}. Despite their severity, these attacks do not pose a strictly \emph{scientific} challenge
-since they are generic to any industrial control system. Their causes and countermeasures are generally well-understood
-and the hardest challenge in their prevention is likely to be budgetary constraints.
-
-Beyond the centralized control systems, the next target for an attacker may be the communication links between those
-control systems and other smart grid components. While in some countries such as Italy special-purpose systems such as
-PLC are common~\cite{ec03}, overall, IP-based technologies have proliferated according to the larger trend towards
-IP-based communications.  This proliferation of IP links brings along the possibility for the application of generic
-network security measures from the IP world to the smart grid domain. In this way, a standardized, IP-based protocol
-stack unlocks decades of network security improvements at little cost.
-
-Beyond these layers towards the core of the smart grid's control infrastructure, an attacker might also corrupt the
-network from the edges and target the endpoint devices itself. The large scale deployment of networked smart meters
-creates an environment that is favorable to such attacks.
-% FIXME cite RECESSIM landis+gyr protocol hacking wiki/youtube
-
-\subsection{Cyberphysical threats in the smart grid}
-
-Assuming that an attacker has compromised devices on any of these levels of smart grid infrastructure, what could they
-do with their newly gained power? The obvious action would be to switch off everything. Of all scenarios,
-this is both the most likely in practice---it is exactly what happened in the Russian cyberattacks on the Ukranian
-grid~\cite{lee01}---but it is also the easiest to mitigate since the vulnerable components are few and centralized.
-Mitigations include the installation of fail safes as well as a defense in depth approach to hardening the grid's
-cyber infrastructure.
-
-Another possible action for an attacker would be to forge energy measurements in an attempt to cause financial mayhem.
-Both individual consumers as well as the utility could be targeted by such an attack. While such an attack might have
-localized success, larger-scale discrepancies will likely quickly be caught by monitoring systems. For example, if a
-large number of meters in an area systematically under- or over-reported their energy readings, meter readings across
-the affected area would no longer add up with those of monitoring devices in other locations in the transmission and
-distribution grid.
-
-In some countries, smart meter functionality goes beyond mere monitoring devices and also includes remotely controlled
-switches. There are two types of these switches: Switches to support \emph{Demand-Side Management} (DMS) and cut
-off-switches that are used to punish defaulting customers. Demand Side Management is when a grid operator can remotely
-control the timing of large, non-time-critical loads on the customer's premises~\cite{dzung01}. A typical example of this
-is a customer using an electric water heater: The heater is outfitted with a large hot water storage tank and is
-connected hooked up to the utility's DSM system. The customer does not care when exactly their water is heated as long
-as there is enough of it, and the utility offers them cheaper rates for the electricity used for heating in exchange for
-control over its precise timing.  The utility uses this control to even out peaks in the consumption/production
-imbalance, remotely enabling DSM systems during off-peak times and disabling them during peak hours.  In contrast to
-DSM, cut-off switches are switches placed in between the grid and the entire customer's household such that the utility
-can disconnect non-paying customers without incurring the expense of sending a technician to the customer's premises.
-Unlike DSM systems, cut-off switches are not opt-in~\cite{anderson01,temple01}.  An attack that uses cut-off switches
-would obviously immediately cause severe mayhem. Attacks on DSM may have more limited immediate impact as affected
-consumers may not notice an interruption for several hours.
-
-Instead of switching off loads outright, an attack employing DSM switches (and potentially also cut-off switches) could
-choose to target the grid's stability.  By synchronizing many compromised smart meters to switch on and off a large
-load capacity, an attacker might cause the entire electrical grid to oscillate~\cite{kosut01,wu01,kim01}. As a large
-system of coupled mechanical systems, the electrical grid exhibits a complex frequency-domain behavior.  Resonance
-effects, colloquially called ``modes'', are well-studied in power system
-engineering~\cite{rogers01,grebe01,entsoe01,crastan03}. As they can cause issues even under normal operating conditions,
-a large effort is invested in dampening these resonances. Howewer, fully eliminating them under changing load conditions
-may not be achievable.
-
-\subsection{Communication Channels on the Grid}
-
-A core part of intervening with any such cyberattack is the ability to communicate remediary actions to the devices
-under attack.  There is a number of well-established technologies for communication on or along power lines. We can
-distinguish three basic system categories: systems using separate wires (such as DSL over landline telephone wiring),
-wireless radio systems (such as LTE) and \emph{Power Line Communication} (PLC) systems that reuse the existing mains
-wiring and superimpose data transmissions onto the 50 Hz mains sine~\cite{gungor01,kabalci01}.
+\section{Grid Frequency as a Communication Channel}
 
 During a large-scale cyberattack, availability of internet and cellular connectivity cannot be relied upon. An attacker
 may already have disabled such systems in a separate attack, or they may go down along with parts of the electrical
-grid. Traditional powerline communication systems or an utitly's proprietary wireless systems would work, but at a range
-of no more than several tens of kilometers reaching all meters in a country would require a large upfront infrastructure
-investment.
-
-\section{Grid Frequency as a Communication Channel}
-
-We propose to approach the problem of broadcasting an emergency signal to all smart meters within a synchronous area by
-using grid frequency as a communication channel.  Despite the technological complexity of the grid, the physics
-underlying its response to changes in load and generation is surprisingly simple. Individual machines (loads and
-generators) can be approximated by a small number of differential equations and the entire grid can be modelled by
-aggregating these approximations into a large system of nonlinear differential equations. As a consequence, small signal
+grid. Powerline communication systems will likely be unaffected by an attack, but at a range of no more than several
+tens of kilometers, covering the entire grid would require a large upfront infrastructure investment for transmitters.
+
+We propose to approach the problem of broadcasting an emergency signal to all grid-connected devices such as smart
+meters or IoT appliances within a synchronous area by using grid frequency as a communication channel.  Despite the
+technological complexity of the grid, the physics underlying its response to changes in load and generation is
+surprisingly simple. Individual machines (loads and generators) can be approximated by a small number of differential
+equations describing their control systems' interaction with the machine's physics, and the entire grid can be modelled
+by aggregating these approximations into a large system of differential equations. As a consequence, small signal
 changes in generation/consumption power balance cause an approximately proportional change in
-frequency~\cite{kundur01,crastan03,entsoe02,entsoe04}.  This \emph{Power Frequency Charactersistic} is about
-\SI{25}{\giga\watt\per\hertz} for the continental European synchronous area according to European electricity grid
-authority ENTSO-E.
+frequency~\cite{kundur01,crastan03,entsoe02,entsoe04}. The slope of this first-order approximation is known as
+\emph{Power Frequency Charactersistic}, and in case of the continental European synchronous area happens to be about
+\SI{25}{\giga\watt\per\hertz}  according to the European electricity grid authority, ENTSO-E.
 
-If we modulate the power consumption of a large load such as a multi-megawatt aluminium smelter, this modulation will
-result in a small change in frequency according to this characteristic. As long as we stay within the operational limits
-set by ENTSO-E~\cite{entsoe02,entsoe03}, this change will not degrade the operation of other parts of the grid. The
-advantages of grid frequency modulation are the fact that a single transmitter can cover an entire synchronous area as
-well as low receiver hardware complexity.
+If we modulate the power consumption of a large load, this modulation will result in a small change in frequency
+according to this characteristic. As long as we stay within the operational limits set by
+ENTSO-E~\cite{entsoe02,entsoe03}, this change will not degrade the operation of other parts of the grid. The advantages
+of grid frequency modulation are the fact that a single transmitter can cover an entire synchronous area as well as low
+receiver hardware complexity.
 
 To the best of the authors' knowledge, grid frequency modulation has only ever been proposed as a communication channel
 at very small scales in microgrids before~\cite{urtasun01} and has not yet been considered for large-scale application.
 
-Compared to traditional channels such as DSL, LTE or LoraWAN, grid frequency as a communication channel has a large
-resiliency advantage: If there is power, a grid frequency modulation system is operational. Both DSL and LTE systems not
-only require power but also require large amounts of centralized infrastructure to operate. Mesh networks such as
+Compared to traditional channels such as DSL, LTE or LoraWAN, grid frequency as a communication channel has a resiliency
+advantage: If there is power, a grid frequency modulation system is operational. Both DSL and LTE systems not only
+require power at their base stations, but also require centralized infrastructure to operate. Mesh networks such as
 LoraWAN can cover short distances up to $\SI{20}{\kilo\meter}$ without requiring infrastructure to be available, but for
 longer distances LoraWAN relies on the public internet for its network backbone. Additionally, systems such as DSL, LTE
 and LoraWAN are built around a point-to-point communication model and usually do not support a generic broadcast
 primitive. During times when a large number of devices must be reached simultaneously this can lead to congestion of
-local cellular towers or gateways.
-Therefore, during an ongoing cyberattack, grid frequency is promising as a communication channel as only a single
-transmitter facility must be operational for it to function, and this single transmitter can reach all connected devices
-simultaneously. After a power outage, it can function as soon as electrical power is restored, even while the public
-internet and mobile networks are still offline and it is unaffected by cyberattacks that target telecommunication
-networks.
+cellular towers and servers. Therefore, during an ongoing cyberattack, grid frequency is promising as a communication
+channel because only a single transmitter facility must be operational for it to function, and this single transmitter
+can reach all connected devices simultaneously. After a power outage, it can resume operation as soon as electrical
+power is restored, even while the public internet and mobile networks are still offline. It is unaffected by
+cyberattacks that target telecommunication networks.
 
 \subsection{Characterizing Grid Frequency}
 \label{grid-freq-characterization}
 
-To collect ground truth measurements for our analysis of grid frequency as a communication channel, we developed a
-device to safely record mains voltage waveforms.  Our system consists of an \texttt{STM32F030F4P6} ARM Cortex M0
-microcontroller that records mains voltage using its internal 12-bit ADC and transmits measured values through a
-galvanically isolated USB/serial bridge to a host computer. We derive our system's sampling clock from a crystal oven to
-avoid frequency measurement noise due to thermal drift of a regular crystal: \SI{1}{ppm} of crystal drift would cause a
-grid frequency error of $\SI{50}{\micro\hertz}$. We compared our oven-stabilized clock against a GPS 1 pps reference and
-found that over a time span of 20 minutes both stayed stable within 5 ppb of each other, which corresponds to the drift
-specification of a typical crystal oven.
-
-In utility SCADA systems, Phasor Measurement Units (PMUs, also called \emph{synchrophasors}) are used to precisely
-measure grid frequency among other parameters.  Details on the inner workings of commercial phasor measurement units are
-scarce but there is a large amount of academic research on measurement.  PMUs employ complex signal analysis algorithms
-to provide fast and precise measurements even when given a heavily distorted input
-signal~\cite{narduzzi01,derviskadic01,belega01}.
+Before analyzing grid frequency as a communication channel, we developed a device that allows us to collect ground truth
+for our analysis by safely recording the grid voltage waveform.  Our system consists of an \texttt{STM32F030F4P6} ARM
+Cortex M0 microcontroller that records mains voltage using its internal 12-bit ADC and transmits measured values through
+a galvanically isolated USB/serial bridge to a host computer. We derive our system's sampling clock from a crystal oven
+to avoid frequency measurement noise due to thermal drift of a regular crystal: \SI{1}{ppm} of crystal drift would cause
+a grid frequency error of $\SI{50}{\micro\hertz}$. We compared our oven-stabilized clock against a GPS 1 pps reference
+and found that over a time span of 20 minutes both stayed stable within 5 ppb of each other, which corresponds to the
+drift specification of a typical crystal oven.
+
+In utility SCADA systems, Phasor Measurement Units (PMUs) are used to precisely measure grid frequency among other
+parameters.  Details on the inner workings of commercial phasor measurement units are scarce but there is a large amount
+of academic research on their measurement algorithms.  PMUs employ complex signal analysis algorithms to provide fast
+and precise measurements even when given a heavily distorted input signal~\cite{narduzzi01,derviskadic01,belega01}.
 
 In our application, we do not need the same level of precision. For the sake of simplicity, we use the universal
 frequency estimation approach of Gasior and Gonzalez~\cite{gasior01}. In this algorithm, the windowed input signal is
@@ -425,7 +441,8 @@ self-regulating effect of loads. %FIXME citation Above a $\SI{10}{\second}$ peri
 thus the $1/f$ noise we observe is the result of the interaction between primary control and consumer demand. On top of
 this $1/f$ behavior, the spectrum shows several sharp peaks at time intervals with a ``round'' number such as
 $\SI{10}{\second}$, $\SI{60}{\second}$ or multiples of $\SI{300}{\second}$. These peaks are due to loads turning on- or
-off depending on wall-clock time. Besides the narrow peaks caused by this effect we can also observe two wider bumps at
+off depending on wall-clock time, and demand forecasting not being able to precisely match the amplitude of these large
+changes in load. Besides the narrow peaks caused by this effect we can also observe two wider bumps at
 $\SI{7.0}{\second}$ and $\SI{4.7}{\second}$. These bumps closely correlate with continental european synchonous area's
 oscillation modes at $\SI{0.15}{\hertz}$ (east-west) and $\SI{0.25}{\hertz}$ (north-south)~\cite{grebe01}.
 
@@ -439,27 +456,27 @@ by repurposing a large industrial load as a transmitter. Going through a
 list of energy-intensive industries in Europe~\cite{ec01}, we found that an aluminium smelter would be a good candidate.
 In aluminium smelting, aluminium is electrolytically extracted from alumina solution. High-voltage mains power is
 transformed, rectified and fed into about 100 series-connected electrolytic cells forming a \emph{potline}. Inside these
-pots alumina is dissolved in molten cryolite electrolyte at about \SI{1000}{\degreeCelsius} and electrolysis is
+pots, alumina is dissolved in molten cryolite electrolyte at about \SI{1000}{\degreeCelsius} and electrolysis is
 performed using a current of tens or hundreds of Kiloampère. The resulting pure aluminium settles at the bottom of the
 cell and is tapped off for further processing.
 
 Aluminium smelters are operated around the clock, and due to the high financial stakes their behavior under power
-outages has been carefully characterized. Power outages of tens of minutes up to two hours reportedly do
-not cause problems in aluminium potlines~\cite{eisma01,oye01}. Recently, even techniques for intentional power modulation
-without affecting cell lifetime or product quality have been developed to take advantage of variable energy
+outages has been carefully characterized. Power outages of tens of minutes up to two hours reportedly do not cause
+problems in aluminium potlines~\cite{eisma01,oye01}. Recently, even techniques for intentional power modulation without
+affecting cell lifetime or product quality have been developed to take advantage of variable energy
 prices~\cite{duessel01,eisma01,depree01}.  An aluminium plant's power supply is controlled to constantly keep all
-smelter cells under optimal operating conditions. Modern power supply systems employ large banks of diodes or thyristors to
-rectify low-voltage AC to DC to be fed into the potline~\cite{ayoub01}. Potline voltage is controlled through a
+smelter cells under optimal operating conditions. Modern power supply systems employ large banks of diodes or thyristors
+to rectify low-voltage AC to DC to be fed into the potline~\cite{ayoub01}. Potline voltage is controlled through a
 combination of a tap changer and a transductor.  Individual cell voltages are controlled by changing the physical
 distance between anode and cathode distance.  In this setup, power can be electronically modulated using the thyristor
 rectifier. Since the system does not have any mechanical inertia, high modulation rates are possible.
 
 In~\cite{depree01}, the authors describe a setup where a large Aluminium smelter in continental Europe is used as
-primary control reserve for frequency \emph{regulation}. In this setup, a rise time of $\SI{15}{\second}$ was achieved
-to meet the $\SI{30}{\second}$ requirement posed by local standards for primary control. In their conclusion, the
-authors note that for their system, an energy storage capacity of $\SI{7.7}{\giga\watt\hour}$ is possible if all plants
-of a single operator are used. Given the maximum modulation depth of $\SI{100}{\percent}$ for up to one hour that is
-mentioned by the authors, this results in an effective modulation power of $\SI{7.7}{\giga\watt}$. Over a longer
+primary control reserve for frequency regulation. In this setup, a rise time of $\SI{15}{\second}$ was achieved to meet
+the $\SI{30}{\second}$ requirement posed by local standards for primary control. In their conclusion, the authors note
+that for their system, an effective thermal energy storage capacity of $\SI{7.7}{\giga\watt\hour}$ is possible if all
+plants of a single operator are used. Given the maximum modulation depth of $\SI{100}{\percent}$ for up to one hour that
+is mentioned by the authors, this results in an effective modulation power of $\SI{7.7}{\giga\watt}$. Over a longer
 timespan of $\SI{48}{\hour}$, they have demonstrated a $\SI{33}{\percent}$ modulation depth which would correspond to a
 modulation power of $\SI{2.5}{\giga\watt}$. We conclude that a modulation of part of an aluminium smelter's power
 consumption is possible at no significant production impact and at low infrastructure cost. Aluminium smelters are
@@ -483,11 +500,17 @@ spread-spectrum technique. By spreading signal energy throughout a wide band, bo
 minimized and the risk of mode excitation is reduced since spread-spectrum techniques minimize energy in any particular
 sub-band.
 
-In this paper, we chose to perform simulations using Direct Sequence Spread Spectrum for its simple implementation and
-good overall performance. DSSS chip timing should be as fast as the transmitter's physics allow to exploit the low-noise
+The spread-spectrum technique that we chose is Direct Sequence Spread Spectrum for its simple implementation and good
+overall performance. DSSS chip timing should be as fast as the transmitter's physics allow to exploit the low-noise
 region between $\SI{0.2}{\hertz}$ to $\SI{2.0}{\hertz}$ in Figure~\ref{fig_freq_spec}. Going past
 $\approx\SI{2}{\hertz}$ would complicate frequency measurement at the receiver side.
 
+\paragraph{Direct Sequence Spread Spectrum (DSSS) modulation}
+
+% FIXME quickly explain DSSS here. 
+
+\paragraph{DSSS parametrization}
+
 We simulated a proof-of-concept modulator and demodulator using data captured from our grid frequency sensor. Our
 simulations covered a range of parameters in modulation amplitude, DSSS sequence bit depth, chip duration and detection
 threshold. Figure~\ref{fig_ser_nbits} shows our simulation results for symbol error rate (SER) as a function of
@@ -509,7 +532,8 @@ from $\SI{0.2}{\hertz}$ to $\SI{2}{\hertz}$.
 
 \begin{figure}
     \centering
-    \hspace*{-1cm}\includegraphics[width=0.5\textwidth]{../notebooks/fig_out/dsss_thf_amplitude_5678}
+    \hspace*{-5mm}\includegraphics[width=0.5\textwidth]{../notebooks/fig_out/dsss_thf_amplitude_5678}
+    \vspace*{-5mm}
     \caption{SER vs.\ Amplitude and detection threshold. Detection threshold is set as a factor of background noise
     level.}
     \label{fig_ser_thf}
@@ -517,8 +541,8 @@ from $\SI{0.2}{\hertz}$ to $\SI{2}{\hertz}$.
 
 \begin{figure}
     \centering
-    \hspace*{-1cm}\includegraphics[width=0.5\textwidth]{../notebooks/fig_out/chip_duration_sensitivity_6}
-    \vspace*{-1cm}
+    \hspace*{-5mm}\includegraphics[width=0.5\textwidth]{../notebooks/fig_out/chip_duration_sensitivity_6}
+    \vspace*{-5mm}
     \caption{SER vs.\ DSSS chip duration.}
     \label{fig_ser_chip}
 \end{figure}
@@ -664,6 +688,7 @@ Source code and EDA designs are available at the public repository listed at the
 \bibliography{\jobname}
 
 \center{
+    \footnotesize
     \center{This is version \texttt{\input{version.tex}\unskip} of this paper, generated on \today. The git repository
     can be found at:}