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The growing connectivity of industrial devices as a result of the Internet of Things is increasing the risks to Industrial Control Systems. Since attacks on such devices can also cause damage to people and machines, they must be properly secured. Therefore, a threat analysis is required in order to identify weaknesses and thus mitigate the risk. In this paper, we present a systematic and holistic procedure for analyzing the attack surface and threats of Industrial Internet of Things devices. Our approach is to consider all components including hardware, software and data, assets, threats and attacks throughout the entire product life cycle.
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Analyzing the Attack Surface and Threats of
Industrial Internet of Things Devices
Simon Liebl∗† , Leah Lathrop, Ulrich Raithel, Andreas Aßmuth, Ian Ferguson, and Matthias S¨
Technical University of Applied Sciences OTH Amberg-Weiden, Amberg, Germany
E-mail: {s.liebl |l.lathrop |a.assmuth |m.soellner}
Abertay University, Dundee, UK
SIPOS Aktorik GmbH, Altdorf, Germany
Abstract—The growing connectivity of industrial devices as a
result of the Internet of Things is increasing the risks to Industrial
Control Systems. Since attacks on such devices can also cause
damage to people and machines, they must be properly secured.
Therefore, a threat analysis is required in order to identify
weaknesses and thus mitigate the risk. In this paper, we present
a systematic and holistic procedure for analyzing the attack
surface and threats of Industrial Internet of Things devices.
Our approach is to consider all components including hardware,
software and data, assets, threats and attacks throughout the
entire product life cycle.
KeywordsThreat analysis; attack surface; Industrial Internet
of Things; cyber-physical systems; cloud.
The Internet of Things (IoT) is increasingly making its way
into our daily lives. Smart devices are omnipresent, in smart
homes, medical and infrastructure applications, and building
automation. Industrial applications, for example, in manufac-
turing, the automotive and oil and gas industry, are other major
fields of application, summarized as the Industrial Internet of
Things (IIoT). The hype around the IoT and IIoT led, for
example, to dozens of different platforms and, consequently,
to compatibility problems. The race for the shortest time to
market also led to security and privacy issues, as these topics
have been neglected or even omitted entirely so far. To address
the latter issues, our work in [1] and this extension aim to
support IIoT device manufacturers and operators in identifying
threats against their devices.
According to [2], about every third Industrial Control Sys-
tem (ICS) computer was attacked within the second half of
2020. The situation was also exacerbated by the COVID-
19 pandemic, as increasing Remote Desktop Protocol (RDP)
connections also led to a rise in brute force attacks on them.
A rise in network-capable Operational Technology (OT) com-
ponents has been observable for years anyway [3]. The main
threat arises from ransomware, i.e., malware that encrypts files
and demands ransom, and coinminers, i.e., malware used to
mine cryptocurrencies [4]. The attack on Colonial Pipeline
[5] showed once again that larger parts of the population can
also be affected by such attacks. In addition, the attack on
a U.S. water treatment facility in early 2021 [6] highlighted
that ICSs in critical infrastructures are particularly at risk of
targeted attacks.
As a consequence of the increasing threats, IIoT manufac-
turers must secure their devices to prevent such incidents.
However, implementing best practices around default pass-
words is not enough for properly secured devices. Manufac-
turers, and also operators, must therefore be fully aware of
threats and the resulting risks. The purpose of this paper is
to support them in their threat analysis. Our goal is a holistic
view of the attack surface of IIoT devices. To achieve this, all
components including hardware, software, and data, assets, as
well as threats and attacks should be considered throughout
the entire life cycle of the device. Our research methodology
can be described as follows: the first two steps are analyses of
the components and assets of an IIoT device, followed by a
threat and an attack categorization. By considering all assets
in the threat categorization, a complete list should be enabled.
Similarly, the component analysis should provide a complete
attack and weakness categorization.
The remainder of this paper is structured as follows: in
Section II, the different terms around the IIoT are clarified.
Section III presents related work from other researchers as
well as organizations. In Section IV, the components of an
IIoT device are decomposed into hardware, software, and data,
followed by an asset analysis in Section V. In Section VI, a
threat categorization is presented that consists of nearly 50
categories organized into 10 groups. A similar categorization
of attacks and weaknesses throughout the life cycle of an IIoT
device is introduced in Section VII. Section VIII recommends
six steps for a systematic threat analysis of IIoT devices. The
paper ends with conclusions in Section IX.
This section briefly explains the terms IoT, IIoT, Cyber-
Physical System (CPS), ICS, Information Technology (IT),
and OT and how they are related (see Figure 1).
The IoT is a network of connected devices, which are
sensors and/or actuators fulfilling a specific function. The
infrastructure enables communication with other equipment
along with the storage, processing, and distribution of data to
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Figure 1. Relationship of the various terms, adopted from [8].
other systems and users [7]. Cloud services realize universal
accessible utility services, e.g., for device management, and
centralized data processing for analytics, which may be sup-
ported by Artificial Intelligence (AI). The gained knowledge
can be made available to users, other systems and the devices
themselves. Use cases span many domains, such as consumer
applications (e.g., smart home), commercial (e.g., medical
and healthcare, transportation), and infrastructure applications
(e.g., smart grid).
The IIoT is a part of the IoT, but differentiates in some
aspects. The IIoT integrates the previously separated areas of
IT and OT by connecting OT components, such as machines
and control systems, with IT systems and business processes
[8]. The integration is accomplished, for instance, through
edge devices and gateways that enable processing in the cloud.
The leading use cases of the IIoT are operational intelligence,
asset monitoring, and predictive maintenance [9]. The goals
are, among others, to increase productivity, improve safety,
gain flexibility and agility, and reduce energy consumption. It
should be noted that use cases, services, and communication in
the IIoT are machine-oriented, while these are human-centered
in many IoT applications, such as in consumer IoT [8].
The OT components listed above are usually installed within
an ICS, which is structured into several layers. In the Purdue
reference model, level 0 describes the physical process, which
is sensed and manipulated by sensors and actuators and
controlled by, for example, Programmable Logic Controllers
(PLCs) in level 1. Level 2 includes Human-Machine Interfaces
(HMIs) and the Supervisory Control And Data Acquisition
(SCADA) system to provide operators with aggregated infor-
mation. Layers above contain backup servers and Enterprise
Resource Planning (ERP) systems, among others.
The last term that needs to be clarified is CPS. These can be
found in the IoT/IIoT and in an ICS. Their primary task is the
control of a physical process in the real world using sensors
and actuators. Furthermore, they are equipped with network
capability. Another characteristic is that they require real-time
interaction with the physical world [10].
Different objectives and requirements emerge from the
characteristics of CPSs and ICSs. Besides integrity and avail-
ability, the security goals for IoT devices are centered on
confidentiality and privacy, as personal data, such as health
data, is processed. IIoT devices, in contrast, focus additionally
on safety and the impact on the environment and society
[11]. In industrial plants, humans work with heavy machinery
in a confined workspace. An accident can potentially cause
injury, death, damaged production equipment or environmental
disasters. The impact of an IIoT failure may be worse in
critical infrastructures, such as energy and water supply, food,
and health, as large parts of the population may be affected.
To sum up, the IIoT allows the processing of data, e.g.,
produced by CPSs in an ICS, in the cloud. This is realized
by connected edge devices, gateways, and OT components.
The control of physical processes leads to further requirements
such as safety. The increased connectivity and the high re-
quirements result in additional threats and increased risk to
IIoT devices, which is further discussed in Section VII.
The need for action in the area of IoT security was rec-
ognized by experts a long time ago [12]. In recent years,
this has also been identified by government institutions and
industry. Nevertheless, manufacturers of embedded systems
still struggle to integrate security features or even to work
according to the principle of security by design.
Varga et al. [13] discuss IoT threats in the automation
domain. Based on an IoT architecture consisting of the four
layers sensors and actuators, networking, data processing,
and application, the authors present different threats and the
required countermeasures. In [14], Atamli et al. describe a
threat-based security analysis that focuses on the three use
cases power management, smart car, and smart healthcare
system. Initially, they discuss sources of threats and classify
attack vectors. Afterwards, the security and privacy impact
of attacks in the area of the listed use cases is described.
Last, desirable security and privacy properties are defined.
Abomhara et al. [15] provide background information on
IoT devices and services, threats, attacks, and security and
privacy goals. Subsequently, the motivation of attacks and
a classification of possible intruders are presented. In [16],
Wurm et al. conducted security analyses on a consumer IoT
and an IIoT device and demonstrated how these devices could
be exploited.
Dozens of organizations and government institutions, such
as the Industrial Internet Consortium (IIC), the Cloud Security
Alliance (CSA), and the US National Institute of Standards
and Technology (NIST), have published guidelines and best
practices for IoT security. Particularly noteworthy is [17],
which brought together about 100 documents from 50 different
organizations, resulting in 13 points for recommended action.
Furthermore, the contributions of the European Union Agency
for Cybersecurity (ENISA) and the German Federal Office
for Information Security (BSI) are recommended. The former
have published several analyses and recommendations for the
various areas of the IoT [18]. For example, the baseline
security recommendations for IoT [19] provides a threat tax-
onomy, attack scenarios and a list of security measures. The
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BSI annually publishes an Information Security Management
System (ISMS), the so-called IT-Grundschutz Compendium,
which also considers ICS components, embedded systems,
and IoT devices, among others. In addition to organizational
aspects, technical issues are also addressed, including a list of
threats and necessary security requirements.
There are numerous papers and guidelines describing the
threats to IoT devices. However, the threats are often described
only in general terms and in a jumbled manner. For example,
the aforementioned threat taxonomy by ENISA lists 25 threats,
but attack techniques (e.g., replay of messages), weaknesses
(e.g., software vulnerabilities), and threat consequences (e.g.,
sensitive information leaking) are considered without further
distinction. Systematic and holistic approaches are needed to
enable the identification of all attack vectors of IIoT devices
by their manufacturers. It is this shortcoming that we wish to
address in this work.
To enable the full analysis of attack surfaces, a breakdown
of the components of a typical device is presented in the fol-
lowing section. The components can be grouped into the three
categories hardware, software, and data, which are described
in the following subsections in detail. Figure 2 presents an
overview of the components an IIoT device may contain.
Services / API
Firmware / OS/RTOSCircuit Board
Security Chip
Sensor / Actuator
Configuration Data
Application Data
Access Data / Keys
Log Data
Figure 2. Hardware and software components and types of data that IIoT
devices may contain.
A. Hardware
The first component that comes to mind is the enclosure.
It must be suitable for the environment and may have to
be explosion-, water-, and dust-proof. Appearance, size, and
usability are particularly important in the consumer sector,
but the industrial field also appreciates these properties. Plant
operators, for example, prefer simple and space-saving in-
stallation in the switching cabinet and quick familiarization
with handling by employees. In critical applications, tamper
protection is used to detect modifications to the device.
A central component of the interior are Printed Circuit
Boards (PCBs). They are often the bridge between the various
mechanical and electrical parts of a device and also connect the
countless electrical components on a PCB such as controllers,
Integrated Circuits (ICs), oscillators, fuses, and basic electrical
The core components on a PCB are processors. Several
types are available, each with its own benefits and drawbacks.
Among others, there are microprocessors, microcontrollers,
Application-Specific Integrated Circuits (ASICs), and Field-
Programmable Gate Arrays (FPGAs). Microprocessors ship
in a single IC, which vice versa may contain multiple mi-
croprocessors in case of a multi-core design. They can be
made for general-purpose or specialized on a specific task,
e.g., signal, graphics, and physics processing. In addition to
the microprocessor, dozens of peripherals are integrated into
microcontroller ICs, such as memory, analog and digital inputs
and outputs, serial communication interfaces, a Real-Time
Clock (RTC) and in-circuit debug support. Increasingly, secu-
rity features such as a Trusted Execution Environment (TEE), a
True Random Number Generator (TRNG), a cryptography ac-
celerator, and a Physical Unclonable Function (PUF) are also
being embedded. ASICs are customized for a certain task and
their advantages include, for instance, greater performance and
optimized size. Unlike ASICs, FPGAs can be updated after
production and are more cost-effective, especially for smaller
quantities. IoT devices usually employ microcontrollers as
their main Central Processing Unit (CPU) because they are
feature-rich and are still low-priced and compact. The average
CPU has a single core and the clock speed is in the double-
digit MHz range, which drastically limits the performance
compared to desktop CPUs in IT systems.
Memory can be integrated into the microcontroller, placed
as separate IC on the PCB or connected by slots in the
enclosure. The main memory is typically Static Random Ac-
cess Memory (SRAM) or Dynamic Random Access Memory
(DRAM). There are different technologies for data storage, for
example, FLASH, Electrically Erasable Programmable Read-
Only Memory (EEPROM), and One-Time Programmable
(OTP) memory. Some security-focused microcontrollers in-
clude a on-the-fly encryption engine that enables secure stor-
age on external ICs. Removable storage technologies, such
as SD cards or USB sticks, are used, for example, to export
user data or import user applications. The total main memory
is usually in the kilobyte range, the data storage reaches the
megabyte range.
Like memory, a security chip can also be integrated into a
microcontroller or placed on the PCB. Security chip is used
as umbrella term for secure cryptoprocessors, which can be a
Trusted Platform Module (TPM) or a Secure Element (SE).
An exception are Hardware Security Modules (HSMs) that
are integrated via an external module. Their capabilities differ
in certain functions, although the common basic idea is to
outsource all cryptographic operations to a tamper-proof co-
Another substantial part of IIoT devices are input and
output components including sensors and actuators. The power
supply is also a type of input component, as it supplies the
device with power via a cable connection, battery, or solar
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panel, among others. User input interfaces may include simple
switches or utilize more advanced human input devices such
as keyboards or touchpads. According to Sikder et al. [20], the
various sensor can be categorized into environmental sensors
(e.g., audio, image, temperature and humidity sensor), position
sensors (e.g., inductive, ultrasonic, proximity, and magnetic
sensor) and motion sensors (e.g., flow sensor, gyroscope,
accelerometer); we extended this list with industrial sensors,
summarized as process sensors (e.g., current, pressure, and
chemical sensor). The output components can be grouped
in visual outputs (e.g., LEDs, display), audio outputs (e.g.,
loudspeaker), power outputs (e.g., relay, power electronics),
and actuators (e.g., electric and pneumatic actuator).
The last group of components in this list are connectivity
elements. This includes conductive paths on the PCB and wires
within the device. The numerous communication interfaces of
a system may require controllers, connectors, or antennas.
B. Software
The firmware is the linking component between hardware
and software of an embedded system, as it provides software
with low-level access to the hardware. Simple embedded
devices often have no underlying Operating System (OS);
therefore, they run only the firmware, which is known as
bare metal program. The most utilized OS in IoT devices
is Linux [21], followed by FreeRTOS, an open source Real-
Time OS (RTOS). In addition to real-time capability, some
CPSs in safety-critical applications may require the fulfillment
of further standards such as IEC 61508. Specialized OSs
have been developed to comply with these requirements, for
example, SAFERTOS achieved the highest Safety Integrity
Level (SIL) of IEC 61508 for a software-only component, i.e.,
SIL 3.
Although hardware-supported cryptography in the form of
cryptographic accelerators or security chips is more efficient
than software libraries, most IoT devices use mainly software-
based cryptography. The main reasons for this are that crypto-
hardware is hardly available and software libraries often do not
exploit available hardware for portability reasons [22]. There is
a wide range of fee-based as well as cost-free crypto libraries
available. However, there are some challenges that complicate
their use. First, only a few of them can easily be adapted to
systems without an OS, which is about every fourth IoT device
[21]. Second, the required storage space exceeds the frequently
limited memory. Finally, the execution of cryptographic algo-
rithms on low-power devices is highly time-consuming and,
therefore, compromises other requirements such as real-time
capability and usability. These challenges are addressed in the
research field lightweight cryptography in order to provide
efficient and storage-saving cryptographic software libraries
on all devices.
Connectivity is one of the major topics addressed during
the design of an IoT device as it defines how a device
interacts with other systems as well as users. A wide range
of communication protocol stacks are used for the various
applications. There are stacks for local communication (e.g.,
USB), Internet communication (e.g., TCP/IP), and automation
processes (e.g., Modbus). Currently, there is a trend towards
Ethernet-based automation protocols to take advantage of syn-
ergy. IIoT devices usually implement several protocol stacks
for compatibility reasons. This results in devices supporting
legacy protocols such as PROFIBUS and HART as well as
more recent ones such as PROFINET, OPC UA, MQTT, and
Smart services unlock the value of the IoT. Countless de-
vices can thus connect with each other and create an elaborated
decision. At the same time, they can be centrally monitored
and controlled. One central service is device management,
which includes device registration, organization, inspection,
and software and firmware updates. This may be accompanied
by several other services, for instance, monitoring, logging,
and attestation services. Frequently, a local service is also
required for configuration and control that is usually only
available in the local network. Therefore, many devices im-
plement an embedded web server or a mobile app Application
Programming Interface (API) for this purpose.
Last, every device runs its own specific application. De-
pending on the use case, this might be providing sensor
values, controlling an actuator, or bundling messages from
multiple smart sensors. Edge devices may also perform data
pre-processing or minor analytics. Additionally, some devices
allow users to execute their own programs and code.
C. Data
IIoT devices hold various types of data. First of all, any code
including the firmware and application can be considered as
data. Code is preferably stored in a FLASH memory or, if it
is not available, in an EEPROM.
During device setup, many options need to be configured.
Configuration data can include network settings, environment
and calibration parameter, sensor and actuator settings, and
machine learning parameters. It is stored in an EEPROM and
optionally also stored in the cloud as backup.
Application data is specific to the device. This can be
collected input data, such as sensor values, and produced
output data, such as analysis results.
Devices that implement basic security controls require au-
thentication data and cryptographic keys. Local access to the
device via display, browser, or mobile app should only be
allowed after entering valid credentials. The user database
is often stored locally and contains the names of the re-
spective users with passwords or the derived hash values.
Cryptographic keys are, for example, required to securely
communicate with any other entity such as a cloud service.
Not only secret keys are stored, but also public keys, for
instance the manufacturer’s public key, to be able to verify
firmware signatures. User databases need to be updatable and
are therefore stored in an EEPROM, whereas public keys may
be required to be tamper-proof and consequently stored, for
example, in an OTP memory.
IoT devices log relevant events for system diagnosis. These
include application-specific events as well as security-relevant
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Intellectual Property
Liability & Reputation
Physical Property
Device / Environment
Virtual Property
Generated Data
Code / Configuration
Information & Privacy
Behavioral Data
Health Data
Audio / Video Data
Functionality & Safety
Logical Operations
Physical Operations
Authentication Data
Cryptographic Keys
Figure 3. Assets from the perspective of manufacturers, owners, and users
as well as their common objectives.
events such as login attempts, for example. Unfortunately, in
practice, some devices also log highly sensitive data such as
This section emphasizes the assets of an IIoT device based
on the previously gathered components. We identified three
major stakeholders in a device: manufacturers, owners, and
users. By putting oneself in the shoes of the particular group,
the interests and assets worth protecting can be identified.
This, in turn, contributes to the understanding of what attackers
might be targeting. Figure 3 summarizes the assets for each
stakeholder as well as common ones highlighted in the arrow.
First, the assets of each group starting with manufacturers
are considered and then the common ones. A primary asset of
manufacturers is their Intellectual Property (IP), which can be
further distinguished into the domains hardware, software, and
process. Hardware IP includes, for instance, the design of an
ASIC or PCB and mechanical components. Manufacturers put
a lot of effort in optimizing the hardware design for energy
usage, heat flow, and size. Consequently, hardware is one of
the assets worth protecting in order to have an advantage over
competitors. The same applies to software IP. Developing the
firmware, inventing application-specific algorithms, or design-
ing machine learning models is time-consuming. Therefore,
manufacturers want to protect their software against copying,
theft, or publication. Process IP means the overall concept or
a unique solution for a specific problem that might be worth
protecting. Two further assets are liability and reputation.
Manufacturers have a certain responsibility to ensure that
no unexpected incidents, such as physical injury or property
damage, occur. Any incidents could also have an impact on
their company’s image.
Next, the assets are considered from the owner’s perspective,
the person or organization that purchased the device. First
of all, owners want to protect their physical property, which
is the newly acquired device and also all other belongings.
Second, the owner has virtual property. This includes, for
instance, code for custom applications and (configuration) data
stored on the device. Additionally, devices produce data during
operation. This includes collected sensor values and also meta
information such as the availability of a device. Furthermore,
owners want to prevent or detect tampering with the device
by users.
Users foremost interest when using the device is their health.
It must neither be endangered by one-time events nor by long-
term use of the device. Users also want to protect their data
on the device, which they actively stored on it or which was
generated during the use of it. Especially IoT devices with
dozens of sensors might collect audio and video, health, and
behavioral data that could compromise the privacy of users.
Last, the assets and objectives that all stakeholders have
in common. Functionality and safety are important attributes
of an IoT device. This includes logical operations, such as
successful firmware updates or changing of settings, and
physical operations, such as moving an actuator or cutting off
electricity if a human is present. If these requirements are not
achieved, this can have consequences for the manufacturer’s
reputation, destroy production facilities and cause downtime,
or injure users.
In this section, the threats to IIoT devices are analyzed. The
ENISA defines a threat as “any circumstance or event with the
potential to adversely impact an asset through unauthorized
access, destruction, disclosure, modification of data, and/or
denial of service” [23]. In order to better understand and assess
threats, we categorized and grouped them according to their
impact. The collected threat categories were identified based
on the preceding asset analysis and are presented in Figure 4.
The various threat groups are briefly outlined below.
A. Nefarious Activity / Abuse / Misuse
The first group summarizes rather generic threats from
nefarious activity, abuse, and misuse. Abuse of personal data
is the use of personal information in a manner for which it
was not intended, for example, a company selling ones email
address to advertisers. Another threat is tampering with data
or also poisoning of data, which can occur in the context of
machine learning. Any threat that compromises availability
is summarized as denial of service. The impact of these
threats is significant in ICSs because they might lead to a
production stop and, thus, cause financial loss or even worse
in critical infrastructures. Information disclosure and leakage
is sharing of data that is intended to be confidential. Misuse
of computing or electrical power can be caused by malware or
the inappropriate use of the device by employees. Computing
power can be misused by botnets that launch Distributed
Denial of Service (DDoS) attacks, mine cryptocurrencies, or
spread spam. Employees might use the device to charge their
phone, affecting the functionality of the device. Privilege abuse
is a threat that arises from employees who maliciously use
their privileges. About two out of three incident are financially
motivated; further reasons are fun, grudge and espionage [24].
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Figure 4. An overview of the grouped threat categories.
The last category of this group is repudiation of actions. In
case of an incident, investigators try to reconstruct the exact
procedure, for example, by analyzing log files. Attackers could
manipulate or delete them in order to remain undetected.
However, many IIoT devices do not uniquely identify users
so far, which makes it easier for them to deny it.
B. Attack Preparation / Persistence
This group summarizes threats in which attackers gain
unauthorized access to the device or a resource to prepare
further attacks. A common technique is privilege escalation
to obtain either initial access or higher privileges. Afterwards,
adversaries might try to keep access across restarts, updates,
or factory resets.
C. Damage / Destruction / Harm / Loss
Damage can arise in many ways. Attackers can destroy
hardware, software, or data of the device. For example,
ransomware can destroy data by encrypting it, software can
be erased by deleting it requiring a reinstallation (e.g., the
malware Brickerbot did it this way [25]), and hardware can
be destroyed by actuator malfunction, a short circuit, or
vandalism. Damage can also exceed device borders. Humans
can be injured by attacks compromising safety such as the
aforementioned example actuator malfunction and by con-
sequential damage from attacks on the power grid. Addi-
tionally, environmental damage can result, as demonstrated
by an attack with simple radio signals on Maroochy Water
Services that discharged 800,000 liters of sewage to local
parks and rivers [26]. Furthermore, financial loss can occur
to manufacturers through product piracy, to owners through
production downtime, and to users through theft of credit card
information. Manufacturers and operators also fear damage to
their company’s image. Many manufacturers do not disclose
vulnerabilities, operators publish incidents sketchily, and it is
also suspected that only a fraction is made public.
D. Espionage / Eavesdropping / Interception / Tampering
In ICSs, encrypted communication is still rather rare. Indus-
trial espionage by eavesdropping on communications is thus
simpler. It also facilitates tampering with data. Payload data
such as sensor values and commands can be manipulated,
and identities can be spoofed. For instance, attackers could
masquerade as the device and send false data to PLCs or
cloud services. In addition, the variety of sensors such as
microphones or cameras enables surveillance, for example, to
monitor the behavior and activities of employees.
E. Intellectual Property Theft
The significance of IP has already been described in Sec-
tion V. The primary source of threats to hardware, software,
and process IP is from competitors.
F. Legal
This group considers legal implications including breach
of service-level agreements, breach of legislation, and loss of
compliance. An increasing number of laws have recently been
drafted for connected devices. For example, a law in California
requires to have unique preprogrammed passwords for each
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device [27]. Germany also passed a law requiring manufactur-
ers of digital devices to provide updates [28]. Consequently,
manufacturers must continuously check whether the legal
situation for their devices has changed. Last, manufacturers
use a variety of third-party components. They must be careful
not to use protected material without authorization.
G. Malfunction / Failure
One of the most famous attacks that caused application
manipulation is Stuxnet, in which the speed of centrifuges
was changed, while hiding it from monitoring systems [29].
Two recent examples are TRITON [30] and Industroyer [31]
that were specifically created for OT devices and protocols. AI
methods are currently used in cloud services, edge devices and
even smart sensors. Special attention should be paid to them
as they can increase the number of threats. Attacks could com-
promise and limit AI results, reduce their effectiveness, and
lead to misclassification by providing adversarial examples.
In addition to the application, the communication can also
be manipulated. In this case, it is not about single bytes, but
rather about entire packets. Gateways, for example, are the link
between sensors and cloud services. Occasionally, packets may
be redirect or not forwarded at all. Further threats may arise
from hardware and software failures; components can fail due
to age or quality issues and software may contain bugs.
H. Outage
This group summarizes outages that affect large parts of
an ICS. The power supply is a basic requirement; an abrupt
interruption could lead to serious consequences. An outage in
communication between the numerous devices could have sim-
ilar outcomes. However, it is sufficient if a single production
support system, such as a logistics service, fails. Furthermore,
downtime can occur due to lack of materials.
I. Privacy
Privacy in the IoT remains a hot research topic. Due to
the complexity of the topic, this section follows the highly
regarded paper by Ziegeldorf et al. about privacy threats in
the IoT [32]. The threats, that mainly concern users, are
briefly summarized below. First, the threat identification arises
when an identifier can be linked with an individual and data
about him/her. This includes the identification of humans as
well as devices, for example, by fingerprinting. Second, as
devices become more interconnected, they can be queried
over the network, allowing attackers to gather information and
characteristics about existing devices, called inventory attacks.
The resulting inventory list of a factory could be interesting
for competitors, for example. Another threat occurs during life
cycle transitions. When device users change, the (sensitive)
data of the previous user is often still there; a function for
disposal is also often missing for full data wipe. The possibility
to link two or more previously separated systems poses a
further threat. The linkage of their data sources may reveal
(truthful or erroneous) information without consent of the
user. There are several ways to locate and track users through
the device or an associated service. This may be necessary
for the functionality of the application, but it also introduces
threats; for example, the performance of employees can be
tracked. The way users interact with IoT devices and the
information they present in response can also result in the
disclosure of sensitive data. For example, it might not be
possible to privately interact with a voice-controlled device in
public space. Last, there is a threat of user profiling to correlate
their interests or behavior with other profiles and data.
J. Unintentional / Disaster
Unfavorable conditions can affect the functionality of de-
vices. It matters little whether these are caused by environ-
mental factors or disasters, if only the impact is considered.
Threats can arise from the operation of a device outside of the
specified parameters, for example, in unapproved temperature
range (e.g., due to fire or lack of switching cabinet cooling)
or voltage range (e.g., due to lightning strike or fluctuations
in the power supply). Other categories are mechanical stress
(earthquake or misuse by employees), pollution (dust), and
There are countless ways to attack an IIoT device and
new attack techniques are also constantly being discovered.
Therefore, we categorized common attack techniques and
weaknesses (see Figure 5). The previously conducted de-
composition of a device into its individual components in
Section IV was leveraged here to get a comprehensive un-
derstanding of the attack surface. Below, these categories are
described by exemplary attack vectors throughout the life cycle
of an IIoT device, namely design, production, distribution,
setup, operation, maintenance, and end of life. Note that not
all attacks can be clearly assigned to a single category.
A. Hardware Attacks
A closer examination of hardware attacks revealed that they
can be subdivided into chip-, PCB- and device-level attacks.
It should be noted here that this category is different from
the commonly used designation physical attacks, as physical
access is not always necessary.
a) Chip-Level Attacks: Attacks against chips, such as
microcontrollers and custom ASICs, start at the design stage
and also during production. Various actors have the ability to
maliciously modify the design and insert a hardware Trojan
or backdoor, for example. This can be done by an untrusted
IP vendor, foundry, or design facility. Another threat source
may be a compiler or Computer-Aided Design (CAD) tool.
For example, a modified version of Apple’s Xcode infected
thousands of iOS apps with malware [33]. The facilities
involved also have the ability of IP theft, which enable product
piracy. There is still the possibility of reverse engineering in
the foundry or later in operation, if someone has no access
to the design files. There are further attack opportunities from
distribution to the end of life. First, it is possible to tamper
with data in chips by exploiting physical access. Such an attack
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End of Life
App1... AppN
Chip, PCB, and
Device Attacks
Attacks Cryptography
Supply Chain
Figure 5. IoT devices can be attacked during the entire product life cycle.
The various types of attacks on an IoT device, illustrated in the circle, are
shown in the blue boxes.
was demonstrated at the Chaos Communication Congress in
late 2019, when attackers were able to reroute orders from a
microcontroller distributor. After the installation of a backdoor,
the controllers could be shipped to the actual customers with-
out them noticing [34]. Furthermore, people with (temporary)
physical access to the chip can conduct microprobing or fault
injection attacks. The former uses microscopic needles to
probe internal wires, the latter deliberately injects a fault in
a system to change its behavior. Both use invasive attack
methods, although fault injection is also possible with semi-
invasive (e.g., focused laser beam) and non-invasive (e.g.,
clock and voltage glitching) attacks [35]. These techniques are
used to gain secret information or bypass security features,
for instance. Another possibility to extract secrets are side-
channel attacks. They observe parametric behaviors, such
as power consumption or timing information, of a specific
implementation of an algorithm to leak encryption keys, for
b) PCB-Level Attacks: First, attackers might be inter-
ested in the PCB design to understand or clone a product
causing a threat to IP. They can get the design by stealing
the CAD files or by utilizing reverse engineering techniques.
Second, a backdoor can be implanted by inserting a malicious
component on the PCB or by replacing an existing one. There
are several ways to accomplish the former [36]. Attackers must
initially insert an extra component into one of the different
design files. In the second step, it must be mounted on the
PCB. This can take place, for example, during production, at
repair and rework stations, or after the PCB has been deliv-
ered to a warehouse. Another option is to add a completely
new component. The project TPM Genie demonstrated that
TPMs can be attacked by an interposed device [37]. PCBs
usually contain interfaces for verifying the design and testing.
The commonly integrated protocol JTAG allows to program
memory or debug controllers, among others. Attackers can
exploit such interfaces to dump the firmware, resulting in an
IP theft, or overwrite local storage, enabling firmware and data
c) Device-Level Attacks: The third group contains more
general attack vectors and weaknesses. It includes sabotage
attacks that can be any physical change to hardware that has
a malicious impact. Threats can also arise unintentionally.
A service technician might replace a burnt out PCB or a
defective engine with a spare part that was not purchased from
the original manufacturer for price reasons resulting in faulty
operation, for example. There are other attack possibilities de-
pending on the specific component, e.g., some are vulnerable
to magnetic field attacks. One such component are displays.
A common weakness occurring in IIoT devices is permissive
displayed information to everyone (e.g., the firmware version),
if authentication is required at all. Another component are
USB ports, which allow insertion of malicious USB sticks
that either imitate a keyboard to inject commands or destroy
the port or entire device by putting a high voltage on the lines.
B. Firmware Attacks
The firmware enables several ways to attack a device.
Attackers can exploit vulnerabilities in one of its components,
for example, a network stack. Within a short time, dozens of
vulnerabilities were discovered in famous IoT and OT TCP/IP
stacks leading to DoS or Remote Code Execution (RCE);
the findings were named as INFRA:HALT, NAME:WRECK,
NUMBER:JACK, AMNESIA:33, and Ripple20 [38]–[42]. If
a vulnerable firmware is already fixed, attackers may try to
downgrade it to a previous version with security flaws. An-
other possibility is to utilize the firmware update mechanism
to install a manipulated version including a backdoor, for
example. Especially the hardware infrastructure including the
boot process is often vulnerable, as concluded by [43].
C. Application Attacks
There are numerous ways to attack applications. However,
the techniques depend on the utilized technologies such as
web servers, databases, and used programming language. A
few of these techniques are described below. A (remote)
code execution attack exploits vulnerabilities in a running
process, allowing the execution of any instruction on a system.
Recently, dozens of vulnerabilities were found in IoT and OT
RTOSs exploiting bad memory allocations [44]. Code injection
attacks that take advantage of insufficiently sanitized input
have a similar goal, e.g., SQL injection. Web applications
running on the IIoT devices are also exposed to typical
vulnerabilities such as Cross-Site Scripting (XSS), broken
access control, and insecure deserialization [45]. Furthermore,
for CPSs, data injection attacks should be considered, e.g.,
from spoofed sensor values. There are also attacks against
AI applications. It is conceivable to attack machine learning
models and data sets during their design or operation. Models
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can be sabotaged or poisoned and data sets or their labels can
be manipulated in order to reduce accuracy, for example.
D. Cryptography Attacks
Cryptography is a rather cross-disciplinary domain, as it is
used for encrypted local storage, authentication, and secure
communication, among others. In order to abstract from the
various applications, attacks against cryptography have been
grouped into this category. A common weakness is the use of
deprecated cryptographic algorithms. Especially OT devices
with a long lifetime employ these obsolete algorithms, such
as the Data Encryption Standard (DES). This can also be
suspected from the fact that still many modern microcon-
trollers implement hardware accelerators for those algorithms.
In addition, algorithm-specific parameters, such as the key
size, are often selected incorrectly. Examples include a poorly
chosen curve in elliptic curve cryptography, and a weak block
cipher mode for symmetric algorithms. Cryptography depends
heavily on random numbers, which potentially introduces fur-
ther weaknesses. These include weak pseudo-random number
generators, lack of sufficient entropy, and the reuse of nonces
(i.e., number that can be used only once). When handling
passwords, design flaws can also occur that facilitate brute
force or rainbow table attacks.
E. Network Attacks
The integration of network interfaces into OT devices has
significantly increased their attack surface. One of the major
concerns of ICS operators are (D)DoS attacks, as unavailabil-
ity may cause production downtime, which in turn results in
financial damage. In addition to the classic attack techniques,
attacks on wireless communication technologies should also be
considered, e.g., radio jamming. IIoT devices can be the target
of attacks themselves as well as being used to launch attacks
against SCADA systems or cloud services. Another risk are
the various automation protocols used within OT systems
that were designed decades ago and do not support security
controls. As a result, man-in-the-middle and replay attacks
can often be conducted because of missing authentication
and unencrypted communication. Last but not least, network
interfaces enable attack preparation attacks, such as port scans
or device fingerprinting.
F. User Behavior
Users can introduce threats at different stages in the device
life cycle. During setup, technicians might misconfigure the
device, for example, by disabling security features, or ne-
glect to create a configuration backup and keep this secure.
Afterwards, operators might install malware, for instance,
to mine cryptocurrencies. The mishandling of warnings and
errors, as well as the incorrect use of the device in general,
can also lead to further threats. Likewise, errors can occur
during maintenance. Technicians are usually responsible for
installing firmware updates, as automatic over-the-air updates
are problematic due to safety requirements. They are also
responsible for deleting all data at the end of life.
a) Credential Attacks: Credentials are an important asset
of users and, therefore, a major target of attackers. With some
exceptions, such as improper storage and cleartext transmis-
sion, users are often the weak point to get them. Contrary
to recommendations, default and simple passwords are still
used, as well as shared passwords with colleagues. Many users
also disregard social engineering attacks such as phishing or
shoulder surfing.
G. Ecosystem Weaknesses
This category summarizes weaknesses in the ecosystem that
arise due to incomplete system design and the heterogeneous
structure of the IIoT. Some manufacturers still do not provide
software updates, and if they do, they are rare. Especially IIoT
device manufacturers rarely implement vulnerability disclo-
sure policies for fear of damaging their reputation. Further bad
practices include hard-coded passwords, developer backdoors,
and compromisable procedures for lost credentials. Another
vulnerability is the implicit trust between components within
the device and also in the entire ecosystem. The latter are still
far from implementing the goal of zero trust. A huge challenge
is interoperability; solutions are complicated by the numerous
legacy standards in ICSs. Other devices and (third-party) IoT
services for device life cycle management, telemetry, and
analytics are therefore blindly trusted.
H. Supply Chain Attacks
Several supply chain attacks have already been mentioned in
the previous categories. For the sake of clarity, these are again
summarized and expanded in this section. A frequent target is
malicious modification of functionality by hardware, software
or data. Hardware logic insertion occurs during design and pro-
duction stage, and replacing a hardware component with a ma-
licious one is feasible during production and in all later stages.
The manipulation or insertion of software code is conceivable
in all life phases, e.g., by malicious third-party libraries or by
a manipulated firmware. Data used to train machine learning
models, for example, can also be compromised. IP theft of
hardware and software is also possible in all stages; in the
case of hardware, this is particularly achievable during design
and production, as these steps are often outsourced to external
contractors. Outsourcing the production additionally enables
cloning of provisioning data and unauthorized overproduction;
discarded or defective equipment could moreover end up on
the gray market. A further threat to production is the use of
counterfeit components or generally inferior material.
Last, we recommend a procedure for the threat analysis of
IIoT devices. For this purpose, the previously discussed as-
pects are revisited and put in order. Note, this recommendation
can be seen as complementary to existing Threat Analysis and
Risk Assessment (TARA) methodologies, such as IEC 62443-
3-2, in order to facilitate a device-oriented procedure.
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1. System Analysis
The first step is to analyze the IIoT device in depth. All
hardware and software components and data files, as described
in Section IV, should be collected. This enables a component-
by-component threat and vulnerability analysis later on. In
addition, requirements and (environmental) conditions should
be gathered. Requirements may originate, for example, from
the operating location, industrial sector, or use in critical
2. System Interaction Overview
As previously stated, connectivity is a central element of
IIoT devices. Therefore, an overview of all interactions with
the device is necessary for a thorough threat analysis. This
diagram should contain all actors including humans, cloud
services, and other machines. This can be supported by an
additional use case diagram to capture when and why an actor
interacts with the system.
So far, we have only referred to users in general terms;
especially in the case of industrial systems, it is sensible to
differentiate between them according to their role. For exam-
ple, the commissioning, operation, and maintenance personnel
are often not the same. The goal is to grant each group only
the least required privileges. The authorization of certain net-
work interfaces should also be considered. This is especially
important for industrial protocols, such as PROFINET. While
most IoT applications allow the implementation of security
measures manually, it is not possible with these proprietary
protocols, as compatibility with other manufacturers must be
3. Asset Identification
The third step is the identification of assets based on the
various types presented in Section V. The assets can be
best identified by considering the interests of the different
stakeholders; again, the different roles of users should be
distinguished. This step helps to understand what values to
each party. It also allows to rank the criticality of assets, which
can be used for defining the impact of attacks.
Furthermore, it is useful to determine the security goals in
general and also per asset. This can support the development
of countermeasures later. For CPSs, availability is often most
important for safety reasons. For edge devices, confidentiality
could be rated higher, as they aggregate information. However,
in low-power IIoT devices that must comply with real-time
requirements, it is not always feasible to implement the most
secure countermeasure. Thus, this initial assessment can be
used to choose the right measure. For example, sensitive data
in transit (e.g., credentials) will be encrypted, less critical data
(e.g., sensor values) will only be protected against manipula-
tion using a message authentication code.
4. Threat Source Identification
Threat sources can be best identified from the perspective
of the attacker to find out what they are targeting and why.
There are several types of attackers with different capabilities,
attack techniques and motives. We classified various types of
threat sources and their respective intentions in [1]. This is
useful for deliberately including or excluding types of attacks.
For IIoT devices in critical infrastructures, the more complex
hardware and supply chain attacks should be addressed.
5. Threat and Vulnerability Identification
The primary task of a threat analysis is the identification
of threats and vulnerabilities. However, the preparatory work
from the previous four steps should significantly accelerate
and enhance this process. As said before, the system analysis
should allow the detection of threats to single hardware
components, such as PCBs and actuators. The list of software
components can also be used to search for publicly known
vulnerabilities. Numerous ways to attack them were presented
in Section VII. Additionally, it is possible to reflect on how
the various threats, presented in Section VI, may arise. Using
attack trees, it is also possible to graphically show how assets
can be attacked. Figure 6 shows a sample tree for attacking
the manufacturer key that is used for firmware updates, for
instance. One option is to dump the memory, which can be
achieved either by (remote) code execution or by exploiting
physical access. In the latter case, it also depends on where the
key is stored; for microcontroller internal memory, a read-out
protection may have to be broken.
Manufacturer Key
Attack Key
Processing Attack
Supply Chain ...
Dump Memory
using RCE
Exploit Physical
Access External
Access Internal
Break Code
Figure 6. Fraction of an example attack tree on an asset.
The system interaction and use case diagrams can be utilized
to create data flow diagrams in order to identify threats
to data in transit. These can be used in combination with
threat modeling techniques such as STRIDE [46], a mnemonic
for threats against the security goals authenticity, integrity,
non-repudiation, confidentiality, availability, and authorization.
Furthermore, penetration testing can be used to discover
additional vulnerabilities as well as to verify those already
identified and show their severity.
Documenting the discovered threats is also crucial. We
have had the experience that detailed attack scenarios can be
better understood in retrospect. Table I shows an excerpt of a
potential attack scenario description. This example lists threats
resulting from a default PIN used in an electrical actuator
controlling a valve. The PIN is required for authentication
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No. Vulnerability Threat
Attack Vector Note
Interface Action
1Default PIN
(users did not change it)
Login as administrator
(obtaining of control)
Bluetooth app,
local HMI Authenticate using default PIN PIN can be found
in the manual
1.1 Moving actuator
(application malfunction) Bluetooth app Open/close valve
1.2 Blocking remote control
(denial of service) Bluetooth app Change control system
communication parameter
1.3 Manipulating user database
(data tampering) Local HMI View/change/add/delete
user accounts
at the attached HMI and in a Bluetooth mobile app. An
unchanged PIN would allow attackers to authenticate as the
6. Vulnerability and Risk Assessment
The final step is to gather all the information to evaluate
the vulnerability and assess the risk. A popular method for
vulnerability assessment is the Common Vulnerability Scoring
System (CVSS). It incorporates exploitability metrics, such as
the attack vector, and the impact on confidentiality, integrity,
and availability. The example in Table I indicates that several
threats arise from a single vulnerability. As the impact can
vary per interface, an overview of all scenarios is important
for a proper scoring. Finally, the risk assessment may include
further criteria such as likelihood and (business) impact.
Targeted attacks on ICSs, including those in critical in-
frastructures, increased recently. IIoT devices that merge the
previously separated areas of IT and OT additionally increase
the attack surface. The consequences of an attack can be
dramatic, as OT equipment controls physical processes that,
in case of compromised safety, can cause harm to humans,
machines, and the environment. Therefore, it is even more
essential that IIoT devices are properly secured. However, this
is not often the case in reality. One reason is that manufacturers
are extensively provided with literature on best practices rather
than threat analysis techniques for their devices.
In this paper, we presented a systematic and holistic pro-
cedure for analyzing the attack surface and threats of IIoT
devices throughout the product life cycle. First, an arbitrary
IIoT device was decomposed into its components for this pur-
pose. This itemization of hardware and software components
as well as data types is essential to ensure that no attack vectors
are overlooked. Afterwards, the assets were analyzed from
the perspective of different stakeholders in order to identify
everything that is valuable and worth protecting. The provided
comprehensive categorization of threats shows an overview of
possible threats and their consequences. The attack techniques
are almost innumerable and are also constantly expanding.
Therefore, we categorized attack techniques and weaknesses
that are frequently exploited. This included not only common
attack vectors in communications and web applications, but
also attacks against the supply chain and the various hard-
ware components. Finally, the threat analysis procedure was
described, which enables manufacturers and operators of IIoT
devices to identify and evaluate attack vectors. Since the threat
categorization considers all assets and the attack categorization
addresses all components, the proposed analysis technique
seems to be valuable. In the next steps, the procedure will
be further validated.
The research project “Intelligent Security for Electrical
Actuators and Converters in Critical Infrastructures (iSEC)”
is a collaboration of SIPOS Aktorik GmbH, Grass Power
Electronics GmbH and OTH Amberg-Weiden. It is supported
and funded by the Bavarian Ministry of Economic Affairs,
Regional Development and Energy.
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... The next step is to identify the various (intangible) assets. The procedure is described in more detail in [9]. The fifth step is to address the questions of who is threatening the device, why they might attack it, which threats arise, and what consequences may result. ...
... The fifth step is to address the questions of who is threatening the device, why they might attack it, which threats arise, and what consequences may result. Further information can be found in [9,10]. ...
Technical Report
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An increasing number of devices from everyday life and the working environment are being connected to a network, and thus become part of the so-called Internet of Things. However, many of these devices have vulnerabilities that consequently threaten the security and privacy of the users. This report presents the current progress of a PhD project that aims to support device manufacturers in identifying threats and vulnerabilities and developing suitable countermeasures. For this purpose, a threat modelling technique and a risk assessment methodology are presented.
... Durch diese Vorarbeiten konnten sämtliche Angriffsszenarien auf die Geräte der Industriepartner untersucht werden. Die angesprochenen Ergebnisse sind detailliert in den Publikationen[2,3] beschrieben.Die Bedrohungsanalyse wurde durch Penetrationstests begleitet, um weitere Schwachstellen zu finden und die identifizierten Angriffsszenarien zu verifizieren. Im Rahmen dessen wurden auch Fuzzing-Tests durchgeführt. ...
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Zusammenfassung: Im Zuge des industriellen Internet der Dinge werden Sensoren und Aktoren netzwerkfähig und intelligenter. Die vergrößerte Angriffsfläche führt jedoch zu einem er-höhten Risiko von Cyberangriffen auf industrielle Kontroll-systeme. Vor allem in kritischen Infrastrukturen kann dies zur Gefahr für Mensch und Umwelt werden. In diesem Bericht wird das Forschungsprojekt "iSEC" vorgestellt, dessen Ziel die Entwicklung von Security-Lösungen für leistungsschwache industrielle eingebettete Systeme ist. Es werden Ergebnisse aus den Bereichen Bedrohungs-analyse, Physical Unclonable Functions und Kryptografie präsentiert und eine Auswahl gewonnener Erkenntnisse beschrieben. Abstract: As the industrial Internet of Things progresses, sensors and actuators become network-enabled and smarter. However, the increasing attack surface leads to a higher risk of cyberattacks on industrial control systems. This may endanger people and the environment, especially for systems in critical infrastructures. This report presents the research project "iSEC", which aims to develop security solutions for low-power industrial embedded systems. The results from the threat analysis, the evaluation of Physical Unclonable Functions and cryptographic solutions are presented and a few findings described.
... The requirement for a wider IP network introduces a new emerging risk in I4.0-industrial cyber-attack. Increasing the number of devices on the network increases the attack surface for malicious actors [29]. Industrial Internet of Things (IIoT) devices connect to a network in much the same way IT devices do, however there are key differences in the risk they present in terms of cybersecurity [30]. ...
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Rapid development of smart manufacturing techniques in recent years is influencing production facilities. Factories must both keep up with smart technologies as well as upskill their workforce to remain competitive. One of the recent concerns is how businesses can contribute to environmental sustainability and how to reduce operating costs. In this article authors present a method of measuring gas waste using Industrial Internet of Things (IIoT) sensors and open-source solutions utilised on a brownfield production asset. The article provides a result of an applied research initiative in a live manufacturing facility. The design followed the Reference Architectural Model for Industry 4.0 (RAMI 4.0) model to provide a coherent smart factory system. The presented solution’s goal is to provide factory supervisors with information about gas waste which is generated during the production process. To achieve this an operational technology (OT) network was installed and Key Performance Indicators (KPIs) dashboards were designed. Based on the information provided by the system, the business can be more aware of the production environment and can improve its efficiency.
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Modern electronic devices have become “smart" as well as omnipresent in our day-to-day lives. From small household devices to large industrial machines, smart devices have become very popular in every possible application domain. Smart devices in our homes, offices, buildings, and cities can connect with other devices as well as with the physical world around them. This increasing popularity has also placed smart devices as the center of attention among attackers. Already, several types of malicious activities exist that attempt to compromise the security and privacy of smart devices. One interesting and noteworthy emerging threat vector is the attacks that abuse the use of sensors on smart devices. Smart devices are vulnerable to sensor-based threats and attacks due to the lack of proper security mechanisms available to control the use of sensors by installed apps. By exploiting the sensors (e.g., accelerometer, gyroscope, microphone, light sensor, etc.) on a smart device, attackers can extract information from the device, transfer malware to a device, or trigger a malicious activity to compromise the device. In this paper, we explore various threats and attacks abusing sensors of smart devices for malicious purposes. Specifically, we present a detailed survey about existing sensor-based threats and attacks to smart devices and countermeasures that have been developed to secure smart devices from sensor-based threats. Furthermore, we discuss security and privacy issues of smart devices in the context of sensor-based threats and attacks and conclude with future research directions.
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As part of the Internet of Things, industrial devices are now also connected to cloud services. However, the connection to the Internet increases the risks for Industrial Control Systems. Therefore, a threat analysis is essential for these devices. In this paper, we examine Industrial Internet of Things devices, identify and rank different sources of threats and describe common threats and vulnerabilities. Finally, we recommend a procedure to carry out a threat analysis on these devices.
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A key application of the Internet of Things (IoT) paradigm lies within industrial contexts. Indeed, the emerging Industrial Internet of Things (IIoT), commonly referred to as Industry 4.0, promises to revolutionize production and manufacturing through the use of large numbers of networked embedded sensing devices, and the combination of emerging computing technologies, such as Fog/Cloud Computing and Artificial Intelligence. The IIoT is characterized by an increased degree of inter-connectivity, which not only creates opportunities for the industries that adopt it, but also for cyber-criminals. Indeed, IoT security currently represents one of the major obstacles that prevent the widespread adoption of IIoT technology. Unsurprisingly, such concerns led to an exponential growth of published research over the last few years. To get an overview of the field, we deem it important to systematically survey the academic literature so far, and distill from it various security requirements as well as their popularity. This paper consists of two contributions: our primary contribution is a systematic review of the literature over the period 2011-2019 on IIoT Security, focusing in particular on the security requirements of the IIoT. Our secondary contribution is a reflection on how the relatively new paradigm of Fog computing can be leveraged to address these requirements, and thus improve the security of the IIoT.
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Historically, Industrial Automation and Control Systems (IACS) were largely isolated from conventional digital networks such as enterprise ICT environments. Where connectivity was required, a zoned architecture was adopted, with firewalls and/or demilitarized zones used to protect the core control system components. The adoption and deployment of ‘Internet of Things’ (IoT) technologies is leading to architectural changes to IACS, including greater connectivity to industrial systems. This paper reviews what is meant by Industrial IoT (IIoT) and relationships to concepts such as cyber-physical systems and Industry 4.0. The paper develops a definition of IIoT and analyses related partial IoT taxonomies. It develops an analysis framework for IIoT that can be used to enumerate and characterise IIoT devices when studying system architectures and analysing security threats and vulnerabilities. The paper concludes by identifying some gaps in the literature.
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Internet of Things (IoT) is an emerging domain that promises ubiquitous connection to the Internet, turning common objects into connected devices. The IoT paradigm is changing the way people interact with things around them. It paves the way to creating pervasively connected infrastructures to support innovative services and promises better flexibility and efficiency. Such advantages are attractive not only for consumer applications, but also for the industrial domain. Over the last few years, we have been witnessing the IoT paradigm making its way into the industry marketplace with purposely designed solutions. In this paper, we clarify the concepts of IoT, Industrial IoT, and Industry 4.0. We highlight the opportunities brought in by this paradigm shift as well as the challenges for its realization. In particular, we focus on the challenges associated with the need of energy efficiency, real-time performance, coexistence, interoperability, and security and privacy. We also provide a systematic overview of the state-of-the-art research efforts and potential research directions to solve Industrial IoT challenges.
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Solving security concerns are one of the main challenges for the Internet of Things. There are different issues to be solved within the physically connected part of the IoT and in the networking domain, and another set of issues exist for the data-processing back-end, not to mention the presentation/configuration layer, where direct human interaction brings in further threats. Automation IoT applications have special real-time requirements, they are expected to have high level of reliability, and often operate in safety-critical environment. These requirements justify extreme security and safety measures. This paper discusses the security threats that can appear in the different layers of an IoT architecture, especially in the automation domain. The mitigation practices of the various security issues are also discussed, bearing in mind that the solutions can bring quite different measures for the physical equipment in the field, for the communication infrastructure, or for the data processing applications.
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The Internet of Things (IoT) has various fields of application including health care, resource management, asset tracking, etc. Depending on the use case, various technologies like RFID, Wireless Sensor Network (WSN) or Smart Objects can be used. With each of these comes a specific vision of what the IoT and connected objects are and – to our knowledge – there is no global picture of the IoT. The issue with this approach is that specific problems have been addressed before global ones: what if something has been missed? We propose a definition and taxonomy for connected objects and the IoT.
• IN 2018, an article in Bloomberg Businessweek made the stupendous assertion that Chinese spy services had created back doors to servers built for Amazon, Apple, and others by inserting millimeter-size chips into circuit boards.
The Internet of Things (IoT) is an emerging paradigm focusing on the inter-connection of things or devices to each other and to the users. This technology is anticipated to become an integral milestone in the development of smart homes and smart cities. For any technology to be successful and achieve widespread use, it needs to gain the trust of users by providing adequate security and privacy assurance. Despite the growing interest of the research community in IoT, and the emergence of several surveys and papers addressing its architecture and its elements, we are still lacking a thorough analysis of the security and privacy properties that are required for a system where the constituent devices vary in their capabilities. In this paper we provide a threat model based on use-cases of IoT, which can be used to determine where efforts should be invested in order to secure these systems. We conclude by recommending measures that will help in providing security and assuring privacy when using IoT.