1CHAPTER 22PEER REVIEWED SUMMARYi. Introduction

1
CHAPTER 2
2
PEER REVIEWED SUMMARY
i. Introduction
ii. Title Searches, Articles, Research Documents and Journals
iii. Big Data
a. What is Big Data
b. Examples of Big Data
c. Pros/Cons of Big Data
d. Industries using Big Data
i. Automobile ~
ii. Manufacturing
iii. Telecommunication
iv. ITS – Intelligent Transportation Systems
a. Introduction to ITS
b. Examples
c. Fields of ITS
i. Automotive Control system
ii. Public Safety
iii. Traffic Management
iv. Public Transportation system
v. Commercial Vehicles Control System
d. ITS Cyber Security
i. Definition and Importance
ii. Vulnerability incidents
v. Big Data and ITS
a. Big Data in ITS
i. Big Data from Smart Cards
ii. Big Data from GPS

iii. Big Data from Sensors
iv. Big Data from Videos
v. Big Data from Connected and Autonomous Vehicles (CAVs)
vi. Big Data from Vehicle Adhoc Network (VANET)
vii. Big Data from Other Sources
b. Gaps in Big Data for ITS
vi. Cyber security
a. Phishing
b. Eves dropping
c. Cyber terrorism
d. Vehicle communication security breach (VANET)
e. Data Breach in industries and Examples
i. Automobile
ii. Manufacturing
iii. Telecommunication
vii. Theoretical framework
viii. Review of Methodological Literature
ix. Summary
Introduction
In recent studies, big data is becoming a more appealing
research subject in Intelligent Transportation Systems (ITS), as
shown by the fact that it is employed in various projects
worldwide. The enormous volumes of data produced will have
significant ramifications for the design and implementation of
intelligent transportation systems and in turn the need to make
it safer, more efficient, and profitable. Intelligent transportation
systems will create a large amount of data, which will be used
to make transportation-related decisions (Darwish & Bakar,
2018). The first section of this chapter is dedicated to a detailed
research of the history and characteristics of big data,
intelligent transportation systems and Cyber security
combined. This chapter will also cover the ITS framework, data
collection and management techniques, data analytics
methodologies, ITS platforms, examples of Big data and
importance of Big Data and ITS in many other industries. A

wide range of topics, including road traffic accident analysis,
road traffic flow prediction, public transportation service
planning, personal travel route planning, public safety,
commercial vehicles control systems, and more, are covered in
this chapter of big data applications in intelligent transportation
systems (Darwish & Bakar, 2018). Finally, this chapter
discusses some of the problems and gaps that still need to be
researched regarding big data in Intelligent Transportation
Systems in Automobiles on cyber security vulnerabilities.
According to a research done by Transportation Research
Board, the growing use of Big Data in large-scale Internet-of-
Vehicles deployments has opened the door to previously
imagined possibilities for unified transportation sector
management and the creation of intelligent transportation
systems. As a result of the widespread heterogeneous data
collecting methods between automobiles and numerous other
application platforms, there is a growing need for secure data
collection in such architectures, which is being fulfilled by an
expanding number of suppliers. In recent years, a rise in the
number of cyber security and privacy breaches has increased the
demand for secure data collection. However, the primary goal of
this chapter is to draw the reader's attention to the challenge
above and provide a brief current background of this
research. In addition, research hurdles, prospects, and open
research topics will be explored.
Title Searches, Articles, Research Documents and Journals
The Literature review heavily depended on scholarly articles on
Big Data Cyber Security in Intelligent Transportation Systems,
mostly covering the years from 2012-2022. Scholarly articles
were searched and researched from many data bases such as
IEEE Xplore (IEEE), ProQuest Central, Google Scholarly,
ProQuest Dissertations and Theses Global. Some of the
keywords used for searching are Intelligent Transportation
Systems, Data Breach, Cyber Security, Big Data, Cyber security

vulnerabilities, Big Data in ITS, ITS in automobiles and Cyber
security challenges in ITS.
The organization of this literate review focus on three key
elements. (a) Big Data, (b) Cyber Security (c) Intelligent
Transportation Systems.
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Big Data
What is Big Data?
Big data is referred to as large datasets with 5 main
characteristics - volume, value, variety. Velocity and veracity.
Big data is a term used to describe a massive amount of various
types of data which cannot be processed using the traditional
management systems (Sivarajah, Kamal, Irani, & Weerakkody,
2017). It is referred with 5 main characteristics - volume, value,
variety. Velocity and veracity which are called the 5Vs . Big
data initially was formed with key characteristics volume;
variety; and Velocity but later the other characteristics were
added.
According to Venkatraman and Venkatraman (2019), Big Data
can be explained with 11V’s which are the fundamentals
characteristics.
(1) Volume refers to the size of the data collected through
various sources such as
integrated systems, mobile applications, sensors, IoT devices,
social media, etc.; (2) velocity
refers to the speed at which the data is generated and processed
in real-time; (3) variety refers to
the formats of the data such as structured, unstructured and
semi-structure. Structured data uses a

underlying structure or a template to store data such as using
tables in the databases, fixed format
length files, etc., whereas unstructured data refers to that which
does not have a underlying
structure to store data, such as free text from files and social
media, audio, video, image, sensor
(digital and analog) signals, and data from other sources; (4)
veracity refers to the accuracy of
the data. Any incomplete or incorrect data would contribute to
bad decisions due to processing of
the bad data; (5) validity refers to using the processed data for
intended use at the right time to
make accurate decisions and reap benefits; (6) volatility refers
to volatility of data or for how long the data is valid. It is
important to note that any information at given particular point
in time
depends on external parameters, and if the external parameters
change the information processed
for a specific situation may not render any value; (7) value
refers to usefulness of the information
in decision making and improving performance; (8) variability
refers to inconsistencies such as
the outliers to detect exceptions; (9) visualization refers to how
data can be represented using
other visual formats such as graphs, dashboards, etc., so that it
is easier for the people using the
information to read and interpret results; (10) valence refers to
the density of the data ; (11)
vulnerability refers to the vulnerability the data which relates to
sensitivity, security and privacy.
Examples of Big Data
This data eventually converges to provide Big Data from the
Internet of Vehicles. While the number, volume, and size of this
data vary, it is of good quality the majority of the time. Big
Data analytics may aid network operators in optimizing overall

resource(s) planning for next-generation vehicle networks,
resulting in cost savings.
Consequently, national transportation authorities would have an
easier time assessing and effectively fixing the country's
chronic traffic congestion issues, increasing the overall quality
of life for millions of people throughout the country.
Automobile manufacturers have lately invested in large-scale
Big Data platforms to advance the development of intelligent
transportation systems. All of this data analytics and subsequent
decision-making becomes more difficult if a hostile vehicle can
input data into the Big Data stream of the Internet of Vehicles,
with substantial ramifications for safety-critical and non-critical
vehicular applications (Chai et al., 2020). According to safety
applications, any intentionally provided information could
result in incorrect trajectory predictions and inaccurate
assessment of a vehicle's nearby environment soon, both of
which could be extremely dangerous for passengers riding in
semi-autonomous and fully autonomous vehicles. In the case of
non-safety applications, this may result in considerable delays
in delivering requested services, but it may also expose a user's
personal information to serious hazards.
This strategy may not only drastically decrease computational
overheads but may also considerably reduce the amount of IoV
Big Data that must be cached (Chai et al., 2020). Furthermore,
real-time and historical data are critical for intelligent decision-
making in IoT infrastructures, which should not be overlooked.
Because of international storage limits and a limited number of
available spaces, it is not feasible to keep all historical IoV Big
Data in a single place for an extended time due to the nature of
the data. As a result, getting IoV Big Data is crucial, as is
ensuring that the data streams received are not kept forever and
are purged after a certain amount of time. In comparison to raw
data streams in their natural state, our proposed architecture
allows raw data streams to be organized in a way that provides
relevant information while utilizing fewer resources and taking
up less storage space.

Pros/Cons of Big Data
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Industries using Big Data
Automobile Industry
Due to the increase in number of automobiles on the road in
recent decades across the globe, present transportation
infrastructure has been strained to its breaking point, forcing
existing infrastructure to collapse. As a consequence of this,
transportation networks have become very inefficient and
prohibitively expensive to maintain and improve over time.
According to most recent data, over one billion automobiles are
on the road, predicting that this number would double by the
end of 2035. Not only has traffic congestion in dense
metropolitan areas increased dramatically due to the increase in
the number of cars, but it has also resulted in an increase in the
number of people killed or injured in automobile accidents,
which has hampered economic development in a variety of
ways. In 2017, according to the World Health Organization,
approximately 1.25 million people died, and millions more were
injured due to road traffic accidents. Additionally, walkers,
roller skaters, and motorcyclists were among the most
vulnerable road users in 2010, accounting for almost half of all
deaths and injuries on the roads (Guevara & Auat Cheein,
2020). According to the World Bank, low- and middle-income
nations and areas account for more than 90 percent of all road
fatalities and injuries worldwide due to poor transportation
infrastructure. In terms of network manageme nt, the outcome is
a considerable drop in the quality of service for a wide variety
of safety-critical and non-safety applications and a drop in the
overall quality of the user experience for vehicle operators
(Guevara & Auat Cheein, 2020). As a result, there is a lot of
room for improvement in today's transportation systems,
especially in terms of safety and efficiency, to name a few.

Early resolution of these challenges will pave the way for
creating highly efficient intelligent transportation networks,
which will be necessary to realize the vision of connected
automobiles.
HealthCare industry
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Telecommunication Industry
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Big Data Collection in Vehicular Networks
The research shows that significant changes in the
transportation sector are expected soon, notably because of new
paradigms such as the Internet of Things (IoT), cloud
computing, edge and fog computing, software-defined
networking, and the newly proposed Named Data Networking.
Indeed, such novel ideas have undoubtedly aided and reinforced
the industry's efforts to develop intelligently linked automobiles
to assure safe and comfortable driving conditions. Furthermore,
new vehicle applications and services are growing because of
the ongoing expansion of high-speed mobile Internet
connections and the rising demand for seamless, ubiquitous
communication at lower prices than previously thought possible
(Gohar et al., 2018). According to the European Commission,
by the end of 2019, between 50 and 100 billion intelligent
gadgets will be directly linked to the Internet, resulting in the
generation of around 507.5 ZB of data every year. According to
Automotive News, smart cars have grown into multisensor
platforms in recent years. The average number of intelligent
sensors placed on a vehicle remains at 100 and will reach 200
by 2020. Because vehicle networks are very dynamic, the data
streams produced by these sensors are massive and extremely
quick.
Furthermore, automobile users obtain data from a range of
social networking sites daily, resulting in a massive number of

real-time traffic data. Because it is time-dependent and
location-dependent, this information is spatiotemporal.
Consequently, since the road network's dispersion often
influences the route chosen by automobiles throughout a large
geographical area, the data received differs in terms of method
of transportation, size, and information quality (Gohar et al.,
2018). Traditional vehicular ad hoc networks are being
expanded to large-scale Internet of Vehicles (IoVs). All data
collected in vehicular networks converge as Big Data in
vehicular networks, routed through core networks to regional
and regional centralized cloud computing environments and
other vehicular networks.
However, the current Internet infrastructure is inefficient and
does not scale efficiently when processing the large amounts of
IoV Big Data generated today. Furthermore, transporting such
data is time-consuming and costly since it requires a significant
quantity of bandwidth and energy. Again, real-time data
processing is necessary for various safety-critical applications,
necessitating the development of a highly efficient data
processing architecture with significant computational
capability. Due to the dispersed nature of vehicle networks,
distributed edge-based processing is preferable to centralized
systems (Payalan & Guvensan, 2019). As a consequence,
performance is improved as compared to traditional centralized
systems. However, since such distributed systems often lack
significant processing and storage capacity, they cannot
correctly cache and analyze enormous quantities of data. Not to
mention the need for a secure approach to guarantee that IoV
Big Data is collected consistently and not tampered with
throughout the collecting process (Payalan & Guvensan, 2019).
A hostile vehicle inserting counterfeit messages into the traffic
system is a definite possibility. Such an occurrence might easily
impair the whole system or even use the entire network to
engage in dangerous activity for its evil objectives. As a result,
research into effectively securing Big Data collecting in IoVs is
required. In this setting, it is critical to draw the attention of

academic and commercial research groups to the importance of
such concerns, which this chapter does wonderfully.
Intelligent Transportation System
Introduction to ITS
An intelligent transportation system (ITS) is created when a
combination of information technologies is properly combined
and implemented with the help of data-driven insights to
improve the efficiency and effectiveness of transportation. As
technology and electronic applications evolve, so does the user
base, which is alarming. Information and Communication
Technology (ICTs) have already impacted many industries and
professions, including healthcare, manufacturing, and security
(IT) (Pustokhina et al., 2018). Furthermore, as a result of
technological advances, the transportation industry is changing
and evolving. Portugal, Singapore, Germany, and the United
Kingdom are leading the transition from traditional modes of
transportation to highly technologically advanced infrastructure.
The United Kingdom is shifting to an intelligent transportation
system due to the change. Intelligent transportation systems are
increasingly recognized as a critical component in
transportation planners' toolkits for addressing long-standing
surface transportation issues that have persisted for decades.
The "info structure," a data-driven design that supports and
complements physical transportation infrastructure, is crucial to
the intelligent transportation system because it serves as its
nerve center.
The ultimate purpose of ITS is to keep passengers safe in the
case of a car accident. This is done via better mobility and
safety and improved operational performance, notably in terms
of congestion and vehicle safety evaluation, ITS goals. Only a
small percentage of the population can create jobs for others.
Increased job opportunities will also benefit the general public.
V2I and V2V systems, such as Japan's Smartway and the United

States' IntelliDrive, are designed to help drivers in keeping a
safe distance from an impending collision throughout the
process, according to its developers. According to one estimate,
IntelliDrive technology can handle 82 percent of all car
collision scenarios in the United States with healthy drivers.
Furthermore, it contributes to increasing the capacity of
existing infrastructure while reducing the need for new road
development, which is advantageous (Garg et al., 2018). For
example, in the United States, real-time traffic data has
considerably improved traffic flow. Pauses have been reduced
by 40%, while travel time has been cut by 25%, gas
consumption has been reduced by 10%, and pollution has been
reduced by 22% compared to the previous year (Garg et al.,
2018). Despite the various and considerable advantages that
intelligent transportation systems may give, many governments
are now underinvesting in them (ITS). This is since multiple
obstacles must be overcome throughout the development and
implementation of the technologies above.
Examples of ITS
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Fields of ITS
Automotive Control system
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Public Safety
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Traffic Management
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Public Transportation system
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Commercial Vehicles Control System

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ITS Cyber Security
Definition and Importance
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Vulnerability incidents
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Big Data Collection Sources in Intelligent transportation system
(ITS)
Furthermore, Big Data and analytics research supports a wide
range of application organizations by giving a significant
opportunity to utilize evidence to impact decision-making in
various domains. Is it possible to effectively apply Big Data and
analytics ideas to the transportation industry? The authors
thoroughly evaluate articles published in the last five years that
address Big Data concepts and applications in the transportation
sector, focusing on the transportation industry. This article
covers the review's conclusions and consequences (Chai et al.,
2020). One of the main goals from this research is to have a
better knowledge of the existing research, possibilities, and
constraints around Big Data and cyber security vulnerabilites in
the Intellignet Transportation Systems in Automobiles. This
research investigates and comprehends current research,
prospects, and issues from various angles. According to the
article, Big Data and analytics may give insights and improve
transportation systems by analyzing data from a range of
sources such as traffic monitoring systems, connected autos,
crowdsourcing, and social media. Numerous storage,
processing, and analytical solutions are being studied, and
specialized platforms and software architecture are built

expressly for the transportation industry. We also look at the
challenges resulting from Big Data and analytics adoption (Chai
et al., 2020). Aside from that, it significantly expands the
number of ways cities may utilize Big Data in transportation to
aid in the construction of sustainable and safer transportation
networks. Because research in Big Data and vehicle is still in its
infancy, this article cannot propose precise answers to the many
issues described. This is also a flaw in the book since it lacks
coherent replies to the many arguments presented (Sumalee &
Ho, 2018).
To guarantee the proper functioning of an intelligent
transportation system, data from a range of sources, including
CCTV cameras, sensors, RFID, GPS, and other technologies,
must be gathered. The information is compiled from various
publicly available sources, including CCTV cameras with
number plate recognition and other comparable technology.
Image processing, which aids in collecting appropriate toll
payments for the identified vehicle, and CCTV cameras for
filming purposes such as criminal identification and the
detection of misappropriated vehicle information are advanced
methods for applying toll charges (Zhou et al., 2020).
It is now possible to employ radio frequency identificati on
(RFID), another data source in intelligent transportation
systems, to automatically detect the unique RFID tags contained
in automobiles.
RFID tags offer information about the car's identification
number, the owner's name, and the amount of prepaid credit
presently accessible on the vehicle (Zhou et al., 2020). When a
vehicle passes through a toll bridge, the RFID tag affixed to the
vehicle is identified, and the toll fees are automatically taken
from the vehicle's account. This relatively new RFID
technology might be used for security reasons to identify
authorized vehicles, which would be helpful. Sensors are the
significant data source in intelligent transportation systems
(ITS); by deploying sensors on the road, transportation data
such as vehicle speed and position can be gathered and

evaluated. Intelligent transportation systems (ITS) are gaining
popularity. Sensors such as global positioning system-based
sensors, magnetometers, and gyroscope-based sensors, among
others, are used to collect transit data [6]. We may be able to
gather additional data about arterials and vehicle access to
highways using sensors, which may then be stored and utilized
for a variety of applications, including incident detection, active
transportation, and highway demand management (Sharma &
Kaushik, 2019). Sensor technology is used in multiple
applications, including adaptive signal control, ramp and
highway metering, and dispatching emergency response
providers. It is feasible to get reliable and fast traffic flow
information by combining sensors with big data platforms.
Aside from that, further analysis may be conducted utilizing a
range of data sources, such as identifying the owner of a car,
obtaining vehicle information, and extracting vehicle owner
details at a particular time, to name a few alternatives.
Big Data in ITS
Big Data in Intelligent Transportation Systems
There are large amounts of data sent from multiple data sources
to intelligent Transportation Systems (ITS). Some of the data
sources include GPS, video, sensor signals, social media and so
on.
Big Data from Smart Cards
In urban and modern public transport, Automatic Fare
Collection (AFC) systems are extensively used to explore the
passenger movement patterns using smart cards data which is
one of the main data sources. Passengers who wish to use buses,
trains or ferries for public transportation utilize smart cards and
the electronic readers which scan these cards collect passenger
data such as origination-destination (OD), boarding times,
transfers etc, (Zhu, et al., 2019). In the US, many transit
authorities use smart cards in cities like San Francisco Bay Area
Rapid Transit (BART) (Buneman, K., 1984)., Washington
Metropolitan Area Transit Authority (WMATA) (Miller, L. S.,

1994) and Philadelphia’s Port Authority Transit Corporation
(PATCO) Lindenwold Line NX-zonal AFC systems (Vigrass, J.
W., 1990) which in turn generate huge amounts of data. Because
smart cards are extensively, its usage data collected is a
important element for public transportation management and
planning (Zhu, et al., 2019) and most researchers agree that this
data is used by ITS for passenger travel behavior, travel time
estimation to destination, travel patterns, frequency of travel
etc. (Nishiuchi, et al., 2013).
For instance, Transportation for London (TfL) collects smart
card data from 8 million trips every day at London metro
stations.
Big Data from GPS
Global positioning System (GPS) is the most important tool
used today by users for location positioning and navigation. On
a busy commuting day, traffic data, vehicle position, vehicle
speed, vehicle density, vehicle type etc. can be collected
efficiently and precisely via GPS. Travel mode detection (Gong,
et al.,2012; Wang, et al.,2016), travel delay measurement
(Asensio, et al., 2009) and Traffic monitoring (Herrera, et al.,
2010) are some of the many traffic issues that could be
addressed from the data collected GPS and other map displaying
technologies.
Big Data from Sensors
Sensor devices connected to ITS is mainly used collect vehicle
and traffic data such as vehicle speeds, traffic flows, vehicle
density, vehicle travel time and vehicle position (Zhu, et al.,
2019).
Standard on-road sensor devices have been constantly evolving
to collect, process and transfer traffic data (Lopes, et al., 2010).
And the data collected from these sensors are mainly split into
three types: floating car data, roadside data and wide area data
(Antoniou, et al., 2008).
Roadside data is referred as data collected from sensory devices

installed alongside a main road or freeway. With evolving
technologies, sensors hardware and software has changed, and
use infrared systems, ultrasonic and acoustic sensor systems,
magnetometer vehicle detectors, light detection and ranging
(LIDAR) etc. (Zhu, et al., 2019). In the US, Colorado
department of transport (CDOT) have installed new sensors on
I-25 (Interstate-25) that can detect ice, water and temperature to
provide the most up-to-the-minute information for road crews
and feeds this data to ITS to provide road safety for the public
(colorado reference).
Floating car data (FCD) is referred as data collected on a
vehicle while it’s in motion at different locations in ITS. They
are used to collect time stamped GPS data and vehicle speeds
while the automobile is in motion, and this data is used to
provide. Along with the road side sensors, the vehicle’s
embedded GPS receiver or cellular phone also acts as a moving
sensor (Huang, E. 2010).
Wide area data refers to the wide area traffic flow data that is
collected by diverse sensor tracking techniques such as
photogrammetric processing, sound recording, video processing,
and space-based radar.
Big Data from Connected and Autonomous Vehicles (CAVs)
Connected and automated vehicles (CAVs) (a.k.a. driver-less
cars) are a transformative technology that has significant
prospects for reducing traffic accidents, enhancing the quality
of life, and improving the efficiency of transportation systems.
CAVs are built with a wide range to technologies in ITS
keeping in mind the safe efficient movement of people and
goods. May automobile industries today like Tesla, Ford etc.
generate large amounts of real-time transportation data such as
location, speed, acceleration, safety data (Uhlemann, E. 2015)
and this data is used to mitigate traffic
CAV enabled traffic system has demonstrated great potential to
mitigate congestion, reduce travel delay, and enhance safety
performance [33], [34]. Using latest network technologies such

as Software Defined Networking, data can be obtained more
efficiently [35] These data can be used to create actionable
information to support and facilitate green transportation
choices, and apply to the real-time adaptive signal
control [36], [37].
Big Data from Vehicle Adhoc Network (VANET)
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Big Data from Other Sources
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Big Data from Videos
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Gaps in Big Data for Intelligent Transportation Systems
It is vital to detect possible threat actors while dealing with
Intelligent Transportation Systems. Potential ITS network
attackers have been highlighted by foreign intelligence services,
criminal gangs, hacktivists, cyberterrorists, insiders, unethical
operators, and natural disasters. National governments collect
information via software espionage tools and viruses that are
purpose-built for their goals. Intellectual property theft or
getting a competitive edge are the primary goals of this kind of
attack. During times of war, for example, a nation's information
and communications technology (IT) infrastructure may be
attacked by a cyberattack. Government-controlled hacking
teams and resources may be outsourced to third-party firms
(Sharma & Kaushik, 2019). Criminal gangs acquire access to
information technology networks and produce illegal income in
several methods. Hacktivists utilize information and
communication technology (IT) infrastructure to draw a
political cause. Previously, roadside message boards have been
hacked to propagate a political agenda. Cyberterrorists attack
information technology systems with the intent of causing
property damage, human casualties, and widespread panic.
Insiders conduct killings inside organizations in which they now

or formerly held positions, with the intent of furthering the
insider's own goals. Numerous variables contribute to the
beginning and endurance of these attacks (Sharma & Kaushik,
2019). Unscrupulous operators may target the ITS system to
avoid paying fines and taxes, avoid traffic, or eliminate
competitors, among other things. On the other side, natural
disasters may jeopardize the ITS system. Natural catastrophes
can trigger system failures, compromising the infrastructure of
the Intelligent Transportation System (ITS). The majority, if not
all, of cyberattacks, is motivated by a desire for financial gain.
The distinction between information and transportation systems
is evident, which has a considerable impact when attacked or
hacked. This kind of exposure may be a potent motivator in
some circumstances. The reasons for these persons have been
variously described as ransom, data theft, information warfare,
system gaming, robbery, revenge, and terrorism, to mention a
few. Physical, wireless, or network attacks, among others, may
be employed to get the data. A seizure may be launched using a
single vector or a collection of vectors. When attackers launch a
ransom attack, they encrypt the data and systems being targeted.
Until the victim obtains the decryption keys, the ransom is not
paid. An attacker might get access to a connected car and
disable it until the attackers are paid a ransom in bitcoin (Hîrţan
et al., 2020). It is feasible that the safety of these cars has been
risked. Stolen information may be utilized for a variety of
reasons. When it comes to stealing information from a business,
the most typical attackers are national governments and
unscrupulous competitors. The objective of data theft is to
benefit directly from the information stolen.
The term "information warfare" refers to assaults on the
information technology infrastructure (ITS) that result in a
denial of service (DoS) condition. Consequently, the systems
malfunction, resulting in more significant road congestion.
Other types of content on the website include political
commentary, demonstrations, and practical jokes. This might
have a detrimental effect on the company's reputation and result

in financial loss. If illicit vehicle-to-vehicle (V2V) broadcasts
to the general public, they can wreak havoc on the
transportation system. This kind of attack can contaminate V2V
networks with data.
Additionally, map hacking tools may be used to compromise
location transmitters, GPS receivers, and GPS signal spoofing.
If you engage in system gaming or vehicle theft, you may steal
items from both inside and outside automobiles. It is also
feasible to avoid paying fees and service charges by using ITS
systems in certain instances. Self-driving cars may be hacked
and directed to a remote location, or they can be used to convey
contraband discreetly. Self-driving vehicles may be hacked and
sent to a remote location, where valuables, car components, the
whole vehicle, or abduction are all possibilities. If you utilize
an ITS system, you may avoid paying service costs. It is
possible to remotely activate a traffic light controlled by a
computer using Mobile Infrared Transmitters (MIRT). This
permits the manipulation of light. There is a definite possibility
of hacking into a competitor's automobile to sabotage
competition and render the cars unusable. It is conceivable to
engineer a situation where autonomous vehicles are compelled
to make way for a high-priority hacking vehicle. False
ridesharing requests may be made to bill unknowing clients for
services they never requested.
While big data technologies have done an incredible job
managing 4V data, traditional business intelligence systems
have done a better job maintaining Metadata associated with
inputted data. This is because conventional methods must deal
with highly structured data, but Big Data Systems do not.
Developing a standard semantic layer for all retained data
continues to be a challenge, and the more different the data, the
more similar the semantic layer should be to maximize data
retrieval efficiency. The transportation industry has significant
challenges due to the breadth of accessible data, including
photos, structured data, and streaming data from sensors.
Unlike traditional business intelligence systems, big data

systems face this challenge in the same way as conventional
business intelligence systems do because big data is derived
from various sources that are highly likely to contain personal
information, such as Twitter and Facebook text data. Because
big data is dispersed over a distributed architecture, enforcing
security policies becomes a much more challenging task, one
that has yet to be entirely resolved to the satisfaction of
industry standards. Due to the volume of data generated by
vehicle monitoring, it is well-suited for transportation systems.
One of the most challenging difficulties confronting the
industry today is deciding which big data architectural
framework best suits the use case. This is especially tough since
big data frameworks are still in their infancy. This issue
impacts a broad spectrum of businesses, not only transportation
networks.
Cyber security
Phishing
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Eves dropping
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Cyber terrorism
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Vehicle communication security breach (VANET)
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Data Breach in industries and Examples
Automobile Industry
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Manufacturing
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Telecommunication
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Summary
Transportation evolution is followed by new threats to ITS
systems concurrently with ecosystem change. Significant
changes are likely in several sectors, and the extent of these
changes is relatively wide. Accepting the need for a more
targeted approach to security, focused on preventing and
resolving attacks, is required. As previously noted, the current
models used by ITS ecosystems have several shortcomings that
must be addressed before the ecosystems can function correctly.
The following section discusses these restrictions in further
detail. Additional research is required in this area to analyze
specific solutions tailored to the unique requirements of each of
the several ITS systems and applications now available. More
study on cooperative systems is required to identify cyber risks
and develop responses to protect against them. This is why
biometric technology is essential to the long-term health of the
transportation industry's cybersecurity.
Consequently, they provide actionable data that supports critical
decision-making in various circumstances, including route
selection, trip scheduling, and whether passengers should drive
or use public transit. This article aims to provide a
comprehensive understanding of big data analytics and the
Internet of Things (IoT) in the transportation business and
identify the factors that may affect the development of an
intelligent transportation system. Apart from that, the plan will
be adequately outlined. Finally, and perhaps most importantly,
the research's objective is to understand better the effects of big
data on cyber security vulnerabilities in Intelligent
transportation system. When we complete our analysis, which
will be addressed in the next part, we will develop an integrated
implementation architecture for big data analytics and Internet

of Things-based transportation systems.
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Work in Progress
The researchers say that the paradigm of vehicular
communication has shifted dramatically over the previous two
decades, and much has been learned and relearned in the
process. The idea of vehicle-to-infrastructure (V2I)
communication has developed into more modern concepts such
as vehicle-to-vehicle (V2V) communication and vehicle-to-
pedestrian (V2P) communication, setting the framework for the
future idea of vehicle-to-everything (V2X) communication.
Because of their vast communication range and very rapid data
rates, cellular networks have traditionally been utilized for
vehicle-to-vehicle communication (Muthuramalingam et al.,
2020). Although they are an unsatisfactory medium of
communication in highly dynamic networks because of their
high cost and low reliability in meeting strict delay
requirements, they offer certain benefits over other ways of
communication. Engineers and scientists alike are interested in
the development of dedicated short-range communication
(DSRC), a wireless communication system capable of sending
and receiving information over short to medium distances for
safety-based vehicle-to-vehicle (V2V) applications. It has
grown in prominence due to its ability to transmit and receive
real-time information between automobiles. While several
obstacles continue to obstruct the effective deployment of
DSRC-based vehicular networks, the US Department of

Transportation (US DoT) is actively pursuing it as one of its
major research priorities for a variety of public safety and
traffic management applications, including but not limited to
forward-collision warnings, blind intersection collision
mitigation, (approaching) emergency vehicle warnings, lane
change assistance, and a variety of other applications
(Muthuramalingam et al., 2020).
Many alternative radio access technologies (RATs) are being
explored for possible in-vehicle communication systems as an
alternative to cellular networks and direct sequence radio
communication (DSRC). The number of sensors put in
automobiles is expected to increase by orders of magnitude in
the following years, particularly as connected and autonomous
vehicles become more common. Because of this necessity, a
wireless communication system capable of transferring large
amounts of data at high data speeds must be built (Ganin et al.,
2021). All of the above considerations are especially pertinent
to network and autonomous cars. The ITS community has
gained momentum in investigating the feasibility of millimeter-
wave communication (mmWave) for vehicular networking
applications since vehicle data transmission ranges can reach up
to 1 km, and reasonable data rates can range from 2 to 6
megabits per second.
In contrast, data rates for cellular networks can only reach 100
megabits per second in high-mobility scenarios. Although
mmWave is designed to produce gigabit-per-second data speeds,
channel modeling effectiveness, security, and beam alignment
have proven significant roadblocks to its broad adoption and
deployment. Higher-frequency bands in the frequency spectrum
and terahertz communication for car networks and other uses
are generating greater attention than ever before. Thus, in the
not-too-distant future, the development and deployment of fifth-
generation (5G) and beyond fifth-generation (beyond 5G)
wireless-networking technologies will be made simpler (Tokody
et al., 2018).
The truth is that no one technology can fully address the

demands of both vehicle safety and non-safety applications
simultaneously, especially when their requirements are in direct
conflict. As a result, achieving synergy between different RATs
is critical to building an effective heterogeneous vehicular
networking platform capable of satisfying stringent
communication requirements (Kaffash et al., 2021). Although
heterogeneity is an essential and timely subject, it falls beyond
the scope of this inquiry. With the large volume of data created
by a vehicle network, the network's security and stability are
substantially jeopardized due to the network's vast number of
heterogeneous data sources (Tokody et al., 2018). This chapter
varies from previous regularly published surveys and chapters
in that it focuses primarily on the secure capturing of Big Data
in-vehicle networks rather than other difficulties.
The research by Kaffash et al. (2021) argues that the ability to
adopt an evidence-based approach to decision-making in several
settings is a benefit of studying Big Data and analytics in a
variety of professions. Big data can improve transportation
networks' overall safety and long-term profitability, particularly
in the transportation industry. Many cities have installed traffic
monitoring equipment such as cameras, roadside sensors, and
wireless sensor networks to keep track of traffic conditions and
enhance traffic flow to keep up with traffic. This technology
collects a vast amount of traffic data, allowing transportation
companies to understand traffic flow in their specific areas
better. It can now undertake historical and real-time data
analysis with new traffic data (Kaffash et al., 2021).
Consequently, traffic patterns may be disclosed, congestion can
be discovered, and accidents and near misses can be adequately
probed. Big Data analytics and methodologies, like machine
learning, may be used to filter through large volumes of traffic
data to extract relevant information that can then be utilized by
the transportation authority to take preventative steps and make
suitable judgments. Machine learning is one way that may be
used to solve this challenge.

Evaluating the data acquired makes it possible to find hidden
values in traffic data that may subsequently be utilized to
design and promote safe and sustainable transportation
networks. For example, data on vehicle speed may be collected
and analyzed using roadside sensors to identify traffic
congestion (Gaber et al., 2019). When traffic congestion is
detected, drivers may be given travel alerts to aid them in
selecting other routes and, consequently, reduce traffic
congestion. In addition to providing valuable data, research on
vehicle wait times at traffic signals may lead to the
development of creative ways for, among other things,
optimizing traffic light rules and increasing traffic flow (Cheng
et al., 2018). It can classify and categorize objects and follow
their motions using video data. It can identify and highlight
major traffic mishaps such as swerving, abrupt braking, and
near misses. Video data analysis is becoming more popular
among businesses. In the future, research like this may aid
decision-makers in making the necessary changes to improve
road safety, prevent accidents, and save lives when driving on
roads (Cheng et al., 2018).
It is indisputable that traffic data epitomizes the characteristics
of Big Data, which are often characterized in terms of volume,
variety, Velocity, honesty, and monetary value. To begin with,
the large number of pieces of equipment put on roadways to
monitor traffic generates a massive amount of data. The volume
of traffic data will skyrocket as connected cars interact and
exchange information with one another, other vehicles on the
road, road infrastructure, and other devices. According to the
National Highway Traffic Safety Administration, this is the case
(Guerrero-Ibáñez et al., 2018). Automobiles are present in the
local neighborhood. According to forecasts, linked cars will
create roughly 30 gigabytes of data every day. If the current
rate of increase continues, the amount of traffic data is expected
to exceed one terabyte in approximately one month. Second,
traffic data may be collected in a variety of forms, including
JPEG, JSON, XML (GPS), PDF (pictures, videos, and social

media posts), and other structured and unstructured data sources
(Guerrero-Ibáñez et al., 2018).
Furthermore, the speed of updating traffic data is impressive,
given the many sources that consistently supply current traffic
data. The fourth aspect of data dependability is concerned with
the inherent uncertainties in traffic data, such as erroneous or
missing information, which have previously been examined in
more depth. Last but not least, although traffic data is valuable,
it is only available in limited amounts. You may be able to
figure out what caused an accident at an intersection by looking
at the camera video from the scene. Because accidents do not
occur regularly, the vast bulk of the data consists primarily of
observations of routine vehicle traffic in the surrounding area.
Achieving real-time data processing and dependable
communication networks in the transportation industry is
crucial since some technologies, such as driverless automobiles,
cannot operate appropriately unless linked (Guerrero-Ibáñez et
al., 2018). Edge computing, which allows data processing and
computation to occur close to data sources, may be
advantageous to Big Data and analytics because of the vast data
generated today. The amount of bandwidth used and network
latency encountered between end-users and cloud computing
platforms that store, manage, and analyze data and the cost of
data storage and management are reduced. Edge computing may
show to be a viable solution for dealing with the issues that
have evolved because of exponential data expansion, limited
connection bandwidth, and vast quantities of processing power
accessible in the cloud soon.
Big Data
Solution

s in ITS
It is challenging to conserve the complex ITS ecosystem in its
entirety adequately. While cyberattacks and data breaches are
unavoidable, IT administrators should include preventative and
recovery measures into their day-to-day operations. Data
transmission should be possible within the time limitations
given by the organization (Kim, 2018). This should be done by
using simple cryptographic techniques with little overhead. Two
of the most critical security features in information technology
(IT) systems are confidentiality and authentication. Continuous
monitoring and response are required in any kind of data
security issue. Security flaws must be maintained to a bare
minimum, and sensitive data must never be lost. It is critical to
fix any security concerns to keep the system secure from
intruders. Following an attack on the ITS environment, it is
crucial to fortify defenses to fend off future attacks. These
recommendations advocate for network segmentation, firewalls
(including next-generation firewalls), and unified threat
management (UTM) gateways to satisfy the security issues and
needs stated in the previous sections.
Encryption technology, anti-malware, anti-phishing, and breach
detection systems are further alternatives (BDS). Other security
solutions, including Shodan scanning, vulnerability scanning,
and patch management, are available today in addition to
intrusion prevention and detection systems (Kim, 2018).).

Segmenting a network, which separates it into subnetworks,
helps relieve traffic congestion while boosting security and
reducing the probability of failure. When ITS controllers are
located on a network distinct from corporate networks, lateral
movement becomes less risky, and overall security improves.
Firewalls safeguard networks because they enable
administrators to manage outgoing and incoming traffic. A
popular technique for achieving this control is to apply a rule
set to the monitor. The security system detects and quarantines
apps and endpoints that generate or request harmful traffic.
Systems and services that merge several methods and services
include a single-engine or appliance, next-generation firewalls,
and Unified Threat Management gateways. Devices with low
traffic must be evaluated by comparing their network traffic at
line speed to comparable devices with higher traffic. A virus
scanner is a piece of software that, among other things, checks
files for the existence of viruses and other infections. Malware
may be detected, stopped, and removed from the computer.
Heuristics-specific and generic signatures are used in
combination to identify known and undiscovered malware.
Anti-phishing software is critical in combating stealth phishing,
one of the most common attack types. Anti-phishing systems
scan incoming emails for spam and phishing messages and take
appropriate action to prevent them from being delivered.
Malicious attachments, in addition to message sandboxes

employed in anti-phishing systems, pose a risk and should be
authenticated. Breach detection systems (BDS) identify a
targeted attack and threaten to steal data from the targeted
system if the assault is not quickly neutralized. BDS can
investigate and diagnose complicated assaults, but it is
incapable of preventing them. A variety of protocols may be
used to analyze network traffic patterns. Domains that may
contain malicious code may be identified. Emulation of
sandboxing is a method for simulating the behavior and
consequences of harmful files on the host computer (Arena et
al., 2020). Intrusion prevention systems (IPS) and intrusion
detection systems (IDS) constantly scan the whole network for
unusual activity. They also do extensive pocket inspections and
file paperwork as part of their duties. When an intrusion
detection system (IDS), a passive system, detects an assault, it
generates a report.
The firewall refuses a packet when an intrusion detection
system (IPS) detects a potentially harmful occurrence. Using
digital signature technologies, it is possible to neutralize the
bulk of assaults on ITS applications and systems. Non-digital
signature-based attacks may be mitigated via encryption.
Encryption and decryption techniques may be used to encrypt
and decrypt data. It is feasible to guard against and avoid Man-
in-the-Middle (MitM) attacks when encrypted network
communication. Patch management software, both physical and

virtual, may be used to update endpoints, servers, and remote
devices. Patch management software is also used to automate
the application of security patches and updates. Security
enforcement layers must be developed to prevent malicious
traffic from gaining access to the network. These layers must
filter out traffic that attempts to exploit known security flaws.
Endpoints, servers, networks, and apps may be scanned for
vulnerabilities using a vulnerability scanner. Unpatched
vulnerabilities may be discovered and made public. When an IT
administrator finds a vulnerability, they may mitigate the risk.
The Shodan algorithm is used to find internet-connected
devices. This technology gathers Open-Source Intelligence from
several sources (OSINT). Shodan's data might be used to
uncover unpatched vulnerabilities in publicly available cyber
assets. Owners and operators of information and communication
technology (ICT) systems may utilize Shodan to verify that
their devices and systems are not connected to the Internet.
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302289#:~:text=Connected%20and%20automated%20vehicles%
20(CAVs,the%20efficiency%20of%20transportation%20systems

.Title: Recent advances in connected and automated vehicles

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