Need help in writing a literature review for below table of contents in APA 7 without any plagiarism. 70 pages with 75 references for Research Topic: Examine the Effects of Big Data on Cyber Security Vulnerabilities in Intelligent Transportation Systems. I have also attached how I started and need help with the attached table of contents.
The theoretical framework used is a Grounded theory which I have added below.
This dissertation investigates the problem “Big Data increases cyber security vulnerabilities in Intelligent Transportation systems due to cyber-attacks” (Aldhaheri, et al., 2020; Bubenikova, et al.,2014; Coppola and Silvestri, 2019; Cui, et al., 2018; Harvey and Kumar, 2020; Liang, et al., 2019; Mahmood, et al., 2018; Petit, 2015; Qureshi and Abdullah, 2013).
Many Researchers utilize theoretical frameworks to illustrate the research problem and provide a practical approach for the study. Huang, S. E. used Empirical Study to investigate cyber security concerns in connected vehicles – Traffic control systems (CV-TCS) and built a cyber security framework for CV-TCS systems (Huang, S. E. 2020). And Thompson, E. E. utilized a qualitative research method using Grounded Theory to understand the end-users of enterprise information technology systems and their practices to maintain health world facts and analyses data with no predetermined ideas or hypotheses (Glaser & Strauss, 1967).
American sociologists Glaser and Strauss developed the grounded theory methodology in 1967 to describe a new qualitative research method. This study adopted an investigative research method with no preconceived hypothesis and used a continuous relative data analysis. Glaser and Strauss believe that the Theory obtained by this method is genuinely grounded in the data. Hence the reason for calling this methodology “grounded theory” (Glaser & Strauss, 1967). This study will begin with the general concept of how recent advancements in big data technologies could address major cyber threats to Intelligent Transportation Systems. Interview transcripts from participants will be collected and analyzed to extract common themes. (Moustafa et al., 2018). Using Grounded Theory, we can determine whether cyber security concerns are present in Intelligent transportation systems and help research the vulnerability of ITS due to cyberattacks.
PEER REVIEWED SUMMARY
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 ~
iv. ITS – Intelligent Transportation Systems
a. Introduction to ITS
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
b. Eves dropping
c. Cyber terrorism
d. Vehicle communication security breach (VANET)
e. Data Breach in industries and Examples
vii. Theoretical framework
viii. Review of Methodological Literature
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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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
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 management, 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.
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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 Transportation system
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Commercial Vehicles Control System
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ITS Cyber Security
Definition and Importance
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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 identification (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 . 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 , . Using latest network technologies such as Software Defined Networking, data can be obtained more efficiently  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 , .
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