60 pages Literature review on Examine the Effects of Big Data on Cyber Security Vulnerabilities in Intelligent Transportation Systems

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CHAPTER 2

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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 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.

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 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 [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 Solutions 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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DavidElliottaWalterKeenbLeiMiaob

https://www.sciencedirect.com/science/article/pii/S2095756418302289#:~:text=Connected%20and%20automated%20vehicles%20(CAVs,the%20efficiency%20of%20transportation%20systems.

Title: Recent advances in connected and automated vehicles